Evidence Report/Technology Assessment 

Number 145 

Genomic Tests for Ovarian Cancer Detection 

and Management

Prepared for: 

Agency for Healthcare Research and Quality 

U.S. Department of Health and Human Services 

540 Gaither Road 

Rockville, MD 20850 

www.ahrq.gov 

Contract No. 290-02-0025 

Prepared by: 

Duke University Evidence-based Practice Center, Durham, NC 

Investigators

Evan R. Myers, M.D., M.P.H. (Principal Investigator) 

Laura J. Havrilesky, M.D. 

Shalini L. Kulasingam, M.P.H., Ph.D. 

Gillian D. Sanders, Ph.D. 

Kathryn E. Cline, M.H.S. (Project Manager) 

Rebecca N. Gray, D.Phil. (Editor) 

Andrew Berchuck, M.D. 

Douglas C. McCrory, M.D., M.H.S. 

AHRQ Publication No. 07-E001 

October 2006
This report is based on research conducted by the Duke University Evidence-based Practice 

Center (EPC) under contract to the Agency for Healthcare Research and Quality (AHRQ), 

Rockville, MD (Contract No. 290-02-0025). The findings and conclusions in this document are 

those of the author(s), who are responsible for its contents, and do not necessarily represent the 

views of AHRQ. No statement in this report should be construed as an official position of AHRQ 

or of the U.S. Department of Health and Human Services. 

The information in this report is intended to help clinicians, employers, policymakers, and others 

make informed decisions about the provision of health care services. This report is intended as a 

reference and not as a substitute for clinical judgment. 

This report may be used, in whole or in part, as the basis for development of clinical practice 

guidelines and other quality enhancement tools, or as a basis for reimbursement and coverage 

policies. AHRQ or U.S. Department of Health and Human Services endorsement of such 

derivative products may not be stated or implied. 
ii

This document is in the public domain and may be used and reprinted without permission except 

those copyrighted materials noted for which further reproduction is prohibited without the 

specific permission of copyright holders. 

Suggested Citation: 

Myers ER, Havrilesky LJ, Kulasingam SL, Sanders GD, Cline KE, Gray RN, Berchuck A, 

McCrory DC. Genomic Tests for Ovarian Cancer Detection and Management. Evidence 

Report/Technology Assessment No. 145. (Prepared by the Duke University Evidence-based 

Practice Center under Contract No. 290-02-0025.) AHRQ Publication No. 07-E001. Rockville, 

MD: Agency for Healthcare Research and Quality. October 2006.

None of the investigators has any affiliations or financial involvement that conflicts 

with the material presented in this report. 
iii

Preface 

 The Agency for Healthcare Research and Quality (AHRQ), through its Evidence-Based 

Practice Centers (EPCs), sponsors the development of evidence reports and technology 

assessments to assist public- and private-sector organizations in their efforts to improve the 

quality of health care in the United States. The Centers for Disease Control and Prevention 

(CDC) requested and provided funding for this report. The reports and assessments provide 

organizations with comprehensive, science-based information on common, costly medical 

conditions and new health care technologies. The EPCs systematically review the relevant 

scientific literature on topics assigned to them by AHRQ and conduct additional analyses when 

appropriate prior to developing their reports and assessments. 

 To bring the broadest range of experts into the development of evidence reports and health 

technology assessments, AHRQ encourages the EPCs to form partnerships and enter into 

collaborations with other medical and research organizations. The EPCs work with these partner 

organizations to ensure that the evidence reports and technology assessments they produce will 

become building blocks for health care quality improvement projects throughout the Nation. The 

reports undergo peer review prior to their release. 

 AHRQ expects that the EPC evidence reports and technology assessments will inform 

individual health plans, providers, and purchasers as well as the health care system as a whole by 

providing important information to help improve health care quality. 

 We welcome comments on this evidence report. They may be sent by mail to the Task Order 

Officer named below at: Agency for Healthcare Research and Quality, 540 Gaither Road, 

Rockville, MD 20850, or by e-mail to epc@ahrq.gov. 

Carolyn M. Clancy, M.D. 

Director 

Agency for Healthcare Research and Quality 

Julie Louise Gerberding, M.D., M.P.H. 

Director 

Centers for Disease Control and Prevention 

Jean Slutsky, P.A., M.S.P.H. 

Director, Center for Outcomes and Evidence 

Agency for Healthcare Research and Quality 

Beth A. Collins Sharp, Ph.D., R.N. 

Director, EPC Program 

Agency for Healthcare Research and Quality 

Gurvaneet Randhawa, M.D., M.P.H. 

EPC Program Task Order Officer 

Agency for Healthcare Research and Quality 
iv

Acknowledgments 

The authors gratefully acknowledge R. Julian Irvine for assistance with project management 

and copy-editing, Udita Patel for assistance with project management, and Dr. Gurvaneet 

Randhawa, AHRQ Task Order Officer, for overall assistance. 

The authors also acknowledge the contributions of Dr. Ralph Coates of the Centers for 

Disease Control and Prevention’s (CDC) Division of Cancer Prevention and Control and 

Dr. Linda Bradley in the CDC’s Office of Genomics.
v

Structured Abstract

Objective: To assess the evidence that the use of genomic tests for ovarian cancer screening, 

diagnosis, and treatment leads to improved outcomes. 

Data Sources: MEDLINE® and reference lists of recent reviews. 

Review Methods: We evaluated tests for: (a) single gene products; (b) genetic variations 

affecting risk of ovarian cancer; (c) gene expression; and (d) proteomics. For tests covered in 

recent evidence reports (cancer antigen 125 [CA-125] and breast cancer genes 1 and 2 

[BRCA1/2]), we added studies published subsequent to the reports. We sought evidence on: 

(a) the analytic performance of tests in clinical laboratories; (b) the sensitivity and specificity of 

tests in different patient populations; (c) the clinical impact of testing in asymptomatic women, 

women with suspected ovarian cancer, and women with diagnosed ovarian cancer; (d) the harms 

of genomic testing; and (e) the impact of direct-to-consumer and direct-to-physician advertising 

on appropriate use of tests. We also constructed a computer simulation model to test the impact 

of different assumptions about ovarian cancer natural history on the relative effectiveness of 

different strategies. 

Results: There are reasonable data on the clinical laboratory performance of most 

radioimmunoassays, but the majority of the data on other genomic tests comes from research 

laboratories. Genomic test sensitivity/specificity estimates are limited by small sample sizes, 

spectrum bias, and unrealistically large prevalences of ovarian cancer; in particular, estimates of 

positive predictive values derived from most of the studies are substantially higher than would be 

expected in most screening or diagnostic settings. We found no evidence relevant to the question 

of the impact of genomic tests on health outcomes in asymptomatic women. Although there is a 

relatively large literature on the association of test results and various clinical outcomes, the 

clinical utility of changing management based on these results has not been evaluated. We found 

no evidence that genomic tests for ovarian cancer have unique harms beyond those common to 

other tests for genetic susceptibility or other tests used in screening, diagnosis, and management 

of ovarian cancer. Studies of a direct-to-consumer campaign for BRCA1/2 testing suggest 

increased utilization, but the effect on “appropriateness” was unclear. Model simulations suggest 

that annual screening, even with a highly sensitive test, will not reduce ovarian cancer mortality 

by more than 50 percent; frequent screening has a very low positive predictive value, even with a 

highly specific test. 

Conclusions: Although research remains promising, adaptation of genomic tests into clinical 

practice must await appropriately designed and powered studies in relevant clinical settings. 
 
vii

Contents

Executive Summary............................................................................................................... 1 

Evidence Report ..................................................................................................................... 7

Chapter 1. Introduction ......................................................................................................... 9 

 Ovarian Cancer ................................................................................................................ 9 

 Approaches to Reducing Ovarian Cancer Morbidity and Mortality................................ 11 

 Primary Prevention in the Entire Population ............................................................. 11 

 Primary Prevention in Women at Increased Risk for Developing Ovarian Cancer .. 11 

 Secondary Prevention through Screening.................................................................. 12 

 Secondary Prevention through Screening in Women at High Risk........................... 12 

 Improved Therapy after Diagnosis of Ovarian Cancer.............................................. 12 

 Genomic Tests ................................................................................................................. 12 

 Tests Measuring Single Gene Products ..................................................................... 13 

 Tests for Variations in DNA...................................................................................... 13 

 Gene Expression ........................................................................................................ 13 

 Proteomics/Protein Characterization ......................................................................... 13 

 Interest in Genomic Tests for Ovarian Cancer ................................................................ 14 

 Use of Genomic Tests in Prevention of Ovarian Cancer Morbidity and Mortality......... 14 

 

Chapter 2. Methods............................................................................................................... 17 

 Topic Assessment and Refinement.................................................................................. 17 

 Analytic Framework ........................................................................................................ 18

 Literature Search and Review.......................................................................................... 19 

 Sources....................................................................................................................... 19 

 Search Strategies........................................................................................................ 19

 Abstract and Full-text Screening ............................................................................... 19 

 Screening Criteria ...................................................................................................... 20 

 Data Abstraction and Development of Evidence Tables ................................................. 23 

 Quality Assessment Criteria ............................................................................................ 23 

 Additional Analyses......................................................................................................... 24 

 Test Characteristics and Confidence Intervals........................................................... 24 

 Ovarian Cancer Model............................................................................................... 25 

 Peer Review Process........................................................................................................ 29 

 

Chapter 3. Results ................................................................................................................. 31 

 Question 1: Analytic Validity of Testing........................................................................ 31 

 Approach.................................................................................................................... 31 

 Results........................................................................................................................ 31 

 Discussion.................................................................................................................. 35 

 Summary.................................................................................................................... 36 

 Question 2: Sensitivity and Specificity of Tests............................................................. 36 

 Approach.................................................................................................................... 36 
viii

 Other Evidence Reports ............................................................................................. 36 

 Results........................................................................................................................ 37 

 Discussion.................................................................................................................. 44 

 Question 3: Impact on Clinical Management of Asymptomatic Patients....................... 44 

 Approach.................................................................................................................... 45 

 Other Evidence Reports ............................................................................................. 45 

 Results........................................................................................................................ 45 

 Discussion.................................................................................................................. 45 

 Summary.................................................................................................................... 46 

 Question 4: Impact on Clinical Management of Diagnosed Patients ............................. 46 

 Approach.................................................................................................................... 46 

 Results........................................................................................................................ 46 

 Discussion.................................................................................................................. 56 

 Summary.................................................................................................................... 57 

 Question 5: Harms of Using Genomic Tests .................................................................. 57 

 Approach.................................................................................................................... 57 

 Other Evidence Reports ............................................................................................. 58 

 Results........................................................................................................................ 58 

 Discussion.................................................................................................................. 59 

 Summary.................................................................................................................... 60 

 Question 6: Direct-to-Consumer and Direct-to-Physician Marketing............................ 60 

 Approach.................................................................................................................... 60 

 Results........................................................................................................................ 61 

 Discussion.................................................................................................................. 62 

 Summary.................................................................................................................... 64 

 Modeling Results ............................................................................................................. 64 

 Model Validation ....................................................................................................... 64 

 Impact of Different Strategies.................................................................................... 68 

 Summary.................................................................................................................... 73 

Chapter 4. Discussion ........................................................................................................... 75 

 Limitations of the Report................................................................................................. 75

 Methodological Issues in the Literature........................................................................... 75 

 Description of the Patient Population ........................................................................ 75 

 Sample Size................................................................................................................ 76 

 Prevalence of Ovarian Cancer in Samples................................................................. 76 

 Stage Distribution within Samples............................................................................. 76 

 Biological and Observer Variability in Test Results ................................................. 77 

 Use of Tests for Decisionmaking in Management..................................................... 77 

 Selection of Cases and Controls in Initial Test Development ................................... 77 

 Natural History of Ovarian Cancer............................................................................ 78 

 Implications of Findings .................................................................................................. 78

 Question 1 (Analytic Validity)................................................................................... 78 

 Question 2 (Sensitivity and Specificity) .................................................................... 79 

 Question 3 (Clinical Management of Asymptomatic Women) ................................. 79 
ix

 Question 4 (Clinical Management of Diagnosed Women)........................................ 79 

 Question 5 (Potential Harms)..................................................................................... 79 

 Question 6 (Direct Marketing)................................................................................... 79 

 Modeling.................................................................................................................... 80 

Chapter 5. Future Research................................................................................................... 81 

 Minimal Data Reporting .................................................................................................. 81 

 Test Reproducibility......................................................................................................... 81 

 Biological Variability....................................................................................................... 82 

 Test-Negative Subjects .................................................................................................... 82

 Evaluation of Tests .......................................................................................................... 82 

 Natural History of Ovarian Cancer.................................................................................. 83 

Chapter 6. Conclusions ......................................................................................................... 85 

References and Included Studies ........................................................................................... 87 

Acronyms and Abbreviations ................................................................................................ 99 

Figures 

Figure 1. U.S. ovarian cancer incidence by age, 1992-2003............................................... 9 

Figure 2. Analytic framework for evidence report.............................................................. 18 

Figure 3. Schematic representation of Markov model of ovarian cancer ........................... 27 

Figure 4. Schematic representation of ovarian cancer prevention and treatment 

 strategies .............................................................................................................. 28

Figure 5. Diagnostic performance of genomic tests to predict response to 

 chemotherapy plotted in receiver operating characteristic (ROC) space............. 50 

Figure 6. Studies of the ability of serum markers to predict positive disease on 

 second-look laparotomy following primary surgery and adjuvant 

 chemotherapy plotted in ROC space.................................................................... 54 

Figure 7. Diagnostic performance of studies of tests to predict suboptimal 

 cytoreduction (surgical debulking with residual disease > 1 cm) 

 plotted in ROC space ........................................................................................... 56 

Figure 8. Age-specific probability of developing ovarian cancer (assuming Stage II 

 is necessary transition)......................................................................................... 65 

Figure 9. Age-specific probability of dying from ovarian cancer (assuming Stage II 

 is necessary transition)......................................................................................... 65 

Figure 10. Age-specific probability of developing ovarian cancer (assuming Stage I 

 can transition directly to Stage III) ...................................................................... 66 

Figure 11. Age-specific probability of dying from ovarian cancer (assuming Stage I 

 can transition directly to Stage III) ...................................................................... 66 

Figure 12. Effect of screening frequency on reduction in cancer mortality at different 

 levels of test sensitivity........................................................................................ 70 
x

Figure 13. Effect of screening frequency on population negative predictive value at 

 different levels of test sensitivity (specificity fixed at 99%) ............................... 72 

Figure 14. Effect of screening frequency on population positive predictive value at 

 different levels of test specificity (sensitivity fixed at 99%) ............................... 73 

Tables 

Table 1. Age-adjusted annual incidence and mortality per 100,000 women by race 

 and ethnicity, 1992-2002 ..................................................................................... 9 

Table 2. Current usage of genomic tests in ovarian cancer ............................................... 14 

Table 3. Results of abstract and full-text screening........................................................... 21 

Table 4. Included full-text articles by research question ................................................... 21 

Table 5. Tests and applications covered by articles included in this report....................... 22 

Table 6. Studies of cancer antigen 72-4 (CA-72-4)........................................................... 39 

Table 7. Studies of vascular endothelial growth factor (VEGF)........................................ 40 

Table 8. Studies of other single gene products .................................................................. 41 

Table 9. Sensitivity and specificity of genomic tests for predicting response to 

 chemotherapy....................................................................................................... 48 

Table 10. Sensitivity and specificity of genomic tests for identifying patients with 

 residual disease at second-look laparotomy......................................................... 52 

Table 11. Sensitivity and specificity of tests for predicting which patients will achieve 

 optimal cytoreduction at primary surgical debulking .......................................... 55 

Table 12. Overall and age-specific stage distribution of ovarian cancer at diagnosis......... 67 

Table 13. Baseline estimates for annual probability of progressing between stages and 

 stage-specific probability of progression............................................................. 67 

Table 14. Parameter estimates resulting in 20% reduction in ovarian cancer mortality 

 under different strategies...................................................................................... 68 

Table 15. Estimated lifetime number of false positive results at different screening 

 frequencies and test specificities.......................................................................... 73 

Appendixes 

Appendix A: Exact Search String 

Appendix B: List of Excluded Studies 

Appendix C: Sample Data Abstraction Forms 

Appendix D: Evidence Tables 

Appendix E: Peer Reviewers 

Appendixes (including Evidence Tables) for this report are provided electronically at 

http://www.ahrq.gov/downloads/pub/evidence/pdf/genomicovc/genovc.pdf.
1

Executive Summary 

Introduction 

Ovarian cancer is the leading cause of cancer death from gynecologic malignancies in the 

United States, with an annual incidence of over 25,000 and an annual mortality of approximately 

14,000. Cancer incidence increases dramatically with age. 

The high case-fatality rate has largely been attributed to the fact that most ovarian cancers are 

diagnosed in advanced stages (Stage III, where the cancer has spread beyond the pelvis to the 

organs of the upper abdominal cavity, and Stage IV, where the cancer has spread outside the 

peritoneal cavity). Stage I cancer (limited to the ovaries) has a survival rate of over 90 percent. 

There are five potential strategies for prevention of the morbidity and mortality from ovarian 

cancer. One is primary prevention through either medical or surgical therapy in the general 

population. Although observational studies suggest that the risk of developing ovarian cancer is 

reduced in women who used oral contraceptives or underwent tubal ligation, there are no 

prospective trials to allow estimation of the risks and benefits of these options specifically for 

ovarian cancer prevention. Although in theory prophylactic oophorectomy at the time of 

hysterectomy for other diseases should almost eliminate the chances of developing ovarian 

cancer, there are also no prospective studies of the benefits of this approach, and a recent 

decision analysis suggested that the harms in terms of other effects might outweigh the benefits. 

An alternative strategy for primary prevention is identifying groups of women at particularly 

high risk of developing ovarian cancer, and then using primary prevention strategies. 

Observational studies suggest that use of oral contraceptives reduces risk of ovarian cancer in 

women with inherited predisposition to ovarian cancer, but this has not been tested 

prospectively. Prophylactic oophorectomy does appear to reduce the risk of ovarian cancer in 

high-risk groups. 

Another strategy for prevention of ovarian cancer mortality is screening to detect early stage 

cancers, either in the general population or in high-risk groups. To date, screening using the 

available technologies of physical examination, ultrasound, and/or cancer antigen 125 (CA-125) 

has not been shown to be effective in either situation. 

Finally, use of targeted therapy based on the results of tests may identify subgroups of 

patients for whom specific therapies are likely to be effective; for example, identification of 

overexpression of human epidermal growth factor 2 (HER 2) in some breast cancers has led to 

improved survival with the use of a monoclonal antibody targeted against the receptor. To date, 

similar breakthroughs have not occurred in ovarian cancer. 

Continued developments in technology have led to rapidly expanding knowledge about 

genes, gene expression, and protein patterns in a variety of disease processes. Because currently 

available strategies for the prevention of ovarian cancer have not proven as effective as 

interventions targeted against other cancers in women, there has been tremendous interest in 

using the tools of genomics and proteomics to identify potential new markers which can be used 

in any of the five classes of strategies. Although the term “genomics” has been used in many 

different ways, for the purposes of this report we define “genomic tests” as one of the following 

broad categories: (1) tests for the presence or quantity of the product of a single gene – the 

classic example of this is radioimmunoassay for CA-125; (2) tests for inherited or acquired 

mutations in genes which convey an increased risk of developing ovarian cancer, or which 
2

predict differential responses to therapy – the classic example is testing for polymorphisms of 

breast cancer genes 1 and 2 (BRCA 1/2); (3) tests for quantitative expression of either single 

genes or multiple genes – differential patterns of expression between normal patients and ovarian 

cancer patients may aid in diagnosis and management, or help identify potential new single gene 

products for evaluation as screening and diagnostic tools; and (4) tests for protein expression, 

particularly in serum, which identify differential patterns between normal patients and patients 

with ovarian cancer. 

This report focuses on the current evidence for the clinical utility of genomic tests, as defined 

above, in any of the five potential strategies for reducing ovarian cancer morbidity and mortality. 

Because evidence on the use of CA-125 for screening and diagnosis of ovarian cancer and the 

use of BRCA1/2 testing for identification of high-risk patients has been covered in recent 

evidence reports, we do not review that evidence directly; we do summarize the results of the 

earlier reports and discuss relevant studies subsequently published. The results of the present 

report are intended primarily to: (a) provide a resource for the Evaluation of Genomics 

Applications in Practice and Prevention (EGAPP) project of the Centers for Disease Control and 

Prevention (CDC); (b) provide a resource for other clinicians and policymakers developing 

guidelines on the use of genomic tests in ovarian cancer prevention; and (c) provide a resource 

for researchers and funding agencies in identifying gaps in our knowledge and research 

priorities. 

Methods 

Working with the Agency for Healthcare Research and Quality (AHRQ), the CDC, the 

EGAPP working group, and members of the technical expert panel, we refined six research 

questions to be addressed, using an analytic framework which incorporated probability of 

developing ovarian cancer, test results, and management based on those tests results. 

We searched MEDLINE® (1966-May 2006). Searches of the databases were supplemented 

by reviews of reference lists of included articles, relevant review articles, and meta-analyses. 

We also searched the Food and Drug Administration web site for relevant documents. The 

searches yielded a total of 1,303 citations. Pairs of readers reviewed each abstract and selected 

552 articles for full text review. Specific inclusion criteria were developed for each question, 

and both readers were required to agree on inclusion. After this review, a total of 113 articles 

were included for abstraction. 

We developed tables to abstract each article, and quality criteria were adapted from the 

evidence report on omega-3 fatty acids for coronary heart disease prevention. For studies of 

diagnostic test performance, 2-by-2 tables were constructed for each included article, and 

sensitivity, specificity, and positive and negative predictive values, with 95 percent confidence 

intervals, were calculated. 

We also further refined a Markov model of the natural history of ovarian cancer; the model is 

able to closely approximate age-specific incidence and mortality from ovarian cancer under two 

different assumptions about natural history – one that requires a stepwise progression through all 

four stages of the disease, and one which allows some cancers to spread directly from the ovaries 

(Stage I) to the upper abdomen (Stage III). The model is then used to estimate the implications 

of these different assumptions on the relative effectiveness of different prevention strategies. 
3

Results 

Literature on Key Questions 

Question 1: What is the evidence that ovarian cancer genomic tests performed in a 

typical clinical laboratory actually measure what they are purported to measure? The 

published data on clinical laboratory performance suggests that currently available 

radioimmunoassays for single gene products have acceptable reproducibility and reliability, 

although even this level of variability may have some impact on clinical interpretation of results, 

especially when comparing relatively small serial changes, or levels close to the discriminatory 

threshold. 

There is insufficient evidence to estimate how newer technologies such as microarrays or 

protein profiles would perform in a “typical clinical laboratory.” 

Question 2: What is the sensitivity and specificity of genomic tests in detecting ovarian 

cancer in asymptomatic and symptomatic women, including high-risk women? In general, 

single gene products other than CA-125 have not been shown to be useful in the diagnosis of 

ovarian cancer, either in symptomatic or asymptomatic women; the sensitivity of CA-125 in 

screening populations is approximately 80 percent. Small sample sizes, lack of detail on the 

prediagnosis history of patients, and an unrealistically high prevalence of ovarian cancer in the 

majority of studies make it difficult to assess how any of these tests would perform in clinical 

practice. 

Estimating the clinical value of more complex tests, using multiple gene and/or protein 

markers, is even more difficult. Studies of protein expression, in particular, are limited by lack of 

consensus on appropriate statistical methods, small sample sizes with substantially higher 

prevalences of ovarian cancer than would be found in the general population, spectrum bias, lack 

of reproducibility, and uncertainty about the specificity of the biological processes resulting in 

the observed protein patterns. 

Question 3: What is the evidence that genomic testing to detect ovarian cancer in 

asymptomatic women, including high-risk women, changes clinical management and leads 

to improved clinical outcomes? We did not identify any evidence on the value of tests other 

than CA-125 to detect ovarian cancer in asymptomatic women. CA-125 has not been shown to 

improve ovarian cancer mortality or quality of life; in series of women with mutations of 

BRCA1 and BRCA2, screening with CA-125 and transvaginal ultrasound does not appear to 

prevent development of advanced stage ovarian cancer. 

Question 4: What is the evidence that genomic testing in women with clinical suspicion 

of ovarian cancer or with already-diagnosed ovarian cancer changes clinical management 

and leads to improved health outcomes? Although there is a reasonable amount of data on the 

association between genomic tests, particularly CA-125, and the likelihood of different clinical 

outcomes, we did not identify any studies which provided evidence for changes in management 

leading to improved outcomes based on the results of the tests, other than for CA-125. Based on 

the results of another evidence report, CA-125 is helpful in distinguishing malignant from benign 

masses in postmenopausal women. 

Question 5: What are the harms of using genomic tests for ovarian cancer prevention 

and management? The majority of the available literature focuses on BRCA1/2 testing and 

rarely describes results specifically for ovarian cancer. In the few studies that did, concerns 
4

about the risk of ovarian cancer were considerably less than for breast cancer; it is unclear 

whether testing for genetic markers of ovarian cancer susceptibility alone has different 

implications compared to testing for genes which affect both breast and ovarian cancer risk. 

Conceptually, the harms of testing for genetic susceptibility for ovarian cancer should be no 

different than testing for genetic susceptibility of other cancers; the main issues are the 

effectiveness and potential risks of prevention strategies in those who are identified as high risk 

(primarily the risks of prophylactic oophorectomy), and issues related to reproduction. 

Similarly, the qualitative harms of the use of genomic tests for screening, diagnosis, and 

management – the psychological effect of a potential cancer diagnosis, the risks of diagnostic 

and therapeutic procedures including laparotomy, the harms of a false negative result leading to 

delayed or inappropriate management – are not conceptually different for genomic tests than for 

other types of tests, such as imaging; the main difference lies in the quantitative risks of these 

events, which in turn are determined by the sensitivity and specificity of the test and the pretest 

probability of disease. 

Question 6: Has direct-to-consumer and direct-to-physician marketing of genomic tests 

for ovarian cancer increased the “appropriate” use of these tests? We identified two studies 

which compared utilization of BRCA1/2 tests for breast and ovarian cancer susceptibility before 

and after an advertising campaign; in both cases, utilization was compared in cities where the 

campaign was put in place to geographically distant cities where there was no formal campaign. 

The studies suggested increased utilization of testing, and one study found that the positive 

predictive value of testing declined after the campaign, but there was no way to judge whether 

the changes in testing were “appropriate.” 

Modeling Results 

The model is able to approximate reported age-specific incidence and mortality from ovarian 

cancer under both assumptions about natural history. At a given value for test sensitivity, 

screening was less effective in reducing mortality in a model assuming direct transition from 

Stage I to Stage III than one assuming that all cancers progress to Stage II prior to Stage III. 

However, screening frequency was much more important than test sensitivity; even at a test 

sensitivity of 99 percent, screening frequencies of less than 12 months are needed to reduce 

ovarian cancer mortality by more than 50 percent. At these high screening frequencies, positive 

predictive values are less than three percent, even for a test with specificity of 99 percent. 

Discussion 

Limitations of the Report 

The report did not include non-English publications. We did not formally attempt to estimate 

pooled sensitivity and specificity for tests because of heterogeneity of study design. Because 

many of the parameters in the natural history of ovarian cancer are unknown, any model will 

require assumptions and imputation of key parameters; calibrating a cohort model to crosssectional data may result in errors in the imputation of these parameters because of unmeasured 

cohort effects in the cross-sectional data. 
5

Limitations of the Literature 

Common limitations of the literature included: failure to adequately describe relevant patient 

characteristics; small sample size with subsequent wide confidence intervals for estimates of 

sensitivity and specificity; unrealistically high prevalences of ovarian cancer; a spectrum of 

disease severity which does not reflect screening populations; lack of reproducibility for complex 

statistical algorithms; potentially inappropriate choices for cases and controls in initial 

developmental studies; and underlying assumptions about the natural history of ovarian cancer 

that may not reflect the actual biology of the disease. 

Future Research 

Research priorities include: 

• A minimal consensus data set on key patient characteristics, with results presented with 

stratification by those characteristics as appropriate; 

• Consensus reporting of key laboratory performance characteristics such as 

reproducibility, with estimates of the impact of reproducibility on test performance in 

practice; 

• Documentation of the effect of any biological variability in test results within subjects on 

interpretation of results, especially for tests designed to be used in a serial fashion; 

• Better characterization of true “negative” results, with documentation of followup; 

• Evaluation of tests in realistic clinical situations, especially with regards to pretest 

probability; 

• Explicit evaluation of the effect of management changes based on test results on patient 

outcomes; and 

• Better understanding of the natural history of ovarian cancer in order to help prioritize 

research into better prevention strategies. 

Conclusions 

Despite intensive research efforts, ovarian cancer remains a leading cause of cancer death in 

women, and efforts at reducing its impact have been noticeably less successful than those for 

other cancers in women. 

The prospect of new strategies for the prevention of ovarian cancer morbidity and mortality 

based on greater understanding of the molecular biology of the disease is exciting; unfortunately, 

we did not find any evidence that currently available tests have had a substantial impact on 

improving patient outcomes. Our modeling work suggests that the natural history of ovarian 
6

cancer may make substantial mortality reductions difficult using a strategy based primarily on 

screening. Although research remains promising, adaptation of genomic tests into clinical 

practice must await appropriately designed and powered studies in relevant clinical settings. 
Evidence Report

9

Chapter 1. Introduction 

Ovarian Cancer 

Cancer of the ovaries is the leading cause of cancer death from gynecologic malignancies, 

with an annual incidence of over 25,000 and an annual mortality of approximately 14,000.1

 

Cancer incidence increases dramatically with age, being relatively rare prior to age 50 (Figure 1). 

Figure 1. U.S. ovarian cancer incidence by age, 1992-2003 

0

10

20

30

40

50

60

70

5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85+

Age

Incidence/100,000

USCS

SEER

Sources: Surveillance, Epidemiology, and End Results (SEER) Program2 and United States Cancer Statistics 

(USCS).3

Ovarian cancer incidence varies by race and ethnicity. Both incidence and mortality are 

highest for white women (Table 1). 

Table 1. Age-adjusted annual incidence and mortality per 100,000 women by race and ethnicity, 1992-2002* 

 White AfricanAmerican Asian/Pacific 

Islander 

Native 

American 

Hispanic 

Incidence 

SEER 15.1 10.3 10.4 8.9 11.9 
10

Table 1. Age-adjusted annual incidence and mortality per 100,000 women by race and ethnicity, 1992-2002* 

(continued) 

 White AfricanAmerican Asian/Pacific 

Islander 

Native 

American 

Hispanic 

USCS 13.5 9.8 9.5 5.4 11.0 

Mortality 

SEER 9.3 7.6 4.8 5.1 6.2 

USCS 9.4 7.4 5.1 4.9 6.0 

* Sources: Surveillance, Epidemiology, and End Results (SEER) Program2

 and United States Cancer Statistics 

(USCS).3

Malignant tumors of the ovary can either arise in the ovary (primary ovarian cancer) or be the 

result of metastasis from another site, such as the breast or colon. Primary ovarian tumors, 

whether benign or malignant, can arise from three broad types of cells: the cells on the surface 

(epithelial cells); the cells that form eggs (germ cells); and the cells surrounding the eggs, 

including the cells that produce ovarian hormones (sex cord-stromal cells). Epithelial tumors are 

the most common type, accounting for 60 percent of all ovarian tumors and up to 90 percent of 

primary cancers. Sex cord-stromal tumors account for 10 to 15 percent of all tumors, while germ 

cell tumors account for 25 percent of tumors. In general, sex cord-stromal tumors and germ cell 

tumors are relatively more common in younger premenopausal women. Thus, although ovarian 

cancer is relatively rare in younger women, when it does occur it is more likely to be a nonepithelial cancer than cancers in postmenopausal women.4

Within the broad classification of epithelial, sex cord-stromal, and germ cell tumors, tumors 

are further classified by the individual cell types from which the tumor is derived. For example, 

the most common epithelial tumors are serous and mucinous tumors, the most common sex-cord 

stromal tumors are fibromas (arising from the connective tissue surrounding eggs), and the most 

common germ cell tumors are teratomas. Within each histological class, tumors can be benign 

or malignant, based on their ability to metastasize.4

Some epithelial tumors are classified as “borderline” or “low malignant potential” (LMP) 

tumors. These are tumors in which there is no invasion into the ovarian stroma, but histologic 

evidence of proliferation (increased cell division, changes in the appearance of the cell nucleus). 

There is controversy over whether these tumors represent preinvasive cancer, and, if untreated, 

would go on to become a cancer, or whether they represent a subtype of tumor which has a 

relatively small chance of becoming a cancer.4

 In estimating the diagnostic accuracy of tests for 

determining whether a mass is benign or malignant, whether one classifies LMP tumors as 

benign or malignant can have an effect on the estimates of test performance, as we will discuss 

later in the report. 

Ovarian cancer spreads primarily by dissemination throughout the peritoneal cavity; common 

sites of metastasis are the small and large bowel, the omentum, the liver, and the diaphragm. 

Spread to retroperitoneal lymph nodes is also common. 

Treatment for ovarian cancer consists of surgical removal of the ovaries, fallopian tubes, and 

uterus (if present), along with as much metastatic disease as possible; if there is no obvious 

spread beyond the ovaries, the lymph nodes are sampled to determine if there has been lymphatic 

metastasis. Surgery is followed by chemotherapy, with responsiveness to chemotherapy 
11

depending on the amount of tumor left after surgical removal and the cell type of tumor, among 

other factors.4

The high case-fatality rate observed in ovarian cancer has largely been attributed to the fact 

that most ovarian cancers are diagnosed in advanced stages (Stages III, where the cancer has 

spread beyond the pelvis to organs of the upper abdominal cavity, and IV, where the cancer has 

spread outside of the peritoneal cavity), when survival is poor. Stage I cancer (limited to the 

ovaries) has a survival rate of over 90 percent. Thus, there has long been a clinical and research 

emphasis on identifying methods for early detection of ovarian cancer, under the rationale that 

increasing the proportion of cancers detected in early stages will lead to decreases in morbidity 

and mortality. 

Approaches to Reducing Ovarian Cancer 

Morbidity and Mortality 

 Conceptually, there are five basic strategies for reducing ovarian cancer morbidity and 

mortality; we briefly review the rationale for each below. 

Primary Prevention in the Entire Population 

Primary prevention can be achieved either through medical or surgical treatment which 

preserves the ovaries but reduces the incidence of ovarian cancer, or by removal of the ovaries 

themselves.

 Although oral contraceptives and tubal ligation have consistently been associated with 

reduction in ovarian cancer in epidemiological studies,5

 the use of these measures as prophylaxis 

has never been prospectively tested in an adequately designed and powered trial; given the 

relative rarity of ovarian cancer, as well as the rarity of some of the serious side effects of oral 

contraceptives, such as an increased risk of deep vein thrombosis, such a trial may ultimately not 

be feasible. 

 Although primary peritoneal carcinomatosis, a condition which histologically and clinically 

is almost identical to ovarian cancer,6

 can occur after removal of the ovaries, it appears to be rare 

in average-risk women.7

 Bilateral oophorectomy in perimenopausal women undergoing 

hysterectomy for other causes has traditionally been recommended for prevention of ovarian 

cancer; however, this practice has also not been subjected to rigorous prospective study. A 

recent decision analysis suggests that, based on the available evidence, the potential harms from 

the other effects of oophorectomy may outweigh the benefits of ovarian cancer prevention.8

Primary Prevention in Women at Increased Risk for Developing 

Ovarian Cancer

This strategy depends on two things: the availability of a test for ascertainment of 

individuals at increased risk for developing ovarian cancer, and the availability of effective 

primary preventive treatment. 

 Although no randomized trials have been conducted, several observational studies suggest 

that women with an inherited predisposition to developing ovarian cancer who undergo 
12

prophylactic oophorectomy are at reduced risk of developing ovarian cancer compared to the 

expected incidence in this population.9-11 Observational data also suggests that oral 

contraceptive use reduces ovarian cancer incidence in high-risk groups.12,13

Secondary Prevention through Screening 

Unlike cervical cancer, where screening has proven remarkably effective, no screening test 

has proven effective in reducing ovarian cancer mortality. Physical examination using the 

bimanual pelvic examination,14 serum testing using the tumor marker cancer antigen 125 (CA125), and imaging using vaginal ultrasound15 have all proven ineffective; the U.S. Preventive 

Services Task Force (USPSTF) gives a D recommendation to current methods for screening for 

ovarian cancer (at least fair evidence that the practice is ineffective or that harms exceed 

benefits). Additional studies are currently being conducted.

Secondary Prevention through Screening in Women at High Risk 

As with primary prevention, this strategy is dependent on both effective screening methods 

and the ability to accurately determine who is at “high risk.” Screening, including more frequent 

screening, has not resulted in a reduced ovarian cancer incidence, or a substantial shift in stage 

distribution of detected cancers, in high-risk groups.16-19

Improved Therapy after Diagnosis of Ovarian Cancer 

Identification of women who are particularly likely to respond to specific therapies, or 

identification of new targets for therapy, could lead to improved survival and quality of life in 

women with ovarian cancer. Although there is much ongoing research into possible targets for 

therapy, ovarian cancer therapy lags behind therapy for breast cancer, where identification of 

particular molecular targets appears to be effective.20 This category could also include tests that 

help distinguish particular types of ovarian cancer from other types, and to distinguish primary 

ovarian cancer from cancer metastatic to the ovary from other sites, since misclassification could 

lead to relatively less effective therapy. 

Genomic Tests 

 Advances in molecular biology, including the decoding of the human genome, have led to 

intensive research across the spectrum of human disease. The terms “genomics” or “genetic 

test” have been used differently in different settings. For the purposes of this report, we include 

the following types of tests based on the interests of the Agency for Healthcare Research and 

Quality (AHRQ), the Centers for Disease Control and Prevention (CDC), and the Evaluation of 

Genomics Applications in Practice and Prevention (EGAPP) program. 
13

Tests Measuring Single Gene Products 

These tests measure the concentration or presence/absence of proteins which are associated 

with the presence of ovarian cancer. The classic example of this type of test is CA-125, a protein 

for which several validated, commercially available assays are available. Levels of CA-125 are 

increased in patients with ovarian cancer compared to normal subjects, and the test is useful in 

discriminating benign from malignant masses in postmenopausal patients.14 Typically, these 

tests are for proteins detectable in serum, although, in some cases, tests may be peformed in fluid 

aspirated from an ovarian mass or the peritoneal cavity, or immunohistochemistry stains may be 

performed on ovarian or tumor tissue.

Tests for Variations in DNA 

Tests for inherited or acquired mutations (e.g., breast cancer genes 1 and 2 [BRCA1/2]) in 

single genes can potentially identify patients at higher risk for developing cancer. Alternatively, 

mutations in some genes in the cancer itself may indicate greater or lesser likelihood of 

responding to a given therapy, or of developing side effects with a given therapy. In addition, 

changes in the overall pattern of the genome, such as loss of heterozygosity, are characteristic of 

many cancers, and potentially have a role in diagnosis.21 Finally, epigenetic changes (reversible 

changes to DNA and chromatin, such as the addition or subtraction of methyl groups), are 

currently under active investigation in a variety of cancers, including ovarian cancer.22-24

Gene Expression 

Quantitative or semi-quantitive measurement of the expression (either higher or lower than 

normal) of particular genes in serum or tumor tissue has the potential for help in diagnosis (either 

as a screening tool or in discrminating particular subtypes of cancer), or potentially to aid in 

targeted therapy; for example, overexpression of human epidermal growth factor receptor 2 

(HER-2) in breast cancer predicts responsiveness to therapy with an antibody against the 

receptor, trastuzimab.20 Both single genes, and patterns of expression of multiple genes using 

technologies such as microarray, can be helpful. The introduction of high-throughput technology 

has facilitated the search for patterns of expression associated with specific outcomes, allowing 

simultaneous comparison of multiple genes in specimens from patients with and without the 

outcome. Studies of gene expression may also serve as the basis for identification of single gene 

products which can subsequently be evaluated as markers for screening, diagnosis, or 

management guidance. 

Proteomics/Protein Characterization 

Finally, quantification of protein patterns, typically in serum, can be performed using mass 

spectroscopy; one of the more common techniques is surfance-enhanced laser desorption 

inonization time-of-flight (SELDI-TOF).25 As with multiple gene expression, protein patterns 

can be compared between patients with and without a given outcome of interest, or used to 

identify single markers. 
14

Interest in Genomic Tests for Ovarian Cancer 

Although there is widespread interest in genomic tests for prevention of morbidity and 

mortality for a wide range of conditions, ovarian cancer has been an area of particular interest on 

the part of the scientific community and lay public, largely because of the lack of an effective 

screening test. In particular, efforts to rapidly commercialize a proteomics-based test, 

OvaCheckTM, prior to validation of the test in a large population, has led to a realization of the 

need for critical evaluation of the validity of these tests.26,27

Use of Genomic Tests in Prevention of 

Ovarian Cancer Morbidity and Mortality 

 Despite the broad definition of “genomic tests” used here, there are very few tests currently 

in clinical use for ovarian cancer (Table 2), based on a recent review of both the published and 

“grey” literature by the Tufts-New England Medical Center Evidence-based Practice Center.28 

Table 2. Current usage of genomic tests in ovarian cancer28

Test Type of test Use of test 

Increased 

risk 

Screening Diagnosis Management 

Commercially available 

Routine use in ovarian cancer 

Cancer antigen 125 (CA-125) Single gene 

product 

 X X 

Beta human chorionic gonadotropin 

(β-hCG; germ cell tumors) 

Single gene 

product 

 X X 

Breast cancer gene 1/2 (BRCA1/2) Genetic variation X 

Carcinoembryonic antigen (CEA) Single gene 

product 

 X 

Investigational for ovarian cancer 

Cancer antigen 27-29 (CA-27-29) Single gene 

product 

 X X 

Lipid-associated sialic acid (LASA) Single gene 

product 

 X X 

Human epidermal growth factor 

receptor 2 (HER2)/neu 

Gene expression X 

Investigational 

Chromosome 8q gain Genetic variation X 

DNA methylation Genetic variation X X X 

Epidermal growth factor receptor 

(EGFR) 

Single gene 

product 

 X X 
15

Table 2. Current usage of genomic tests in ovarian cancer28 (continued) 

Test Type of test Use of test 

Increased 

risk 

Screening Diagnosis Management 

Genome-wide loss of 

heterozygosity 

Genetic variation X 

Lysophospholipids (LSA) Single gene 

product 

 X X 

Matrix metalloproteinases (MMP) Single gene 

product 

 X 

Protein expression profiles 

(OvaCheck, etc.) 

Protein 

expression 

 X 

Urinary plasminogen activator Single gene 

product 

 X 

 Because the majority of applications for genomic tests are investigational, there are few 

formal guidelines for their use, other than recommendations for the use of CA-125 as an adjunct 

to diagnosis of ovarian cancer,29 against the use of CA-125 for routine screening for ovarian 

cancer,15,30 and for the use of BRCA1 and 2 testing in women with family histories suggestive of 

familial breast or ovarian cancer.31

Because the use of BRCA 1 and 2 testing for identifying women at high risk and the use of 

CA-125 for screening and as a diagnostic test in women with an adnexal mass have been recently 

covered by AHRQ evidence reports,14,30,31 we have summarized the findings of these reports in 

the appropriate sections, incorporating any additional relevant evidence published subsequent to 

the reports. 

In this review, and particularly in the discussion of the results and suggestions for future 

research, we will attempt to identify: (a) issues related to evaluation of specific strategies for 

ovarian cancer prevention; (b) issues related to evaluation of specific classes of “genomic tests;” 

and (c) where applicable, specific issues related to the evaluation of a given class of genomic test 

for a given prevention strategy.

17

Chapter 2. Methods 

This section of the report describes the basic methodology used to develop the evidence 

report, including topic assessment and refinement, analytic framework, literature search 

strategies and results, literature screening, quality assessment, data abstraction methods, and 

quality control procedures. 

Topic Assessment and Refinement 

The Centers for Disease Control and Prevention (CDC) and the Agency for Healthcare 

Research and Quality (AHRQ) originally identified six key questions to be addressed by the 

report, which is intended to assess the evidence for the diagnostic accuracy, benefits, and harms 

of genomic tests in screening and management of ovarian cancer. The Duke research team 

clarified and refined the overall research objectives and key questions by first consulting with the 

two study sponsors, AHRQ and CDC, and then convening a national panel of technical experts 

to serve as advisors to the project. These experts were selected to represent relevant specialties. 

Members of the technical expert panel were: 

Alfred O. Berg, M.D., M.P.H.; Department of Family Medicine, University of Washington; 

Seattle, WA (member of the CDC Evaluation of Genomic Applications in Practice and 

Prevention [EGAPP] Working Group) 

Katrina Armstrong, M.D., M.S.C.E.; Leonard Davis Institute of Health Economics, 

University of Pennsylvania School of Medicine; Philadelphia, PA (EGAPP Working Group 

member) 

Jeffrey Botkin, M.D., M.P.H.; Department of Pediatrics and Medical Ethics, University of 

Utah; Salt Lake City, UT (EGAPP Working Group member) 

JoEllen Schildkraut, Ph.D.; Department of Prevention Research, Duke University; Durham, 

NC 

As a result of an initial conference call with the technical experts, AHRQ, and CDC, the 

Duke research team finalized the key research questions to be included in the report and the 

approach that would be used to address them. The final key questions are as follows: 

• Question 1: What is the evidence that ovarian cancer genomic tests performed in a 

typical clinical laboratory actually measure what they are purported to measure? 

• Question 2: What is the sensitivity and specificity of genomic tests in detecting ovarian 

cancer in asymptomatic and symptomatic women, including high-risk women? 
18

• Question 3: What is the evidence that genomic testing to detect ovarian cancer in 

asymptomatic women, including high-risk women, changes clinical management and 

leads to improved health outcomes?

• Question 4: What is the evidence that genomic testing in women with clinical suspicion 

of ovarian cancer or with already-diagnosed ovarian cancer changes clinical management 

and leads to improved health outcomes?

• Question 5: What are the harms of using genomic tests for ovarian cancer prevention and 

management?

• Question 6: Has direct-to-consumer and direct-to-physician marketing of genomic tests 

on ovarian cancer increased the “appropriate” use (as defined by study investigators) of 

these tests? 

Analytic Framework 

The methodological approach to this review was designed to inform the EGAPP Working 

Group’s deliberations in formulating evidence-based recommendations for the use of genetic 

testing in the detection and management of ovarian cancer. We developed a project-specific 

analytic framework to address the key questions within the context of a standardized evidence 

report (Figure 2). 

Figure 2. Analytic framework for evidence report 

Note: Numbers refer to key questions 

The analytic framework depicted above serves to clarify the relevant key questions as 

follows: 

• Genomic tests can detect an inherited predisposition, genes and proteins that are 

associated with the presence of cancer, or genes and proteins that identify targets for 

therapy or predict response to therapy. Question 1 addresses whether available tests 

perform as intended at the level of the laboratory (“analytic validity”).
19

• Genomic tests in the second category, above, may detect ovarian cancer either in women 

without symptoms (used as a screening test) or as part of the evaluation of women with 

symptoms (Question 2).

• Based on the results of genomic testing, women may have different strategies; women 

with a predisposition to ovarian cancer may undergo primary or secondary prevention 

strategies, while, ideally, asymptomatic women detected through genomic tests will have 

reduced ovarian cancer mortality, without unacceptable levels of harm from testing and 

diagnosis, than women who do not undergo genetic testing (Question 3).

• Genomic testing can potentially serve as a test to help discriminate cancer from benign 

conditions in women with symptoms, or lead to specific therapies with better outcomes in 

women who have already had a diagnosis of ovarian cancer (Question 4).

• As with any test, there are potential harms associated with genomic testing. These 

include anxiety about the risk of ovarian cancer and difficult decisions regarding 

reproduction and possible prophylactic surgery in women with inherited predispositions; 

additional diagnostic tests, including diagnostic surgery, or use of inappropriate therapy, 

in women with false-positive tests; and the failure to further evaluate, or appropriately 

treat, women with false-negative tests (Question 5). 

• Although not in the formal pathway, marketing to consumers and physicians may make 

women more likely to undergo testing. Particularly in asymptomatic women, this testing 

may lead to (a) diagnosis of a predisposition in the absence of clear evidence on 

appropriate management strategies, or (b) diagnosis of “abnormality,” leading to 

additional tests, including surgery (Question 6). 

Literature Search and Review 

Sources 

The primary source of literature was MEDLINE® (1966-May 2006). Searches of this 

database were supplemented by reviews of reference lists contained in all included articles and in 

relevant review articles and meta-analyses. 

Search Strategies 

The basic search strategy used the National Library of Medicine’s Medical Subject Headings 

(MeSH) key word nomenclature developed for MEDLINE.® Searches were limited to articles 

published in English. The exact search string used is given in Appendix A.∗

 The three searches 

yielded a total of 1,303 citations, whose records were maintained in a ProCite (Thompson ISI 

ResearchSoft, Berkeley, CA) database. 

 

∗ Appendixes cited in this report are provided electronically at http://www.ahrq.gov/clinic/tp/genovctp.htm.
20

Abstract and Full-text Screening 

Paired researchers from the Duke research team independently reviewed abstracts and 

classified each as “include” or “exclude” according to study-specific criteria, which they also 

developed. Abstracts were included if at least one of the paired reviewers recommended that it 

be included. A total of 552 abstracts were included for the further full-text review stage. 

Interrater reliability for include/exclude decisions at the abstract screening stage was tested by 

having seven pairs of readers review 813 abstracts. Agreement was good to excellent (kappa 

0.36 to 0.75). 

At the full-text review stage, the paired researchers independently reviewed a set of the 

articles and indicated a decision to “include” or “exclude” the article for the data-abstraction 

stage. When a pair of reviewers arrived at different opinions about whether to include an article, 

they were asked to reconcile the difference. Detailed inclusion and exclusion screening criteria 

were developed by research question and are described immediately below. 

Screening Criteria 

Abstracts were included for full-text review if they met the criteria described below, or if 

insufficient information was provided to judge whether they met the criteria. Articles were 

included for abstraction if full-text review showed that all criteria were met. 

An article was included if it pertained to: 

(1) Epithelial ovarian cancer or primary peritoneal carcinomatosis; and 

(2) Genomics as defined by AHRQ for this project to mean any gene-based test used for 

predicting risk of developing disease, screening, diagnosis of disease, disease 

management, or prognosis only in strategies for the prevention of ovarian cancer 

morbidity and mortality. These included single gene products (e.g., cancer antigen 125 

[CA-125]); genetic variations (e.g., breast cancer genes 1/2 [BRCA1/2] ); gene 

expression (e.g, human epidermal growth factor receptor 2 [HER2]/neu); and either 

single or multiple genes (e.g., microarrays) and protein expression (e.g., mass 

spectroscopy of multiple proteins in sera of patients with ovarian cancer compared with 

controls). 

We included tests that: 

(1) Detect the presence of inherited mutations or gene polymorphisms which increase the 

risk of development of ovarian cancer; 

(2) Genes, RNA, or protein markers which are present or produced (or are present or 

produced in greater quantity) only in cells that have already undergone the transformation 

to cancer, and which can be used to detect asymptomatic or symptomatic cancers; and 
21

(3) Genes or proteins which may help predict the response to specific types of therapy, or 

themselves be targets of specific therapies. 

We excluded the following: 

(1) Studies on BRCA1/2 screening and identification of risk covered in an earlier AHRQ 

evidence report;31

(2) Studies on CA-125 screening and diagnosis covered in earlier AHRQ evidence 

reports;14,30

(3) Studies involving only germ cell or stromal ovarian cancer, or non-ovarian primary; 

(4) Studies where patients are not the denominator; 

(5) Studies involving a cell line only; 

(6) Studies where reported data do not allow construction of a 2-by-2 table. 

 Summaries of the results of the abstract screening and full-text review are provided in Tables 

3 and 4. A list of excluded articles, with reasons for exclusion, is provided in Appendix B.∗

Table 3. Results of abstract and full-text screening 

Articles identified 1,303 

Abstracts screened 1,303 

 Included 552 

 Excluded 751 

Full-text articles screened 549† 

 Included 113 

 Excluded 436 

†

 We were unable to obtain copies of 3 articles that passed the abstract screen. 

Table 4. Included full-text articles by research question 

Question Number of 

articles 

Question 1: Analytic validity of testing 32 

Question 2: Sensitivity and specificity of tests 50 

Question 3: Impact on clinical management of asymptomatic patients 0 

Question 4: Impact on clinical management of diagnosed patients 29 

Question 5: Harms of testing 4 

 

∗ Appendixes cited in this report are provided electronically at http://www.ahrq.gov/clinic/tp/genovctp.htm.
22

Question Number of 

articles 

Question 6: Impact of direct-to-consumer or physician marketing 2 

Total number of included articles 113†

†

 Total does not equal sum of number of articles across questions because some articles were included for more than 

one question. 

Table 5 depicts the specific tests and clinical application of the tests covered by the included 

articles. 
23

Table 5. Tests and applications covered by articles included in this report 

Clinical use of test Category of 

genomic test Predisposition Screening Diagnosis Management 

Single gene 

products 

 CA-125 Alpha-L-fucosidase 

CA-125, CA-72-4, CA15-3, CA-19-9 

CEA 

c-erb-2 

CYFRA 21-1 

Epithelial cell 

adhesion molecule 

FAS 

G-CSF 

hK6, hK10 

IL-6, IL-8 

M-CSF 

OVX1 

p55, p75 (tumor 

necrosis factor 

receptors) 

Secretory leukocyte 

protease inhibitor 

Serum cadherin 

Soluble IL-2 alpha 

Soluble intracellular 

adhesion molecule 

TPS 

TATI 

Urinary gonadotropin 

peptide 

VEGF 

Bcl-2 (anti-apoptosis 

protein) 

CA-125 

CASA 

Cathespin-D 

CYFRA 21-1 

c-erb-B2 

hK6, hK10 

IL-6 

LRP 

Mdm2 

MDR-1 

MRP1/2 

nm23 (metatstasis 

suppressor) 

Pgp 

p53 = TP53 

(transcription factor) 

TN 

TPS 

Genetic 

variations 

BRCA1 

BRCA2 

 p53 = TP53 

(transcription factor) 

Gene expression CK19 

Multiple genes: 

ascitic fluid 

Multiple genes: 

immunohistochemistry 

c-erb-B2 

Multiple genes: 

microarray 

Proteomics Ciphergen 

ProteinChips: SAX2, 

WCX2 

Mass spectrometry 

using SELDI 

(statistical methods 

varied widely) 

 
24

Data Abstraction and Development 

of Evidence Tables 

The Duke research team developed data abstraction forms/evidence table templates for 

abstracting data for the various key questions (Appendix C∗). Based on clinical expertise, a pair 

of researchers was assigned to the research questions to abstract data from the eligible articles. 

One of the pair abstracted the data, and the second researcher over-read the article and the 

accompanying abstraction to check for accuracy and completeness. The completed evidence 

tables are provided in Appendix D.* 

Quality Assessment Criteria 

At the data abstraction stage, abstractors were asked to evaluate each included article for 

factors affecting internal and external validity. The quality assessment criteria used for this 

purpose were previously developed by the Tufts-New England Medical Center Evidence-based 

Practice Center for an evidence report on “Effects of Omega-3 Fatty Acids on Cardiovascular 

Disease.”32 Abstractors were instructed to assign a “+” or “-” to each item and provide a brief 

rationale for their decisions. Quality criteria assessed in this way were: 

For Questions 1 and 2: 

• Reference standard 

• Verification bias 

• Test reliability/variability 

• Sample size 

• Statistical tests 

• Blinding 

• Definition of +/- on screening test 

For Questions 3-5 (randomized controlled trials [RCTs]): 

• Randomization method 

• Blinding 

• Dropout rate < 20 percent 

• Adequacy of randomization concealment 

For Questions 3-5 (cohort studies): 

• Unbiased selection of the cohort (prospective recruitment of subjects) 

• Large sample size 

• Adequate description of the cohort 

• Use of validated method for genomic test (i.e., analytic validity established) 

• Use of validated method for ascertaining clinical outcomes (e.g., surgical pathology, use 

of validated quality-of-life instrument, death) 

 

∗ Appendixes cited in this report are provided electronically at http://www.ahrq.gov/clinic/tp/genovctp.htm.
25

• Adequate followup period 

• Completeness of followup 

• Analysis (multivariate adjustments) and reporting of results 

 

For Questions 3-5 (case-control studies): 

• Valid ascertainment of cases 

• Unbiased selection of cases 

• Appropriateness of the control population 

• Verification that the control is free of cancer 

• Comparability of cases and controls with respect to potential confounders 

• Appropriateness of statistical analyses 

After evaluating each study against its question- and design-specific quality criteria, 

abstractors applied a three-category (A, B, C) summary quality grading system that has been 

used in previous evidence reports by the Tufts-New England Medical Center Evidence-based 

Practice Center, including the report cited above.32 This scheme defines a generic grading 

system for study quality that is applicable to each type of study design (i.e., RCT, cohort study, 

case-control study). The categories are defined as follows: 

A Least bias; results are valid. A study that mostly adheres to the commonly 

held concepts of high quality, including the following: a formal 

randomized study; clear description of the population, setting, 

interventions, and comparison groups; appropriate measurement of 

outcomes; appropriate statistical and analytic methods and reporting; no 

reporting errors; less than 20 percent dropout; clear reporting of dropouts; 

and no obvious bias. 

B Susceptible to some bias, but not sufficient to invalidate the results. A 

study that does not meet all the criteria in category A. It has some 

deficiencies but none likely to cause major bias. Study may be missing 

information, making assessment of the limitations and potential problems 

difficult. 

C Significant bias that may invalidate the results. A study with serious 

errors in design, analysis, or reporting. These studies may have large 

amounts of missing information or discrepancies in reporting. 

Additional Analyses 

Test Characteristics and Confidence Intervals 

For test characteristics, a Microsoft Excel® spreadsheet was developed that calculated 

appropriate test characteristics (sensitivity, specificity, negative predictive value, positive 

predictive value) for individual studies if studies provided enough data to input (a) values for 

individual cells of a 2-by-2 table, (b) the prevalence of disease and values for sensitivity and 
26

specificity, or (c) sufficient data to solve for two equations involving sensitivity, specificity, or 

predictive values. Ninety-five percent confidence intervals were automatically estimated using 

the approximate formula for proportions: 

p ± 1.96* p * (1− p)/ N , where p = point estimate of proportion, N = total sample size. 

Ovarian Cancer Model 

Model description. We developed a Markov model to estimate the life expectancy for 

asymptomatic women who are considered candidates for potential prevention and screening 

strategies for ovarian cancer. The model tracks a hypothetical cohort of 40-year-old women over 

their lifetimes and compares the impact of one of six strategies for the prevention of ovarian 

cancer on cancer incidence, mortality, and overall life expectancy. 

Simulation model. Women enter the Markov model (Figure 3), which follows the women’s 

natural history of ovarian cancer; the probabilities of each event can be modified based on 

different strategies for primary prevention, screening, or targeted treatment (see below). Each 

month women are at risk for developing ovarian cancer. Over time, the cancer could progress 

through the different stages of ovarian cancer; we assumed that death from ovarian cancer was 

always preceded by diagnosis. Women with cancer could be detected either through a screening 

program or through clinical symptoms and diagnosis. Once detected, women undergo a 

laparotomy, and those who survive undergo treatment for their cancer. Cancer survival is based 

on the stage at diagnosis. 

 Historically, cancer progression has been modeled as a serial progression through clinical 

stages – Stage I is followed by Stage II, Stage II is followed by Stage III, and Stage IV follows 

Stage III. This conceptual model has worked well with cervical cancer, but it is not clear that 

using this overall “model” for ovarian cancer is appropriate for the following reasons: 

• The main purpose of cancer staging is to identify groups of patients who have similar 

prognosis; this allows comparability in comparing treatment results in both prospective 

and retrospective studies. Although the concept that stages also represent biological 

progression is attractive, it is not necessarily true, and, at least in the case of the ovarian 

cancer staging system of the International Federation of Obstetrics and Gynecology 

(FIGO),33 plays no role in the development and validation of a staging system. 

• Cervical cancer is, in many ways, unique among human cancers: it has a single cause 

(persistent infection with certain types of human papilloma virus); exposure to this cause 

in most people occurs within a relatively narrow time frame (roughly ages 15 to 25, the 

times of highest sexual activity with multiple partners); and the most common type of 

cancer is a squamous type, which primarily spreads through direct extension. In contrast, 

the cause or causes of ovarian cancer are unclear, duration of exposure is unclear, and, 

most importantly, the pattern of spread and metastases is quite different. 

• By definition, Stage I ovarian cancer is limited to the ovary, Stage II involves the ovary 

and other organs in the pelvis, and Stage III, the most common stage at diagnosis, 

involves organs in the upper abdomen, including the large and small bowel, the 
27

omentum, the diaphragm, and other peritoneal surfaces. Peritoneal fluid constantly 

circulates, and it is not uncommon for loops of small bowel to come in contact with the 

ovary. In order for the “conceptual model” requiring an intervening Stage II prior to 

development of Stage III to be correct, one has to assume that cancer cells on the surface 

of the ovary must necessarily spread to the uterus or other pelvic organs before they can 

spread to areas in the upper abdomen via transport in peritoneal fluid or via direct contact 

with small bowel. We postulate that a scenario where a certain unknown proportion of 

ovarian cancers progress directly from Stage I to Stage III is at least as plausible a 

scenario. 

Given this uncertainty about the clinical progression of ovarian cancer, we therefore modeled 

the progression under two alternative assumptions: (1) that ovarian cancer needs to progress 

from Stage I to Stage II before progressing to Stage III; and (2) that a proportion of ovarian 

cancer progresses directly from Stage I to Stage III. We evaluated how these two competing 

assumptions about the natural history of ovarian cancer affect the required stage progression and 

mortality rates and the estimated life expectancies of the alternative prevention strategies. 

We assumed that women who have survived their detected cancer for 5 years are to be 

considered disease-free and to have mortality equal to that of the general population. Each 

month, a woman may also choose to have a benign oophorectomy, reducing her risk of ovarian 

cancer. Throughout their lifetimes, all women are at risk for age-specific mortality unrelated to 

ovarian cancer. We also included age-specific rates for bilateral oophorectomy, under the 

assumption that women without ovaries are not at risk for developing ovarian cancer; we did not 

specifically model the possibility of primary peritoneal carcinomatosis in these women. 
28

Figure 3. Schematic representation of Markov model of ovarian cancer 

Arrows in Figure 3 depict possible transitions between states. Note that one version of the 

model allows transition directly from Stage I (confined to the ovaries) to Stage III (metastases to 

the upper abdomen). 

Data sources. We obtained age-specific estimates of ovarian cancer incidence, mortality, 

stage distribution, and survival from the National Cancer Institute’s Surveillance, Epidemiology, 

and End Results (SEER) online database Cancer Query System (http://seer.cancer.gov/canques). 

 Estimates for other-cause mortality were obtained by subtracting age-specific ovarian cancer 

mortality from age-specific all-cause mortality for women, using U.S. lifetables available from 

the National Center for Health Statistics (www.cdc.gov/nchs/deaths.htm). 

 Estimates for age-specific oophorectomy rates were obtained from AHRQ’s Nationwide 

Inpatient Sample, using ICD-9 codes for bilateral oophorectomy, bilateral salpingoophorectomy, 

or removal of remaining ovary or remaining tube and ovary (http://hcup.ahrq.gov/HCUPnet.asp). 

Software. We constructed the model and performed all analyses using DATA Pro 2006 

(Williamstown, MA: TreeAge Software, Inc). 

Prevention strategies. We modeled six clinical strategies of prevention for ovarian cancer 

(Figure 4): 

(1) The baseline strategy of no screening or prevention (NoScreen) where women are 

identified with ovarian cancer only through development of clinical symptoms. 

(2) A primary prevention strategy (PrimaryPrevention) where women undertake a 

hypothetical method of primary prevention which reduces their incidence of ovarian 

cancer. 
29

(3) An interval screening strategy (IntervalScreen) where women are screened at recurrent 

intervals for ovarian cancer using a hypothetical test. Women identified through 

screening could benefit from early treatment. 

(4) A genetic screening strategy where women are tested for a specific genetic mutation and 

if positive undergo primary prevention for ovarian cancer (Genetic&PrimaryPrevention). 

The overall population risk for ovarian cancer is unchanged; we varied incidence in those 

with and without the putative mutation. 

(5) A genetic screening strategy where women are tested for a specific genetic mutation and 

if positive they undergo screening for ovarian cancer at recurrent intervals 

(Genetic&IntervalScreen). 

(6) A strategy where women once identified with ovarian cancer are tested for a hypothetical 

marker which allows targeted treatment for ovarian cancer (TargetTx). Women who are 

positive for the marker and undergo the targeted treatment experience greater survival. 

Figure 4. Schematic representation of ovarian cancer prevention and treatment strategies 

Approach. Because the majority of the literature on genomic testing does not allow 

definitive conclusions about the relative effectiveness of different strategies using different tests, 

we adapted a “generic” approach to comparison of different strategies. 

 We chose as a goal a 20 percent reduction in ovarian cancer death, similar to the reductions 

targed for other cancers in the Healthy People 2010 objectives. With this target, we used the 

calibrated models to explore the following clinical questions: 
30

(1) How effective would a primary prevention intervention need to be to reduce ovarian 

cancer deaths by 20 percent? 

(2) What combinations of test sensitivity and frequency result in at least a 20 percent 

reduction in mortality? 

(3) What combinations of (a) prevalence of a genetic mutation in the population and 

(b) relative risk associated with that mutation would result in the target 20 percent 

reduction in ovarian cancer deaths with either primary prevention (at various levels of 

effectiveness) or interval screening (at varying levels of sensitivity and frequency)? 

(4) How effective would a targeted treatment for ovarian cancer need to be (and in what 

proportion of the patient population would the marker for that treatment need to exist)? 

Note that we assume that targeted therapy would be equally effective across all stages of 

disease. 

(5) How do the test characteristics for targeted treatment or genetic screening affect the 

results? 

(6) How do the above results differ under the assumption that cancer must progress from 

Stage I to II and then III versus that assumption that ovarian cancer may progress directly 

from Stage I to Stage III? 

(7) What effect does the assumption about natural history have on the relative efficacy of 

screening? 

(8) What is the impact of attributable risk proportion on the potential efficacy of genetic risk 

factors? 

Peer Review Process 

We employed internal and external quality-monitoring checks through every phase of the 

study to reduce bias, enhance consistency, and verify accuracy. Examples of internal monitoring 

procedures include: three progressively stricter screening opportunities for each article (abstract 

screening, full-text article review, data abstraction review); involvement of three individuals 

(two clinicians and copy-editor) in each data abstraction; and agreement of at least two clinicians 

on all included studies. 

Our principal external quality-monitoring device is the peer-review process. Nominations for 

peer reviewers were solicited from several sources, including the technical expert panel and 

interested federal agencies. The list of nominees was forwarded to AHRQ for vetting and 

approval. A list of peer reviewers submitting comments on this draft is provided in Appendix E.∗

 

∗ Appendixes cited in this report are provided electronically at http://www.ahrq.gov/clinic/tp/genovctp.htm.
31

Chapter 3. Results 

Question 1: Analytic Validity of Testing 

Question 1 is: What is the evidence that the ovarian cancer genomic tests performed in a 

typical clinical laboratory actually measure what they are purported to measure? 

Approach 

We sought to identify articles that provided details on the performance of genomic tests in a 

laboratory setting, with an emphasis on laboratories providing results for clinical care. Because 

data on sensitivity and specificity are covered under Question 2, our emphasis in this question 

was on evidence related to analytic performance, such as: 

• Test reproducibility, as measured by inter- and intra-assay coefficients of variation for 

quantitative tests, or measurements of observer variability for tests that require human 

observation (such as immunohistochemistry). 

• Measurements of correlation with other tests, including previous generations of other 

tests. 

• Quantification of variability between laboratories. 

• Analytic sensitivity and specificity in comparison to a recognized reference standard. 

 We included only articles that specifically addressed the laboratory performance of genomic 

tests for ovarian cancer. Although specific assays may have documented analytic validity when 

used for other cancers, or other conditions, our focus was on ovarian cancer. 

Results 

 Articles included for Question 1 are summarized in Evidence Table 1 (Appendix D∗). 

Radioimmunoassays for single gene products – cancer antigen 125 (CA-125). We 

identified six articles that compared the performance of a next-generation radioimmunoassay 

(RIA) for CA-125 (CA-125 II) from various manufacturers to earlier generation tests or to other 

RIAs for other tumor markers.34-39 All six studies reported high correlation coefficients with 

previous assays. All studies reported low inter- and intra-assay coefficients of variation (values 

generally less than 10 percent for inter-assay, less than 5 percent for intra-assay). Of note, two 

studies examined coefficients of variation at different levels of CA-125 and found changing 

variability with CA-125 levels. Fillela et al.,38 using an automated analyzer, found coefficients 

of variation of 2.8 to 6.4 percent for “level 2” values of CA-125 (mean 47.1 U/mL), with values 

 

∗ Appendixes cited in this report are provided electronically at http://www.ahrq.gov/clinic/tp/genovctp.htm.
32

of 1.8 to 4 percent for “level 3” (mean 164.3 U/mL). Hubl et al.34 reported slightly higher intraassay coefficients of variation in mid-range (40 U/mL) compared to low-range values (10 to 20 

U/mL). A third study39 did not find an effect of concentration in the clinically relevant range. 

Because 35 U/mL is the most commonly used threshold for considering a CA-125 value 

suspicious for cancer, these results suggest that random variation in test results may have some 

impact on sensitivity and specificity at values close to the threshold. The clinical impact of this 

variability would ultimately depend on how values close to the threshold are managed. 

 Tuxen and colleagues performed serial measurements of CA-125 over the course of a year in 

26 women with known ovarian cancer40 and 31 healthy controls41 to assess the relative effect of 

analytic variability and inter- and intra-individual biologic variation on CA-125 levels. In 

women with cancer, analytic imprecision accounted for 12 percent of the variability in levels, 

intra-individual variations 24.0 percent, and inter-individual variations 43.6 percent; after 

accounting for this imprecision, the investigators estimated that a change of greater than 62.6 

percent in the reference value would be needed in order to be statistically significant. Similar 

values were found in healthy controls, with imprecision being greater in premenopausal women 

(69.5 percent) compared with postmenopausal women (35.7 percent) due to variability in levels 

over the course of the menstrual cycle. The change in reference value required for significance 

after accounting for variation in the entire group was 50 percent. 

 One study37 compared sensitivity and specificity of the new generation and first generation 

assays using 138 stored samples and found slightly higher sensitivity with the new assay (89.8 

vs. 84.7 percent), and lower specificity (83.5 vs. 84.7 percent). However, the prevalence of 

cancer in the samples was much higher than would be expected in a typical clinical population, 

and the confidence intervals for the sensitivity and specificity estimates overlapped. 

Radioimmunoassays for other single gene products. Few studies of other markers were 

performed in clinical laboratories. Hasholzner and colleagues36 evaluated clinical laboratory 

performance of an RIA for cancer antigen 72-4 (CA-72-4); intra-assay coefficients of variation 

were 3.5 to 4 percent, and inter-assay coefficients of variation were 5 to 7.4 percent. Correlation 

between CA-125 levels and CA-72-4 levels was good for healthy controls (-0.066), but poor for 

serous ovarian cancer patients (0.576). 

 Tuxen and colleagues41 measured carcinoembryonic antigen (CEA) and tissue plasminogen 

activator (TPA) along with CA-125 in healthy controls in the study described above. For CEA, 

the change in reference values needed for significance after accounting for imprecision was 44.8 

percent; for TPA, the value was 67.9 percent. Unlike with CA-125, menopausal status did not 

affect the degree of intra-individual variability. 

 Two studies reported research laboratory performance of two other single gene products and 

preliminary clinical validation. Riisbro and colleagues42 reported an inter-assay coefficient of 

variation of 7.6 percent, and an intra-assay coefficient of variation of 4.6 percent for an RIA for 

soluble urokinase plasminogen activator receptor. Using 129 stored serum samples, levels were 

correlated with malignancy and stage of disease, but not after adjusting for other variables. 

Thougaard and colleagues43 compared three different antibodies targeted against tetranectin, with 

similar performance in terms of assay variability, differences in absolute levels of 10 percent or 

less, and similar correlations in ovarian cancer patients (n = 43); levels were observed to 

decrease as cancer stage worsened. 

 Two studies44,45 reported on the performance of RIAs developed after identification of 

candidate single gene products identified after using microarrays to identify overexpressed 

genes. These studies will be discussed below under “Microarrays.” 
33

Other assays. Sapi and colleagues46 reported a method for removing peripheral 

lymphocytes from blood samples or ascitic fluid in order to measure telomerase activity. After 

this method, telomerase activity was observed in 8 of 8 patients with Stage IV ovarian cancer, 7 

of 20 patients with Stage III, and 0 of 30 controls. CA-125 levels were higher in patients with 

positive telomerase assays. 

Single gene mutation/polymorphism. Janatova and colleagues47 evaluated the performance 

of Spreadex Polymer NAB (electrophoresis gels) in patients with known breast cancer gene 1/2 

(BRCA1/2) mutations (n = 13) and 13 controls; the technique successfully identified mutations 

only in those subjects with known mutations; all patients with known mutations had mutations 

detected. 

 Wen and colleagues48 compared microarray with gel-based DNA sequencing for identifying 

mutations in the p53 gene in 108 patients with ovarian cancer. Mutations were detected by both 

methods in 57 cancers, and no mutations by both methods, for a concordance of 81 percent. 

Microarrays. Only one study specifically examined test performance in a clinical 

laboratory. Zarrinkar et al.49 compared high-throughput microarray using parallel analysis to 

single sample assays in specimens from 31 patients with known ovarian cancer and found a high 

level of correlation (0.980). 

 Two studies reported preliminary data from research laboratories on candidate single-gene 

products identified initially through microarray studies.44,45 Hellstrom and colleagues45

compared the performance of an antibody to human epididymis protein 4 (HE4) to CA-125 in 

121 subjects, of whom 37 (30.6 percent) had ovarian cancer. Reported sensitivities for HE4 

were better for HE4 then for CA-125 at a fixed specificity of 96 percent, but confidence intervals 

were quite wide. Mok and colleagues44 examined the performance of prostatin in 201 subjects, 

64 (31.8 percent) of whom had ovarian cancer. Prostatin levels correlated poorly with CA-125 

levels; sensitivity of prostatin was less than that of CA-125 at the same specificity of 94 percent, 

but the combination of the two markers had a sensitivity of 92 percent at the same level of 

specificity. 

Proteomics. Although we identified 10 studies that looked at protein expression in serum as 

a potential biomarker for ovarian cancer,50-59 all were performed in research laboratories. 

Because several of these studies have attracted wide attention in the media, we will discuss them 

in more detail here. 

Petricoin et al.,57 created a proteomics-based genetic algorithm with cluster analysis to 

distinguish between ovarian cancer and non-ovarian cancer serum samples using a training set of 

50 ovarian cancers and 50 healthy controls from a high-risk population. The new algorithm was 

then tested using a validation set consisting of 50 ovarian cancers and 66 non-cancers, some with 

benign ovarian cysts, benign gynecologic disease, or benign non-gynecologic disease. The 

algorithm successfully classified 50/50 cancers (sensitivity = 100 percent) and 63/66 non-cancers 

(specificity = 95 percent) in the validation set. The study has two major limitations. First, the 

proteins used to distinguish cancers from non-cancers were not identified, leading to questions of 

whether proteins of interest were actually produced by tumor cells or by other inflammatory 

responses in the tumor’s microenvironment. Although the reported positive predictive value is 

94 percent in the study, the low prevalence of ovarian cancer (1 in 2,500) in the general 

population would reduce the positive predictive value of proteomic screening to less than one 

percent in a screening population. 

 These investigators subsequently published three datasets online as the Clinical Proteomics 

Program Databank (http://home.ccr.cancer.gov/ncifdaproteomics/ppatterns.asp). The first 
34

dataset (2-16-02) consists of 100 control, 100 ovarian cancer, and 16 benign disease samples run 

on a Ciphergen H4 ProteinChip array. Ovarian Dataset 4-3-02 consists of the same samples run 

on Ciphergen WCX2 ProteinChip array. Ovarian Dataset 8-7-02 contains serum profiles run on 

Ciphergen WCX2 ProteinChip array of 162 ovarian cancer patients subdivided into stages and 

91 non-cancer control subjects. 

 Sorace et al.55 analyzed Ovarian Dataset 8-7-02 using a training set containing 45 controls 

and 80 cancers. A 2-sided Wilcoxon test was used to compare intensity between controls and 

cancers at different mass-to-charge (M/Z) values. A subset of M/Z values that resulted in the 

lowest Wilcoxon p-values was selected, and stepwise discriminant analysis was used to 

determine the subset of M/Z values that best discriminated cancers from controls. Classification 

rules were then used on the remainder of the patient data (test set). Three classification rules 

were developed, all with sensitivity > 90 percent and specificity > 90 percent when applied to the 

test set. The authors expressed concerns over the existence of highest discriminatory ability in 

the M/Z < 500 range, where data are traditionally discarded due to increased “noise.” They 

hypothesized several explanations for these findings including very low molecular weight (MW) 

biomarkers such as LPA, low MW degradation products of higher MW macromolecules, and 

systematic processing error. 

 Li et al.59 analyzed all three Clinical Proteomics Program Datasets using two different 

approaches: support vector machine statistical testing (SVM-ST) and support vector machine 

with genetic algorithm (SVM-GA). Datasets were not split into training and validation sets; 

instead, a leave-out-one cross validation was used. Sensitivity and specificity for analysis of 

Dataset 2-16-02 were lower than in the analysis by Petricoin et al.57 of the same data (0.79 and 

0.80 for SVM-ST, and 0.96 and 0.948 for SVM-GA, respectively). Sensitivity and specificity 

were improved with analysis of the other two datasets, achieving 100 percent sensitivity and 100 

percent specificity using SVM-GA to analyze Dataset 8-7-02. The authors were unable to 

reproduce the sensitivity and specificity reported by Petricoin et al. when training an SVM with 

the discriminatory features identified in the latters’ paper. 

 Zhang et al.53 performed a multicenter study to analyze serum proteomic expression profiles 

using Ciphergen ProteinChip in 153 patients with epithelial ovarian cancers, 42 with other 

ovarian cancers, 166 with benign pelvic masses, and 142 healthy controls. Results were crossvalidated against different subsets of the data to identify biomarkers. Three biomarkers 

(apolipoprotein A1, transthyretin, both down-regulated in ovarian cancers, and a fragment of 

human inter-alpha trypsin inhibitor, upregulated) were identified and immunoassays performed 

on serum from another subset of patients. Levels of these three biomarkers were included in a 

multivariate model to predict malignancy, and the model was tested on a validation set consisting 

of 138 ovarian cancers and 63 healthy controls. The resulting model had a sensitivity and 

specificity of 0.775 and 0.968, respectively, in distinguishing cancer from healthy controls. The 

authors also created a model incorporating CA-125 with the three markers, which improved on 

the specificity of CA-125 alone. The discovered biomarkers were all acute phase reactants 

deemed unlikely to be released by tumor cells. Controls were not age-matched and were 

significantly younger (median test and validation sets 39 and 44) compared to cancer patients 

(median 52 and 57). 

Kozak et al.60 analyzed serum from 109 ovarian cancers, 19 patients with benign disease, and 

56 healthy donors using the Ciphergen ProteinChip SAX2. Samples were divided into training 

and test sets. Proteins differentially expressed were identified using t-test and Wilcoxon rank 

sum tests. Three biomarker protein panels were then developed: SBP (five markers), VBP I 
35

(five markers), and VBP II (four markers). Multivariate logistic regression was used to develop 

panels with the best predictive value. Sensitivity and specificity were 0.957 and 0.826, 

respectively, for SBP; 0.815 and 0.949 for VBP I; and 0.728 and 0.949 for VBP II. Test sets 

were employed. Panels correctly identified early stage disease with variable sensitivity. 

Individual discriminatory proteins were not identified. 

Although all studies reported good discrimination for the particular protein profile studied, 

there were several recurrent issues that limit the ability to draw inferences about potential clinical 

applicability: 

• Technical issues with the assay. For example, Conrads and colleagues58 noted that 

“comparisons...revealed that the variation in mass spectra (overall amplitude, total record 

count and deviation between ovarian cancer cases and control samples) was statistically 

indistinguishable from the variance within the process itself, as indicated by the serum 

reference standard.” Sorace and Zahn,55 in an analysis of a dataset used by several other 

groups, found sensitivity and specificity of 100 percent in a training set, but noted that 

much of the discrimination of the profile lies in the region of the spectroscopy results 

with low mass-to-charge ratios. They note that this region is problematic both because of 

technical issues of measurement and because differences in protein profiles in this region 

may result from processes independent of cancer. 

• Varying analytic methods. No consistent methodology was used. Given the complexity 

of the data and the variety of methods used, it is difficult to draw consistent conclusions 

about performance. Li and colleagues59 found marked variability in results using similar 

statistical methods on different datasets, as well as using different statistical methods on 

the same dataset. 

• Unrealistically high prevalence of ovarian cancer. The majority of the studies compared 

serum samples from known ovarian cancer patients to healthy controls, using relatively 

small datasets of 100 to 200 subjects, with a prevalence of cancer of 30 to 50 percent. 

Although repeated sampling and resampling was performed in all of these studies, the 

prevalence of cancer was still substantially higher than it would be in a screening 

population (approximately 0.05 percent). Only one study59 provided estimates for the 

positive predictive value within a screening population; these estimates were in general at 

least an order of magnitude lower than the results based on the original dataset. 

Discussion 

The majority of the literature we identified that specifically addressed issues of clinical 

laboratory performance in ovarian cancer dealt with radioimmunoassays of single gene products, 

with CA-125 being the most common product. Test reproducibility and validity is in general 

quite good for these assays, although a series of Danish studies by Tuxen and colleagues 

suggests that both inherent laboratory variation and biological variation should be considered 

when considering thresholds for determining clinically relevant changes in concentrations of 

these markers. In addition, coefficients of variation for CA-125 are generally greatest when 

levels are in the range of the most commonly used discriminatory threshold of 35 U/mL, 
36

suggesting that this irreducible imprecision may have some impact on sensitivity and specificity 

in practice. 

 We did not identify any relevant literature on the clinical laboratory performance of other 

types of genomic tests. Although there were numerous articles describing research laboratory 

performance, the relevance of these studies to widespread clinical practice is uncertain. In 

particular, the prevalence of ovarian cancer in studies of potential proteomic patterns as 

predictors of early stage ovarian cancer is at least an order of magnitude higher than the likely 

prevalence in the general population. 

Summary 

The published data on clinical laboratory performance suggests that currently available 

radioimmunoassays for single gene products have acceptable reproducibility and reliability, 

although even this level of variability may have some impact on clinical interpretation of results, 

especially when comparing relatively small serial changes, or levels close to the discriminatory 

threshold. 

 There is insufficient evidence to estimate how newer technologies such as microarrays or 

protein profiles would perform in a “typical clinical laboratory.” 

Question 2: Sensitivity and Specificity of Tests 

Question 2 is: What is the sensitivity and specificity of genomic tests in detecting ovarian 

cancer in asymptomatic and symptomatic women, including high-risk women? 

Approach 

We sought to identify articles that provided details on the sensitivity and specificity of 

genomic tests in a clinical setting. We separately reviewed studies intended for screening 

purposes, both in the general population and in women identified as high risk based on family 

history and/or BRCA testing, and studies used for diagnostic purposes, either in women with 

symptoms or women with a diagnosed mass. 

Other Evidence Reports 

Asymptomatic women – average risk. The systematic review conducted for the U.S. 

Preventive Services Task Force (USPSTF)30 concluded that annual CA-125 screening had an 

estimated sensitivity of 80 percent, with false positive rates of 0.1 to 0.6 percent, based on three 

studies with small numbers of cancers and variable, relatively short, followup durations; the 

estimated positive predictive value for screening was 1 percent for women called for additional 

testing, and 15 percent for women undergoing surgery. 

 The evidence report on management of adnexal masses14 found that the majority of studies 

did not report results separately for women with asymptomatic masses compared with those who 

had masses detected because of symptoms. Of note, the report also found an extremely low 
37

sensitivity (less than 50 percent) of the bimanual pelvic examination as both a screening test and 

an initial diagnostic test. 

Asymptomatic women – high risk. The BRCA1 and BRCA2 systematic review for the 

USPSTF31 did not specifically address the sensitivity and specificity of genomic tests in this 

setting. 

Symptomatic women – average risk. Again, the literature on the use of diagnostic tests, 

including genomic tests, does not provide useful information on differences in test performance 

in symptomatic versus asymptomatic women. The evidence report on management of adnexal 

masses14 found an approximate sensitivity of 78 percent for CA-125 in the diagnosis of cancer in 

adnexal masses, with an approximate specificity of 78 percent; both sensitivity and specificity 

were higher in postmenopausal women. Other genomic tests (all single gene products) reviewed 

included TAG-92, cancer antigen 19.9 (CA-19.9), and CEA; all had sensitivities lower than the 

pooled estimates for CA-125. There were few studies examining combination testing; those that 

did failed to find improved discrimination compared to CA-125 alone. 

Symptomatic women – high risk. The adnexal mass evidence report14 did not identify any 

studies uniquely in high-risk populations. 

Results 

 Articles included for Question 2 are summarized in Evidence Table 2 (Appendix D∗). 

Asymptomatic women. We did not identify any studies of genomic tests other than CA-125 

that provided evidence of sensitivity and specificity as primary screening tests for ovarian cancer 

in asymptomatic women. The one major study published subsequent to the USPSTF review 

reported the initial baseline results of the National Cancer Institute Prostate, Lung, Colon, and 

Ovarian (PLCO) screening trial.61 In this study, over 28,000 women aged 55 or older were 

screened with transvaginal ultrasound and CA-125; 402 women (1.4 percent) had an abnormal 

CA-125. Of the 19 invasive cancers, four had normal CA-125 levels for a sensitivity of 78.9 

percent (95 percent confidence interval [CI], 60.6 to 97.3 percent), and a specificity of 98.7 

percent (95 percent CI, 98.5 to 98.8 percent), consistent with previous studies in postmenopausal 

women. 

 Only one study62 provided sufficient detail about patient characteristics to be able to 

ascertain test performance of a genomic test (in this case, vascular endothelial growth factor 

[VEGF]) as a diagnostic tool in asymptomatic women identified with a pelvic mass through 

screening; sensitivity was 55.9 percent, and specificity 55.3 percent, too low to be considered 

useful as a second line diagnostic test. All of the other studies that included women with a pelvic 

mass failed to report the proportion of women with a mass who had presented on the basis of 

symptoms, or on the basis of asymptomatic detection of a mass through a pelvic examination or 

imaging study; this limitation is shared by the majority of the literature on diagnosis of ovarian 

cancer in women with masses.14

Symptomatic women – single gene products. The majority of studies identified were 

retrospective studies that compared serum or, in some cases, tissue from women with known 

ovarian cancer to serum from women with benign adnexal masses and/or asymptomatic women. 

There were more than two studies identified for only two markers, CA-72-4 and VEGF.

 

∗ Appendixes cited in this report are provided electronically at http://www.ahrq.gov/clinic/tp/genovctp.htm.
38

 Table 6 shows results for CA-72-4. In general, sensitivity is poor. Although specificity is 

better (85 percent or higher), the high positive predictive values observed in these studies are a 

reflection of the high prevalence of cancer in the study populations. In a screening setting, where 

prevalence is likely to be less than one percent, positive predictive values would be much lower. 

 Table 7 shows results for VEGF. Although sensitivity was in general somewhat higher than 

for CA-72-4, specificity was somewhat lower. 

 Table 8 shows results for other single gene products. In general, there is a trade-off between 

sensitivity and specificity. Common limitations of these studies included failure to adequately 

characterize the study population (such as underlying risk factors, menopausal status, and how 

the patients presented to the health system); small numbers (as reflected in the wide confidence 

intervals of the sensitivity and specificity estimates); and prevalence of ovarian cancer much 

higher than would be expected in many clinical settings, especially screening settings. These 

limitations preclude meaningful synthesis or direct comparisons between tests. 
39

Table 6. Studies of cancer antigen 72-4 (CA-72-4) 

Study Gene 

product 

TP FN FP TN Sensitivity 

(95% CI) 

Specificity 

(95% CI) 

PPV 

(95% CI) 

N

(95

Wakahara et al. 200163 CA-72-4 20 20 18 108 50.0% 

(34.5 to 65.5%) 

85.7% 

(79.6 to 91.8%) 

52.6% 

(36.8 to 68.5%) 

8

(78.1 

Schutter et al., 199864 CA-72-4 28 15 6 86 65.0% 

(50.7 to 79.3%) 

93.0% 

(87.8 to 98.2%) 

82.4% 

(69.5 to 95.2%) 

8

(78.2 

Fayed et al., 199865 CA-72-4 21 9 3 57 70.0% 

(53.6 to 86.4%) 

95.0% 

(89.5 to 100%) 

87.5% 

(74.3 to 100%) 

8

(78.1 

Zakrzewska et al., 199966

CA-72-4 39 31 0 26 55.7% 

(44.1 to 67.4%) 

100% 

(88.5 to 100%) 

100% 

(92.3 to 100%) 

4

(32.7 

Hasholzner et al., 199636

CA-72-4 (benign vs. cancer) 

66 57 1 36 54.0% 

(45.2 to 62.8%) 

97.0% 

(91.5 to 100%) 

98.5% 

(95.6 to 100%) 

3

(28.8 

Hasholzner et al., 199636

CA-72-4 (healthy vs. cancer) 

66 57 1 29 54.0% 

(45.2 to 62.8%) 

97.0% 

(90.9 to 100%) 

98.5% 

(95.6 to 100%) 

3

(23.7 

Abbreviations: CI = confidence interval; FN = false negative; FP = false positive; NPV = negative predictive value; PPV = positive predic

negative; TP = true positive 
40

Table 7. Studies of vascular endothelial growth factor (VEGF) 

Study Gene product TP FN FP TN Sensitivity 

(95% CI) 

Specificity 

(95% CI) 

PPV 

(95% CI) 

N

(95

Tanir et al., 200367 VEGF 11 1 6 44 91.7% 

(76.0 to 100%) 

88.0% 

(79.0 to 97.0%) 

64.7% 

(42.0 to 87.4%) 

9

(93.5 

Gorelik et al., 200568 VEGF 35 9 27 55 79.5% 

(67.6 to 91.4%) 

67.4% 

(57.3 to 77.5%) 

56.5% 

(44.1 to 68.8%) 

8

(77.4 

Obermair et al., 199869

VEGF 24 20 19 62 54.5% 

(39.8 to 69.3%) 

76.5% 

(67.3 to 85.8%) 

55.8% 

(41.0 to 70.7%) 

7

(66.3 

Cooper et al., 200270 VEGF 75 26 16 34 74.0% 

(65.4 to 82.6%) 

68.0% 

(55.1 to 80.9%) 

82.4% 

(74.6 to 90.2%) 

5

(44.1 

Oehler and Caffier, 199971 VEGF (benign mass controls) 29 12 7 13 70.7% (56.8 to 84.7%) 65.0% (44.1 to 85.9%) 80.6% (67.6 to 93.5%) 5

(32.4 

Oehler and Caffier, 199971 VEGF (healthy controls) 30 11 6 14 73.2% (59.6 to 86.7%) 70.0% (49.9 to 90.1%) 83.3% (71.2 to 95.5%) 5

(36.5 

Abbreviations: CI = confidence interval; FN = false negative; FP = false positive; NPV = negative predictive value; PPV = positive predic

negative; TP = true positive 
41

Table 8. Studies of other single gene products 

Study Gene product TP FN FP TN Sensitivity 

(95% CI) 

Specificity 

(95% CI) 

PPV 

(95% CI) 

(9

Abdel-Aleem et al., 199672 Alpha-L- fucosidase 43 5 0 28 89.6% (80.9 to 98.2%) 100% (89.3 to 100%) 100% (93.0 to 100%) 

(72.6 

Cherchi et al., 200273

CA-15-3 10 10 6 38 50.0% 

(28.1 to 71.9%) 

86.4% 

(76.2 to 96.5) 

62.5% 

(38.8% to 86.2%) 

(67.7 

Cherchi et al., 200273

CA-19-9 13 7 2 42 65.0% 

(44.1 to 85.9%) 

95.5% 

(89.3 to 100%) 

86.7% 

(69.5 to 100%) 

(75.9 

Wakahara et al., 200163

CA-19-9 24 42 78 127 36.4% 

(24.8 to 48.0%) 

62.0% 

(55.3 to 68.6%) 

23.5% 

(15.3 to 31.8%) 

(68.6 

Cherich et al., 200273

CEA 8 12 0 44 40.0% 

(18.5 to 61.5%) 

100% 

(93.2 to 100%) 

100% 

(62.5 to 100%) 

(67.8 

Zakrzewska et al., 199966

CEA 7 63 0 26 10.0% 

(3.0 to 17.0%) 

100% 

(88.5 to 100%) 

100% 

(57.1 to 100%) 

(19.8 

Mabrouk and AliLabib, 200374 c-erb-2 4 16 4 16 20.0% (2.5 to 37.5%) 80.0% (62.5 to 97.5%) 50.0% (15.4 to 84.6%) 

(32.7 

Inaba et al., 199575

CYFRA 21-1 48 27 3 137 64.0% 

(53.1 to 74.9%) 

97.9% 

(95.5 to 100%) 

94.1% 

(87.7 to 100%) 

(77.9 

Tempfer et al., 199876

CYFRA 21-1 15 22 2 38 40.5% 

(24.7 to 56.4%) 

95.0% 

(88.2 to 100%) 

88.2% 

(72.9 to 100%) 

(51.1 

Gorelik et al., 200568

EGF 37 7 19 63 84.1% 

(73.3% to 94.9%) 

76.7% 

(67.5 to 85.9%) 

66.1% 

(53.7 to 78.5%) 

(83.0 

Kim et al., 200377 Epithelial cell 

adhesion molecule 

22 30 2 50 42.3% 

(28.9 to 55.7%) 

96.2% 

(90.9 to 100%) 

91.7% 

(80.6 to 100%) 

(51.9 

Hefler et al., 200078

FAS 28 24 3 62 53.0% 

(39.4 to 66.6%) 

95.0% 

(89.7 to 100%) 

90.3% 

(79.9 to 100%) 

(62.6 

Gorelik et al., 200568

G-CSF 32 12 21 61 72.7% 

(59.5 to 85.9%) 

74.4% 

(65.0 to 83.8%) 

60.4% 

(47.2 to 73.5%) 

(75.1 

Diamandis et al., 200379

hK6 69 77 12 226 47.0% 

(38.9 to 55.1%) 

95.0% 

(92.2 to 97.8%) 

85.2% 

(77.4 to 92.9%) 

(69.7 
42

Table 8. Studies of other single gene products (continued) 

Study Gene product TP FN FP TN Sensitivity 

(95% CI) 

Specificity 

(95% CI) 

PPV 

(95% CI) 

(9

Luo et al., 200180 hK10 62 18 0 42 77.5% 

(68.3 to 86.7%) 

100% 

(92.9 to 100%) 

100% 

(95.2 to 100%) 

(58.4 

Berek et al., 199181

IL-6 18 18 2 10 50.0% 

 (33.7 to 66.3%) 

83.3% 

(62.2 to 100%) 

90% 

(76.9 to 100%) 

(18.0 

Gorelik et al., 200568

IL-6 37 7 11 71 84.1% 

(73.3% to 94.9%) 

86.0% 

(78.5 to 93.5.%) 

77.1% 

(65.2 to 89.0%) 

(84.7 

Gorelik et al., 200568

IL-8 39 5 25 57 88.6% 

(79.2 to 98.0%) 

69.8% 

(59.9 to 79.7%) 

60.9% 

(49.0 to 72.9%) 

(85.2 

Gorelik et al., 200568

MCP 37 7 23 59 84.1% 

(73.3% to 94.9%) 

72.1% 

(62.4 to 81.8%) 

61.7% 

(49.4 to 74.0%) 

(82.0 

van Haaften-Day et al., 200182

M-CSF 69 134 28 166 34.0% 

(27.5 to 40.0%) 

85.6% 

(80.6 to 90.5%) 

71.1% 

(62.1 to 80.2%) 

(49.7 

Bon et al., 199683 Mucin-like 

carcinomaassociated antigen 

29 47 0 70 38.2% 

 (27.2 to 49.1%) 

100% 

(95.7 to 100%) 

100% 

(89.7 to 100%) 

(50.9 

van Haaften-Day et al., 200182

OVX1 38 165 16 178 18.7% 

(13.4 to 24.1%) 

91.8% 

(87.9 to 95.6%) 

70.4% 

(58.2 to 82.5%) 

(46.6 

Onsrud et al., 199684 p55 26 19 3 24 57.8% (43.3 to 72.2%) 88.9% (77.0 to 100%) 89.7% (78.6 to 100%) 

(41.0 

Opala et al., 200585 p55 28 23 1 15 54.9% (41.2 to 68.6%) 93.8% (81.9 to 100%) 96.6% (89.9 to 100%) 

(23.9 

Onsrud et al., 199684 p75 7 38 1 26 15.6% (5.0 to 26.1%) 96.3% (89.2 to 100%) 87.5% (64.6 to 100%) 

(28.6 

Opala et al., 200585 p75 22 29 3 13 43.1% (29.5 to 56.7%) 81.3% (62.1 to 100%) 88.0% (75.3 to 100%) 

(17.0 

Tsukishiro et al., 2005 86 Secretory leukocyte 

protease inhibitor 

42 13 5 20 76.0% 

(64.7 to 87.3%) 

80.0% 

(64.3 to 95.7%) 

89.4% 

(80.5 to 98.2%) 

(43.9 
43

Table 8. Studies of other single gene products (continued) 

Study Gene product TP FN FP TN Sensitivity 

(95% CI) 

Specificity 

(95% CI) 

PPV 

(95% CI) 

(9

Darai et al., 199887

Serum cadherin 11 5 0 52 68.8% 

(46.0 to 91.5%) 

100% 

(94.2 to 100%) 

100% 

(72.7 to 100%) 

(83.9 

Baron et al., 200388

Serum EGFR 125 100 8 136 55.6% 

(49.1 to 62.0%) 

94.4% 

(90.7 to 98.2%) 

94.0% 

(89.9 to 98.0%) 

(51.3 

Baron et al., 200589

Serum EGFR 141 84 86 160 62.7% 

(56.3 to 69.0%) 

65.0% 

(59.1 to 71.0%) 

62.1% 

(55.8 to 68.4%) 

(59.6 

Udagawa et al., 199890

Serum GAT 68 68 13 285 50.0% 

(41.6 to 58.4%) 

95.6% 

(93.3 to 97.9%) 

84.0% 

(76.0 to 91.9%) 

(76.6 

Sedlaczek et al., 200291

sIL to 2R 54 13 1 31 80.6% 

(71.1 to 90.1%) 

96.9% 

(90.8 to 100%) 

98.2% 

(94.7 to 100%) 

(57.0 

Hurteau et al., 199592

Soluble IL-2 receptor alpha 

37 2 58 3 94.9% 

(87.9 to 100%) 

4.9% 

(0 to 10.3%) 

38.9% 

(29.1 to 48.8%) 

(17.

Opala et al., 200393

Soluble intracellular adhesion molecule 1 

42 9 7 9 82.4% 

(71.9 to 92.8%) 

56.3% 

(31.9 to 80.6%) 

85.7% 

(75.9 to 95.5%) 

(26.9 

McIntosh et al., 200494

Soluble mesothelinrelated marker (benign masses) 

15 37 4 216 28.8% 

(16.5 to 41.2%) 

98.2% 

(96.4 to 99.9%) 

78.9% 

(60.6 to 97.3%) 

(81.0 

Sedlaczek et al., 200291

TPS 53 14 6 26 79.1% 

(69.4 to 88.8%) 

81.3% 

(67.7 to 94.8%) 

89.8% 

(82.1 to 97.5%) 

(50.2 

Medl et al., 199595

TATI 75 40 67 200 65.2% 

(56.5 to 73.9%) 

74.9% 

(69.7 to 80.1%) 

52.8% 

(44.6 to 61.0%) 

(78.6 

Peters-Engl et al., 199596

TATI 114 66 60 154 63.3% 

(56.3 to 70.4%) 

72.0% 

(65.9 to 78.0%) 

65.5% 

(58.5 to 72.6%) 

(63.9 

Schutter et al., 199997 Urinary gonadotropin 

peptide 

7 2 7 14 78.0% 

(50.9 to 100%) 

65.0% 

(44.6 to 85.4%) 

50.0% 

(23.8 to 76.2%) 

(71.3 

Abbreviations: CI = confidence interval; FN = false negative; FP = false positive; NPV = negative predictive value; PPV = positive predic

negative; TP = true positive 
44

Symptomatic women – DNA variations. We did not identify any studies that allowed 

estimation of sensitivity and specificity of inherited or acquired mutations in detecting ovarian 

cancer. 

Symptomatic women – gene expression. We identified one study that reported on the 

sensitivity of cytological tests of ascitic fluid for the presence of a series of genes believed to be 

activated in ovarian cancer;98 although specificities were universally high, sensitivities ranged 

from 8 to 60 percent, with wide confidence intervals for both values. These low sensitivities 

were even more striking given the high prevalence of ovarian cancer in the samples (61 percent). 

 We identified one study that used immunohistochemistry for a range of gene products in the 

diagnosis of ovarian cancer in ovarian tissue;99 performance for different markers ranged widely. 

Because there were only 20 ovarian cancer patients (out of a total of 70), confidence intervals 

were wide. In addition, no data were provided on the reproducibility of the assay or 

interpretation of results. 

 Another study measured cytokeratin 19 (CK19) expression in peripheral blood mononuclear 

cells;100 false-positive rates were quite high in both discriminating cancer from benign ovarian 

tumors (specificity 28.6 percent) and cancer from normal controls (specificity 40.0 percent). 

Again, confidence intervals were very wide. 

 No other studies directly reported the sensitivity and specificity of gene expression patterns 

identified through the use of microarray technology. 

Symptomatic women – proteomics. Studies of protein profiles as a potential tool for early 

diagnosis of ovarian cancer have attracted considerable attention recently. However, all of the 

identified studies examined test performance using databases; none have been tested in a clinical 

population.

Discussion 

In general, single gene products other than CA-125 have not been shown to be useful in the 

diagnosis of ovarian cancer, either in symptomatic or asymptomatic women. Small sample sizes, 

lack of detail on the prediagnosis history of patients, and an unrealistically high prevalence of 

ovarian cancer in the majority of studies make it difficult to assess how any of these tests would 

perform in clinical practice. 

 Estimating the clinical value of more complex tests (those using multiple gene and/or protein 

markers) is even more difficult. Studies of protein expression, in particular, are limited by lack 

of consensus on appropriate statistical methods, small sample sizes with substantially higher 

prevalences of ovarian cancer than would be found in the general population, lack of 

reproducibility, and uncertainty about the specificity of the biological processes resulting in the 

observed protein patterns. Most importantly, none have been tested in clinical populations. 

Question 3: Impact on Clinical Management of 

Asymptomatic Patients 

Question 3 is: What is the evidence that genomic testing to detect ovarian cancer in 

asymptomatic women, including high-risk women, changes clinical management and leads to 

improved health outcomes? 
45

Approach 

We searched for articles related to the use of genomic tests in screening for ovarian cancer in 

asymptomatic women, including any studies that focused on screening in women previously 

identified as being at greater risk on the basis of family history or other genomic tests. We 

excluded studies of CA-125 that were previously reviewed in a report for the USPSTF;30 we did, 

however, review studies published subsequent to the USPSTF review. 

Other Evidence Reports 

 In the review of ovarian cancer screening for the USPSTF,30 studies did consistently show a 

greater prevalence of Stage I ovarian cancer among women screened with CA-125 (based on 

small numbers of cancers), but there were no data on the impact of screening on mortality. 

 In the BRCA1 and BRCA2 review,31 there were “limited” data on the efficacy of intensive 

screening among carriers, and no prospective studies of chemoprevention (especially oral 

contraceptives) or tubal ligation, although some suggestion from observational studies that both 

of those interventions might reduce ovarian cancer risk. In three retrospective studies and one 

cohort study, prophylactic oophorectomy reduced the risk of ovarian cancer by 85 to 100 

percent, although the authors of the review noted that the confidence interval for risk reduction 

crossed 1.0 in the prospective study. 

Results 

We did not identify any studies of the use of genomic tests for screening asymptomatic 

women in any risk group that met our inclusion criteria. 

Discussion 

To date, no test has been shown to have acceptable sensitivity and specificity for screening 

for ovarian cancer, or to reduce the morbidity and mortality associated with ovarian cancer. 

Because definitive diagnosis of ovarian cancer requires surgery, a high level of specificity is 

needed in order to minimize the costs and potential complications of unnecessary surgery; 

although screening in high-risk groups could theoretically have better outcomes (because a 

higher pretest probability of cancer should result in better positive predictive values), this has not 

been demonstrated in adequately designed studies. One study of 1,610 women at increased risk 

because of family history found a positive predictive value for ovarian cancer of less than five 

percent (3 of 61 abnormal tests),101 a value similar to that observed in the 28,000 average-risk 

women in the PLCO study (3.7 percent).61 The ability to detect early cancer in these 

populations, even with intensive screening, may be limited; a study of 291 high-risk women who 

were screened every 6 months with ultrasound and CA-125 detected early stage ovarian cancer 

in only one of the eight women who developed ovarian or peritoneal cancer during 10 years of 

followup; five of the eight had had normal screening tests within 6 months of diagnosis.18 Other 

studies in similar populations have reported similar findings.16,19 The degree to which the lack of 

effectiveness of screening is due to insufficient test sensitivity rather than the inherent biology of 

ovarian cancer is discussed further in the section on modeling. 
46

Summary 

We found no articles on the use of genomic tests (other than CA-125) for detecting ovarian 

cancer in asymptomatic women, regardless of risk group. 

Question 4: Impact on Clinical Management of 

Diagnosed Patients 

Question 4 is: What is the evidence that genomic testing in women with clinical suspicion of 

ovarian cancer or with already-diagnosed ovarian cancer changes clinical management and leads 

to improved health outcomes? 

Approach 

Studies included in this section reported data on the association of genomic test results with 

either a change in clinical management or a health outcome related to a particular management 

strategy. For example, genomic tests whose results were associated with response to therapy are 

included here. We did not, however, include studies that related genomic tests strictly to 

prognosis, for example, describing survival differences based on genomic test results. Similarly, 

if genomic test results were associated with staging data, we did not include these studies here 

despite the fact that staging may in turn be used to select treatment. Our notion is that we were 

primarily interested in how genomic testing could inform clinical management beyond usual 

clinical staging, which is already routinely used to guide therapy. 

Results 

We found no studies that compare two groups of women, one of which underwent genomic 

testing and one of which did not. Ideally, such a study would be prospective with random 

allocation to the groups. In fact, we did not encounter any non-randomized comparative studies, 

either prospective or retrospective, that compare management or health outcomes in two such 

groups. Therefore, the following review considers only uncontrolled studies describing the 

association of test-positive and test-negative women with management and health outcomes. 

This design limits the certainty with which one might infer that applying the test in clinical 

practice could result in improved management decisions or health outcomes compared to not 

applying the test. 

The description of evidence is divided first between studies of women with a clinical 

suspicion of ovarian cancer versus women with already diagnosed ovarian cancer. Within these 

groups, we will discuss the influence of genomic tests on specific management decisions or 

health outcomes. Summaries of all the included articles are provided in Evidence Table 3 

(Appendix D∗

). 

 

∗ Appendixes cited in this report are provided electronically at http://www.ahrq.gov/clinic/tp/genovctp.htm.
47

Women with clinical suspicion of ovarian cancer. We found no studies that describe 

evidence regarding change in management or health outcome resulting from use of genomic 

testing in women with a clinical suspicion of ovarian cancer. A large number of studies 

discussed under Question 2 describe the diagnostic accuracy of genomic tests in women with a 

clinical suspicion of ovarian cancer (based on symptoms), but none of these studies, nor any 

others screened in the literature search, described clinical management changes or health 

outcomes resulting from these tests. 

Women with already diagnosed ovarian cancer. The most studied use of genomic tests 

was for predicting or detecting response to treatment after debulking therapy and adjuvant 

chemotherapy. The studies were of two types. First, several studies sought to predict which 

patients would have a favorable response to chemotherapy (e.g., complete or partial response vs. 

stable or progressive disease). A second goal of studies was to predict, among women who 

appeared to have no evidence of disease on clinical evaluation, who would have evidence of 

disease on second-look laparotomy (SLL). Finally, several studies related genomic test results at 

the time of primary debulking surgery with the ability to achieve optimal cytoreduction. 

Predicting favorable response to chemotherapy. We found six studies describing the 

association between CA-125 and favorable response to chemotherapy;79,102-106 these studies used 

a wide range of threshold values, from 10 to 500 U/mL. In addition, the following tests were 

described in one study each: human kallikrein 6 (hK6);79 low-density lipoprotein receptorrelated protein (LRP), multidrug resistance protein (MRP), and P-glycoprotein (Pgp);107

multidrug resistance gene 1 (MDR-1), MRP-1, and MRP-2;108 TP53;109 c-erb-B2;110 and human 

kallikrein 10 (hK10).111

Table 9 and Figure 5 show the sensitivity and specificity of these tests for predicting 

response to chemotherapy. Estimates of sensitivity are widely scattered and range from 12 to 

100 percent; specificity ranged from 0 to 84 percent. The only study to report diagnostic 

performance that exceeded that of the studies of change in CA-125102,104-106 was one of MDR1;108 however, it is important to note that this relatively small study (n = 27) did not report 

estimates from the other markers assessed in the study (MRP-1 and MRP-2), but noted only that 

there was no association between those tests and response to chemotherapy.108 No other studies 

have replicated this finding. 
48

Table 9. Sensitivity and specificity of genomic tests for predicting response to chemotherapy 

Test/Study TP FN FP TN Sensitivity 

(95% CI) 

Specificity 

(95% CI) 

PPV 

(95% CI) 

NPV 

(95% CI) 

CA-125 Balbi et al., 2005102 32 8 2 24 80.00% 

(65.2 to 89.5%) 

92.30% 

(75.9 to 97.9%) 

94.10% 

(80.9 to 98.4%) 

75.00% 

(57.9 to 86.7

Balbi et al., 2005102 22 4 2 19 84.60% 

(66.5 to 93.8%) 

90.50% 

(71.1 to 97.3%) 

91.70% 

(74.2 to 97.7%) 

82.60% 

(62.9 to 93.0

Rustin et al., 2001104 80 5 1 2 94.10% 

(87.0 to 97.5%) 

66.70% 

(20.8 to 93.9%) 

98.80% 

(93.3 to 99.8%) 

28.60% 

(8.2 to 64.1%

Rustin et al., 1996105 73 12 4 42 85.90% 

(76.9 to 91.7%) 

91.30% 

(79.7 to 96.6%) 

94.80% 

(87.4 to 98.0%) 

77.80% 

(65.1 to 86.8

Gronlund et al., 2004106 27 1 14 30 96.40% 

(82.3 to 99.4%) 

68.20% 

(53.4 to 80.0%) 

65.90% 

(50.5 to 78.4%) 

96.80% 

(83.8 to 99.4

Gadducci et al., 2004103 (CA-125 half life) 

26 16 10 19 61.90% 

(46.8 to 75.0%) 

65.50% 

(47.3 to 80.1%) 

72.20% 

(56.0 to 84.2%) 

54.30% 

(38.2 to 69.5

Gadducci et al., 2004103 (CA-125% reduction) 

26 17 10 19 60.50% 

(45.6 to 73.6%) 

65.50% 

(47.3 to 80.1%) 

72.20% 

(56.0 to 84.2%) 

52.80% 

(37.0 to 68.0

hk6Diamandis et al., 200379 17 4 46 61 81.00% 

(60.0 to 92.3%) 

57.00% 

(47.5 to 66.0%) 

27.00% 

(17.6 to 39.0%) 

93.80% 

(85.2 to 97.6

MDR-1 Kamazawa et al., 2002108 21 0 1 5 100.00% 

(84.5 to 100.0%) 

83.30% 

(43.6 to 97.0%) 

95.50% 

(78.2 to 99.2%) 

100.00% 

(56.6 to 100.0

TP53 Kupryjandzyk et al., 2003109

98 57 37 37 63.20% 

(55.4 to 70.4%) 

50.00% 

(38.9 to 61.1%) 

72.60% 

(64.5 to 79.4%) 

39.40% 

(30.1 to 49.5
49

Table 9. Sensitivity and specificity of genomic tests for predicting response to chemotherapy (continued) 

Test/Study TP FN FP TN Sensitivity 

(95% CI) 

Specificity 

(95% CI) 

PPV 

(95% CI) 

NPV 

(95% CI) 

c-erb-B2 Lassus et al., 2004110 234 66 30 51 78.00% 

(73.0 to 82.3%) 

63.00% 

(52.1 to 72.7%) 

88.60% 

(84.2 to 91.9%) 

43.60% 

(34.9 to 52.6

hk10 Luo et al., 2003111 74 44 7 14 62.70% 

(53.7 to 70.9%) 

66.70% 

(45.4 to 82.8%) 

91.40% 

(83.2 to 95.8%) 

24.10% 

(15.0 to 36.5

Pgp Izquierdo et al., 1995107 32 9 8 0 78.00% 

(63.3 to 88.0%) 

0.00% 

(0.0 to 32.4%) 

80.00% 

(65.2 to 89.5%) 

0.00% 

(0.0 to 29.9%

MRP Izquierdo et al., 1995107 13 28 3 5 31.70% 

(19.6 to 47.0%) 

62.50% 

(30.6 to 86.3%) 

81.30% 

(57.0 to 93.4%) 

15.20% 

(6.7 to 30.9%

LRP Izquierdo et al., 1995107 5 36 5 3 12.20% 

(5.3 to 25.5%) 

37.50% 

(13.7 to 69.4%) 

50.00% 

(23.7 to 76.3%) 

7.70% 

(2.7 to 20.3%

Abbreviations: CI = confidence interval; FN = false negative; FP = false positive; NPV = negative predictive value; PPV = positive predic

negative; TP = true positive 
50

Figure 5. Diagnostic performance of genomic tests to predict response to chemotherapy plotted in receiver 

operating characteristic (ROC) space 

Key to Figure 5: Crosses indicate studies of CA-125; circles indicated other serum or immunohistochemical tests. 

Predicting residual disease on SLL. A second goal of studies was to predict, among women 

who appeared to have no evidence of disease on clinical evaluation, who would have evidence of 

disease on SLL. A test might be clinically useful if it could predict with a high sensitivity which 

patients with clinically undetectable disease might have cancer progression on SLL. Such a test 

might obviate the need for SLL or at least improve the accuracy of clinical staging. CA-125 is 

one marker used to detect early recurrence. 

We found five studies describing the association between CA-125 and positive disease on 

SLL.112-116 Two studies described cancer-associated serum antigen (CASA).113,114 In addition, 

the following markers were described in one study each: Cathespin-D and nm23;117 p53, murine 

double minute protein (Mdm2), and Bcl-2;118 interleukin 6 (IL-6);81 cytokeratin fragment 21-1 

(CYFRA 21-1);119 tetranectin (TN);113 and TPS.115

Three reports classified patients with microscopic disease as disease-negative,81,117,118 while 

the remaining studies classified macroscopic or microscopic disease at SLL as disease-positive. 

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0%

Specificity

Sensitivity
51

Table 10 shows the sensitivity of a positive test for identifying patients with positive SLL. 

The sensitivities for CA-125 range from 6.4 to 57.9 percent. Thresholds for a positive test vary 

from 10 U/mL to 35 U/mL, but there is no clear relationship between cut-point and sensitivity 

that explains the differences between studies. While most studies of CA-125 fell in the lower 

left quadrant of the receiver operating characteristic (ROC) space (Figure 6), representing low 

sensitivity but high specificity, the other genomic tests reported displayed greater variability, 

particularly in specificity. The higher average sensitivity was associated with lower specificity. 

Studies also differ with regard to the separation in time between the time at which the marker 

was measured and the time of SLL. The immunohistochemical tests are based on surgical 

samples, while serum markers were measured after surgery, after adjuvant chemotherapy, and 

immediately prior to SLL. 
52

Table 10. Sensitivity and specificity of genomic tests for identifying patients with residual disease at second-look laparotomy

Test/Study TP FN FP TN Sensitivity 

(95% CI) 

Specificity 

(95% CI) 

PPV 

(95% CI) 

NPV 

(95% CI

CA-125 Folk et al., 1995112 3 24 0 18 11.1% 

(3.9 to 28.1%) 

100.0% 

(82.4 to 100.0%) 

100.0% 

(43.9 to 100.0%) 

42.9% 

(29.1 to 57.

Hogdall et al., 1996113 21 17 0 30 55.3% 

(39.7 to 69.9%) 

100.0% 

(88.6 to 100.0%) 

100.0% 

(84.5 to 100.0%) 

63.8% 

(49.5 to 76.

Senapad et al., 2000115

CA-125 > 10 

11 8 11 3 57.9% 

(36.3 to 76.9%) 

21.4% 

(7.6 to 47.6%) 

50.0% 

(30.7 to 69.3%) 

27.3% 

(9.7 to 56.6

Kierkegaard et al., 1995114

CA-125 > 15 

23 335 0 35 6.4% 

(4.3 to 9.5%) 

100.0% 

(90.1 to 100.0%) 

100.0% 

(85.7 to 100.0%) 

9.5% 

(6.9 to 12.9

Wong et al., 2000116

CA-125 > 35 

5 23 1 17 17.9% 

(7.9 to 35.6%) 

94.4% 

(74.2 to 99.0%) 

83.3% 

(43.6 to 97.0%) 

42.5% 

(28.5 to 57.

CASA Hogdall et al., 1996113 12 26 0 29 31.6% 

(19.1 to 47.5%) 

100.0% 

(88.3 to 100.0%) 

100.0% 

(75.8 to 100.0%) 

52.7% 

(39.8 to 65.

Kierkegaard et al., 1995114 13 45 0 35 22.4% 

(13.6 to 34.7%) 

100.0% 

(90.1 to 100.0%) 

100.0% 

(77.2 to 100.0%) 

43.8% 

(33.4 to 54.

Cathepsin D Baekelandt et al., 1999117 22 14 78 61 61.1% 

(44.9 to 75.2%) 

43.9% 

(35.9 to 52.2%) 

22.0% 

(15.0 to 31.1%) 

81.3% 

(71.1 to 88.

CYFRA 21-1 Gadducci et al., 2001119 20 4 5 6 83.3% 

(64.1 to 93.3%) 

54.5% 

(28.0 to 78.7%) 

80.0% 

(60.9 to 91.1%) 

60.0% 

(31.3 to 83.

p53 Baekelandt et al., 1999118 20 16 64 75 55.6% 

(39.6 to 70.5%) 

54.0% 

(45.7 to 62.0%) 

23.8% 

(16.0 to 33.9%) 

82.4% 

(73.3 to 88.

Ayhan et al., 1998120 9 6 5 10 60.0% 

(35.7 to 80.2%) 

66.7% 

(41.7 to 84.8%) 

64.3% 

(38.8 to 83.7%) 

62.5% 

(38.6 to 81.
53

Table 10. Sensitivity and specificity of genomic tests for identifying patients with residual disease at second-look laparotomy (

Test/Study TP FN FP TN Sensitivity 

(95% CI) 

Specificity 

(95% CI) 

PPV 

(95% CI) 

NPV 

(95% CI

Other Baekelandt et al., 1999117

nm23 

24 12 100 39 66.7% 

(50.3 to 79.8%) 

28.1% 

(21.3 to 36.0%) 

19.4% 

(13.4 to 27.2%) 

76.5% 

(63.2 to 86.

Baekelandt et al., 1999118

Mdm2 

4 32 25 114 11.1% 

(4.4 to 25.3%) 

82.0% 

(74.8 to 87.5%) 

13.8% 

(5.5 to 30.6%) 

78.1% 

(70.7 to 84.

Baekelandt et al., 1999118

Bcl-2 

19 17 49 90 52.8% 

(37.0 to 68.0%) 

64.7% 

(56.5 to 72.2%) 

27.9% 

(18.7 to 39.6%) 

84.1% 

(76.0 to 89.

Berek et al., 199181

IL-6 

16 5 2 13 76.2% 

(54.9 to 89.4%) 

86.7% 

(62.1 to 96.3%) 

88.9% 

(67.2 to 96.9%) 

72.2% 

(49.1 to 87.

Hogdall et al., 1996113

TN 

9 29 0 30 23.7% 

(13.0 to 39.2%) 

100.0% 

(88.6 to 100.0%) 

100.0% 

(70.1 to 100.0%) 

50.8% 

(38.4 to 63.

Combination markers Kierkegaard et al., 1995114 27 31 0 35 46.6% 

(34.3 to 59.2%) 

100.0% 

(90.1 to 100.0%) 

100.0% 

(87.5 to 100.0%) 

53.0% 

(41.2 to 64.

Senapad et al., 2000115 11 8 13 1 57.9% 

(36.3 to 76.9%) 

7.1% 

(1.3 to 31.5%) 

45.8% 

(27.9 to 64.9%) 

11.1% 

(2.0 to 43.5

Abbreviations: CI = confidence interval; FN = false negative; FP = false positive; NPV = negative predictive value; PPV = positive predic

negative; TP = true positive 
54

Figure 6. Studies of the ability of serum markers to predict positive disease on second-look laparotomy 

following primary surgery and adjuvant chemotherapy plotted in ROC space 

Key to Figure 6: Crosses indicated studies of CA-125; open circles indicate studies of other genomic tests. 

Predicting ability to perform optimal cytoreduction. Several studies evaluated genomic tests 

for their ability to predict whether optimal cytoreduction (by surgical debulking) was possible. 

Definitions for optimal cytoreduction were identical between studies, based on the Gynecology 

Oncology Group criteria,121 requiring no residual tumor masses > 1 cm at debulking surgery.

Table 11 and Figure 7 show the sensitivity and specificity of tests for predicting which 

patients will achieve optimal cytoreduction at primary surgical debulking. 

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0%

Specificity

Sensitivity
55

Table 11. Sensitivity and specificity of tests for predicting which patients will achieve optimal cytoreduction at primary surgica

Study/Test TP FN FP TN Sensitivity 

(95% CI) 

Specificity 

(95% CI) 

PPV 

(95% CI) 

NPV 

(95% CI

Berchuck et al., 2004122

Chip 

20 5 7 12 80.0% 

(60.9 to 91.1%) 

63.2% 

(41.0 to 80.9%) 

74.1% 

(55.3 to 86.8%) 

70.6% 

(46.9 to 86.

Diamandis et al., 200379

hK6 

53 28 9 40 65.4% 

(54.6 to 74.9%) 

81.6% 

(68.6 to 90.0%) 

85.5% 

(74.7 to 92.2%) 

58.8% 

(47.0 to 69.

Gemer et al., 2001123

CA-125 

10 6 4 20 62.5% 

(38.6 to 81.5%) 

83.3% 

(64.1 to 93.3%) 

71.4% 

(45.4 to 88.3%) 

76.9% 

(57.9 to 89.

Memarzadeh et al., 2003124

CA-125 

14 12 31 42 53.8% 

(35.5 to 71.2%) 

57.5% 

(46.1 to 68.2%) 

31.1% 

(19.5 to 45.7%) 

77.8% 

(65.1 to 86.

Obeidat et al., 2004125

CA-125 

13 5 6 16 72.2% 

(49.1 to 87.5%) 

72.7% 

(51.8 to 86.8%) 

68.4% 

(46.0 to 84.6%) 

76.2% 

(54.9 to 89.

Saygili et al., 2002126

CA-125 

33 11 12 36 75.0% 

(60.6 to 85.4%) 

75.0% 

(61.2 to 85.1%) 

73.3% 

(59.0 to 84.0%) 

76.6% 

(62.8 to 86.

Gemer et al., 2005127

CA-125 

126 56 138 104 69.2% 

(62.5 to 72.9%) 

57.0% 

(50.8 to 63.2%) 

54.8% 

(48.4 to 61.2%) 

71.1% 

(64.8 to 77.

Abbreviations: CI = confidence interval; FN = false negative; FP = false positive; NPV = negative predictive value; PPV = positive predic

negative; TP = true positive 
56

Figure 7. Diagnostic performance of studies of tests to predict suboptimal cytoreduction (surgical debulking 

with residual disease > 1 cm) plotted in ROC space 

Key to Figure 7: Crosses indicate studies of CA-125; open circles indicate other tests. 

Discussion 

The vast majority of the available literature on the use of genomic tests in the management of 

patients with ovarian cancer consists of serum measurement of single-gene products, particularly 

CA-125, as predictors of (a) initial response to chemotherapy; (b) complete resolution of disease 

(i.e., negative SLL); or (c) the ability to perform optimal cytoreduction. There is also a 

substantial literature that reports on the association of various genomic tests with prognosis, but 

the majority of these studies were excluded because they did not describe patient management. 

The studies that sought to predict initial response to chemotherapy were generally performed 

in unselected women with ovarian cancer, not just those with optimal debulking, for example. 

Although, in theory, there is significant benefit, both clinically and in the research setting, of 

being able to predict who will respond to chemotherapy, we did not identify any studies that 

demonstrated this; there were no studies, for example, that compared different chemotherapeutic 

regimens based on test results. 

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0%

Specificity

Sensitivity
57

Studies evaluating the association of genomic test results with second-look surgery were 

commonly limited to women who had appeared to have a complete response to initial debulking 

and chemotherapy. In these studies serum markers were often measured close to the time of SLL 

(e.g., at the end of chemotherapy or immediately prior to SLL); however, immunohistochemical 

tests were usually based on tissue obtained at the time of primary surgery. SLL is sometimes 

used to evaluate women who appear clinically to have had a complete response, since other 

techniques, such as CA-125 and imaging, are fairly insensitive for very small disease. However, 

most of the data suggests that there is no substantial survival benefit to SLL, even if residual 

tumor is removed; a Gynecologic Oncology Group non-randomized study reported a difference 

in median survival of only 1 month.128 Thus, the SLL might be more properly though of as a test 

for monitoring disease or the outcome of treatment (a reference standard) rather than as a 

therapeutic option itself; the potential benefit of better sensitivity at detecting residual disease 

would be the ability to avoid the need for SLL altogether (and its concomitant cost and 

morbidity). 

Finally, the prediction of optimal debulking is potentially helpful; for example, patients 

might benefit by referral to particularly expert surgeons, or to research protocols. Patients 

unlikely to obtain optimal debulking could be selected for neoadjuvant chemotherapy to improve 

the likelihood of debulking success; however, this strategy has not been tested.129 As with tests 

for response to chemotherapy, there are, as yet, no studies prospectively demonstrating improved 

patient outcomes from such a strategy. 

Summary 

Although there is a reasonable amount of data on the association between genomic tests, 

particularly CA-125, and the likelihood of different clinical outcomes, we did not identify any 

studies that provided evidence for changes in management leading to improved outcomes based 

on the results of the tests. 

Question 5: Harms of Using Genomic Tests 

Question 5 is: What are the harms of using genomic tests for ovarian cancer prevention and 

management? 

Approach 

The nature of the potential harms associated with genomic testing in ovarian cancer varies 

depending on the potential application of the test: 

Testing for increased risk of ovarian cancer. Potential harms associated with testing for 

inherited or acquired genetic changes that are associated with increased risk of ovarian cancer 

include: 

• The harms associated with the management of women who have positive results. These 

include complications of primary preventive therapy (for example, surgical complications 

from prophylactic oophorectomy) and sequelae of the therapy (loss of fertility, premature 
58

menopause). For strategies involving more frequent screening, the impact on time and 

any discomforts associated with the screening test need to be considered. 

• The effects on quality of life and other psychological measures of a diagnosis that 

provides knowledge of an increased risk of disease, with little direct evidence for the 

benefit of management strategies. 

• The potential impact on decisions about childbearing for inherited mutation. 

Screening for early ovarian cancer, diagnosis of ovarian cancer. The main potential 

harm associated with the use of genomic tests for screening for ovarian cancer or for the 

diagnosis of ovarian cancer is the risk of a false-positive test result, with potential for anxiety 

about the diagnosis, as well as the risks of definitive diagnostic surgery.14

Testing for targets for specific therapy. The main potential harm associated with testing 

for specific targets is a false-positive result, which would lead to inappropriately exposing a 

patient to the risks of the targeted treatment, provision of ineffective treatment, and delayed start 

of potentially more effective therapy. 

We searched for studies that described these classes of adverse outcomes for any type of 

genomic testing, excluding studies covered in previous reviews of the potential harms of 

screening using CA-125 and BRCA1/2 conducted for the USPSTF,14,30,31 but including articles 

published after the inclusion dates for these reviews. 

Other Evidence Reports 

 The review of ovarian cancer screening for the USPSF did not identify any specific articles 

describing harms of screening, but pointed out the low positive predictive value of available tests 

and the large number of unnecessary surgeries.30 The adnexal mass evidence report was unable 

to draw any conclusions about the potential harms of false-positive surgeries because of 

limitations in the literature, primarily a failure to distinguish the preoperative indication for 

surgery from the postoperative findings.14 

 The review of BRCA1/2 testing identified relatively few studies addressing the harms of 

testing.31 Only one study reported complications of prophylactic oophorectomy in carriers (4 of 

80 women). Quality-of-life studies which involved only prophylactic oophorectomy (as opposed 

to prophylactic mastectomy with or without oophorectomy) were inconclusive.31

Results 

We did not identify any articles that specifically described the harms associated with 

genomic testing for ovarian cancer. In the PLCO study, 62 of 402 women with an elevated CA125 underwent surgical biopsy; of these, 16 had any neoplasm, with 13 (3.7 percent) having an 

invasive cancer.61 The paper did not report whether there were any complications from these 

surgeries. 
59

 We identified four studies of BRCA1/2 testing published since the USPSTF review (see 

Evidence Table 4, Appendix D∗

). 

McInerney-Leo and colleagues published two papers from the same large study. In the first 

paper,130 they reported data on a variety of psychological measures, including depression, selfesteem, and cancer-related distress, in 212 adult members of families with documented 

BRCA1/2 mutations before and after counseling and possible testing. Intrusive thoughts, 

depressive symptoms, and breast and ovarian cancer worries improved in subjects with negative 

test results, but there was no change in those with positive results, or in those who declined 

testing. 

In the second paper, McInerney-Leo and colleagues131 measured the impact of BRCA1/2 

testing on family relationships using a validated index in the same study population. 

Interestingly, subjects who declined testing had more positive changes than those who accepted 

testing. In those who accepted testing, there was a non-significant trend towards decreased 

expressiveness among family members in those who had an abnormal test result. 

Two studies reported measures separately for ovarian and breast cancer in women at risk for 

BRCA1 and BRCA2 mutations. In the first, Bish and colleagues132 collected data on a variety of 

quality-of-life measures in 203 subjects undergoing counseling regarding BRCA1 and 2 

mutation testing because of family histories, and found that (1) worry about ovarian cancer was 

significantly less than worry for breast cancer; (2) worry about ovarian cancer was highest in 

women with a personal history of cancer, independent of the degree of risk or results of testing 

and (3) there was no overall change in worry about ovarian cancer in response to testing. 

Claes and colleagues133 performed a similar study in 71 similar subjects. As in the Bish 

study, there were differences in responses by cancer type: risk perception and distress were 

higher for breast cancer than for ovarian cancer. After testing, distress related to ovarian cancer 

was higher in carriers then in non-carriers, but was not signficiantly different from baseline 

levels by 12 months posttesting. Women who underwent prophylactic oophorectomy had 

decreased levels of concern, but higher levels of somatic symptoms. 

Discussion 

Only two articles specifically reported outcomes relevant to ovarian cancer. The majority of 

the available literature focuses on BRCA1/2 testing, but does not report results separately for 

ovarian and breast cancer outcomes. The differences observed in the studies we did identify 

suggest that testing for genetic markers of ovarian cancer susceptibility alone may have different 

implications compared to testing for genes that affect both breast and ovarian cancer risk. 

 For the most part, the potential harms associated with the use of genomic tests in screening, 

diagnosis, and management of ovarian cancer are no different than those of other tests, such as 

imaging: the risks of false-positive results leading to unnecessary and potentially dangerous 

treatment, as well as the psychological effects of a cancer diagnosis; and the risks of falsenegative results leading to delayed diagnosis and therapy, with a potential for a poorer prognosis. 

The types of risks are similar – the only potential difference between genomic tests and other 

modalities lies in the quantitative risk of false-negative and false-positive results, which in turn 

depends on test sensitivity, specificity, and the pretest probability of disease. Higher quality 

evidence about the test characteristics of genomic tests for ovarian cancer should allow better 

 

∗ Appendixes cited in this report are provided electronically at http://www.ahrq.gov/clinic/tp/genovctp.htm.
60

estimation of these types of harms. The one type of test that might have implications similar to 

those for markers of increased risk would be a test for an inherited polymorphism that affected 

the likelihood of response to therapy; if such a polymorphism also affected the likelihood of 

responses or side effects for therapies for other conditions, the longer term implications for the 

patient and her family would have to be considered. 

For tests of markers of increased risk, such as inherited mutations, there are some additional 

potential harms, namely, the impact of knowledge of increased risk when optimal options for 

reducing that risk, either through primary or secondary prevention, are unclear. Specifically, in 

the case of ovarian cancer, bilateral oophorectomy in premenopausal women affects childbearing 

potential and induces premature menopause; given the uncertainty about optimal methods for 

hormone replacement, this option may be even more confusing for some women. Because of 

varying degrees of penetrance, even the estimate of increased risk associated with a given 

mutation is subject to a fairly wide degree of uncertainty. Issues surrounding heritability in 

subsequent generations may also be important to some women. In this case, although the 

literature on BRCA testing is helpful in identifying some of these issues, there is a greater need 

for providing results specifically for ovarian cancer-related issues in studies of markers which 

affect the risk of several types of cancers. The available literature suggests that, for most women 

with BRCA mutations, breast cancer is a greater concern than ovarian cancer. Given the 

differences in both quantitative risk and the types of risks associated with testing, diagnosis, and 

prophylaxis, results need to be provided specifically for ovarian cancer-related outcomes in 

studies of BRCA testing. 

Summary 

 The literature on the harms of genomic tests for ovarian cancer is sparse, with the majority of 

the available literature on psychological impacts of testing consisting of studies of women tested 

for susceptibility to both breast and ovarian cancer. Future studies will need not only to identify 

the short-term psychological impact of different test results, but also provide data on the 

outcomes of strategies used to reduce the risk of ovarian cancer in patients who undergo testing 

for susceptibility. The theoretical harms of genomic tests in the setting of screening, diagnosis, 

and management of ovarian cancer are similar to those for other types of tests and should 

ultimately be estimated based on better evidence for test characteristics and the effectiveness of 

management strategies based on test results. 

Question 6: Direct-to-Consumer and 

Direct-to-Physician Marketing 

Question 6 is: Has direct-to-consumer and direct-to-physician marketing of genomic tests on 

ovarian cancer increased the “appropriate” use (as defined by study investigators) of these tests? 

Approach 

We searched for articles that specifically measured responses by providers and/or patients to 

direct advertising campaigns. We also considered alternative sources of data on the nature and 
61

volume of direct-to-consumer and direct-to-provider marketing and identified methodological 

issues involved with utilizing these alternative sources. 

Results 

We did not identify any articles specifically targeting ovarian cancer genomic testing. We 

identified two articles that investigated the impact of a single advertising campaign for BRCA1/2 

testing, targeted at women at high risk for breast or ovarian cancer.134,135 

From September 2002 through February 2003, the U.S. manufacturer of BRCA1/2 testing 

conducted a pilot direct-to-consumer marketing campaign in Atlanta, Georgia, and Denver, 

Colorado. The campaign was targeted at women aged 25 to 54 with personal or family histories 

of breast and/or ovarian cancer, along with their healthcare providers. Television, radio, and 

print advertisements were generated to raise awareness about BRCA1/2 testing and to motivate 

women to ask their providers about how testing might help assess risk and change management. 

Providers received information and patient support materials prior to the beginning of the 

campaign. Although this marketing campaign was not designed as a research study, two groups 

were able to take advantage of the campaign and design studies to assess the impact of the 

campaign on test utilization. 

In a study conducted by the Centers for Disease Control and Prevention (CDC) and the state 

health departments of Colorado, Georgia, North Carolina, and Washington,134 investigators 

conducted a survey of providers and consumers in the two pilot cities and two comparison 

metropolitan areas (Raleigh-Durham, NC, and Seattle, WA). From April 21 through May 20, 

2004, a 51-question consumer telephone survey was conducted using random telephone 

numbers, with a target response of 1,600 women. Questions included family history; campaign 

awareness; interest in genetic testing for BRCA1/2; cancer concerns; and interactions with 

providers, family, and friends. A 35-question survey and monetary survey were mailed to 

providers (randomly selected to be proportionately representative of family practice, internal 

medicine, obstetrics/gynecology, and oncology) on May 1, 2003; the target response was 

approximately 1,600. 

One thousand and six hundred and thirty-five (1,635) women completed the survey, for a 

response rate of 45 percent; most were non-Hispanic white women with more than high school 

education and a median age of 40 years. Women in the pilot cities were significantly more likely 

to have heard of the test, but no significant differences were observed in stated knowledge about 

genetic testing, concern about risk for breast and ovarian cancer, or proportion of women who 

had talked with someone about genetic testing. Family histories were similar among those who 

expressed an increased interest compared to those who did not. 

One thousand and fifty-four (1,054) providers completed the survey (66 percent response 

rate). Providers in pilot cities were significantly more likely to report that they and their patients 

had been exposed to an advertisement about genetic testing and to report an increase in the 

number of patients asking about testing, asking for genetic counseling, and requesting testing. 

The number of tests ordered increased significantly in the pilot cities as well, although the 

number of referrals for counseling did not increase. Provider knowledge about testing did not 

differ between cities, but knowledge did differ between specialists, with 

obstetricians/gynecologists and oncologists having higher levels of knowledge. 

Limitations noted by study investigators included lack of data on non-responders; the 

potential for bias because of the low response rate among consumers; a relatively short lag time 
62

between the advertising campaign and the survey, which might have been insufficient to allow 

all those interested in testing to undertake and complete the process; lack of availability of data 

on the number of tests actually performed and the appropriateness of those tests because of the 

proprietary nature of the tests; and lack of data on the appropriateness of education, counseling, 

and testing ordered by providers. 

A separate study conducted by investigators at Kaiser Permanente Colorado compared 

utilization of testing before and after the advertising campaign to similar time periods using data 

from the Henry Ford Health System in Detroit, Michigan.135 Utilization assessment was through 

electronic records. The investigators noted a 240 percent increase in the number of referrals for 

genetic testing in Colorado during the advertising campaign compared to a similar time period 1 

year prior to the campaign (from 144 referrals per average membership to 499 referrals per 

average membership), while no change was seen in Detroit (53 and 52 referrals per average 

membership during the two time periods). 

Interestingly, although the absolute number of women with 10 percent or greater pretest 

probability of a mutation increased during the advertising campaign, the proportion of all 

referrals with a high pretest probability decreased from 69 percent to 48 percent in Denver, while 

no change was seen in Detroit. An increase in referrals from non-physician providers was noted. 

The authors noted the difference between self-reported patient behavior in the CDC report 

and their observations; possible explanations included inaccurate self-report, differences in 

interest in testing between the general population and women with prepaid access to healthcare, 

concurrent education efforts by Kaiser Permanente, and discussion among women at workplaces 

or other settings with common insurance coverage. 

Discussion 

We identified only two relevant articles on the impact of direct-to-consumer advertising on 

utilization of genomic tests, both involving the same advertising campaign. One study found 

evidence of increased awareness of the test covered in the advertising campaign among 

consumers, but no self-reported increase in knowledge or intention to get tested. Conversely, 

providers reported the perception of an increased number of patients discussing and requesting 

testing and reported ordering more tests. The second study used administrative data to measure 

test utilization within a managed care organization before and after the campaign and found an 

increase in the number of tests ordered, with a decrease in the proportion of women with a high 

pretest probability of a mutation. 

There are a number of methodological issues involved in assessing the impact of advertising 

on genetic test utilization: 

Definition of “appropriate” use of testing. Possible definitions include: 

• Use of the test only in those women with characteristics similar to those for whom the 

benefit of the test has been conclusively demonstrated, preferably through a randomized 

trial. 

• Use of the test only in those women with characteristics that meet criteria agreed upon by 

expert consensus. 
63

• Use of the test only in women who receive unbiased counseling on the state of 

knowledge regarding the benefits and harms of the test and who, based on their personal 

preferences, wish to have the test. 

• Use of the test only in women for whom use of the test is estimated to result in an 

acceptable cost-effectiveness ratio. 

Given the state of the literature on genomic tests in ovarian cancer, consensus opinions on 

appropriateness are likely to be the only criteria available for the near future. 

Measuring test utilization. There are several challenges to measuring test utilization: 

• As the CDC researchers noted, data on tests performed in private laboratories may not be 

publicly available; companies may view these data as proprietary information which, if 

available, would put them at a disadvantage in a competitive marketplace. 

• Test utilization data could be obtained through administrative data, such as from a 

managed care organization. The ability to link to clinical data could help in estimating 

“appropriateness” – estimation of pretest probability of BRCA1/2 mutations by the 

Kaiser researchers is an example of this. However, this type of data is also often 

proprietary and not readily accessible to outside researchers. In addition, depending on 

the data source, there may be issues about the generalizability of results, since 

presumably utilization of tests and other resources would be more tightly constrained 

within a managed care organization. Also, as the Kaiser researchers point out, women 

enrolled in managed care plans may be different in many respects from the general 

population. 

Quantifying direct-to-consumer advertising. Challenges to measuring and quantifying 

direct-to-consumer advertising include: 

• Estimating exposure to various advertising in various types of media requires complex 

survey methodology. 

• Measurement of the impact of non-advertising coverage in the media, such as coverage in 

news reports of scientific meetings, journal publications, or other forums (such as 

congressional hearings), needs to be considered. 

• It is possible that other publicly available information could provide some insight into 

advertising; for example, annual reports of publicly traded companies might provide a 

breakdown of marketing expenses, although the extent to which specific inferences could 

be drawn about the nature of these marketing expenses is unclear. 

Quantifying direct-to-physician advertising. Methdological challenges in measuring 

direct-to-physician advertising include: 
64

• Quantifying advertising in journals would require either hand searches or access to 

records from a specific journal. This would be particularly difficult for non-peerreviewed journals, which are frequently not maintained in medical libraries, but which 

may represent a significant portion of the literature. 

• Quantifying other types of “advertising,” such as sponsorship of symposia at meetings, 

exhibits at meetings, sponsorship of continuing education activities at local hospitals, etc. 

Study design. Even with a clear definition of “appropriate use” and methods for measuring 

utilization and advertising, there remain additional methodological issues. For example, 

although randomized trials would be ideal, it is difficult to imagine how one would be feasible. 

Before-and-after studies, with “exposed” and control populations, as were done with BRCA1/2 

testing, appear to be the most practical, but require considerable planning, including, ideally, 

advance notice about the advertising campaign. 

Summary 

There are considerable methodological issues involved in determining the effect of direct-toconsumer and direct-to-physician marketing on the appropriate or inappropriate use of tests. We 

identified two studies of a single advertising campaign which suggested increased utilization of 

testing in the near term after a direct-to-consumer campaign, but provided little information on 

the appropriateness of the testing. The decrease in pretest probability observed after the 

advertising campaign suggests that, for some types of genomic tests, there may be a decrease in 

positive predictive value in response to advertising. 

Modeling Results 

The results presented below represent initial calibration of the natural history models (one 

assuming that all cancers progress through Stage II, and one allowing direct progression to Stage 

III from Stage I). 

Model Validation 

Lifetime cancer incidence and mortality. Figures 8-11 and Table 12 show the results of 

these calibrations under our two natural history assumptions of disease progression. 
65

Figure 8. Age-specific probability of developing ovarian cancer (assuming Stage II is necessary transition) 

SEER vs MODEL

0.0000%

0.2000%

0.4000%

0.6000%

0.8000%

1.0000%

1.2000%

1.4000%

1.6000%

4550556065707580859095

95+

Age

Probability of 

Developing Ovarian 

Cancer

SEER

MODEL

Figure 9. Age-specific probability of dying from ovarian cancer (assuming Stage II is necessary transition) 

SEER vs MODEL

0.0000%

0.2000%

0.4000%

0.6000%

0.8000%

1.0000%

1.2000%

4550556065707580859095

95+

Age

Probability of Dying from 

Ovarian Cancer

SEER

MODEL
66

Figure 10. Age-specific probability of developing ovarian cancer (assuming Stage I can transition directly to 

Stage III) 

SEER vs MODEL

0.0000%

0.2000%

0.4000%

0.6000%

0.8000%

1.0000%

1.2000%

1.4000%

1.6000%

4550556065707580859095

95+

Age

Probability of 

Developing Ovarian 

Cancer

SEER

MODEL

Figure 11. Age-specific probability of dying from ovarian cancer (assuming Stage I can transition directly to 

Stage III) 

SEER vs MODEL

0.0000%

0.2000%

0.4000%

0.6000%

0.8000%

1.0000%

1.2000%

4550556065707580859095

95+

Age

Probability of Dying from 

Ovarian Cancer

SEER

MODEL

Both models closely approximate the Surveillance, Epidemiology, and End Results (SEER) 

lifetime risk for women at age 40 of 1.44 percent; there is a slight underestimation of mortality 

risk, primarily for women 85 and older (which is likely due to the assumption of constant stagespecific probability of diagnosis; because of age-specific variations in access to care, prevalence 

of conditions mimicking ovarian cancer, etc., this assumption may be incorrect). Lifetime risk of 
67

dying from ovarian cancer within the SEER data is 1.13 percent; model predictions are 1.06 

percent. 

Stage distribution. Table 12 illustrates overall and age-specific stage distributions from 

SEER and the two models. 

Table 12. Overall and age-specific stage distribution of ovarian cancer at diagnosis 

Age Stage I Stage II Stage III/IV 

SEER Model 1* Model 2* SEER Model 1* Model 2* SEER Model 1* Model 2* 

45-54 25.77% 20.66% 21.18% 8.25% 9.71% 5.81% 65.98% 69.63% 73.02% 

55-64 17.71% 19.57% 20.33% 7.29% 9.45% 5.79% 75.00% 70.98% 73.88% 

65-74 12.63% 19.14% 19.98% 6.32% 9.34% 5.78% 81.05% 71.53% 74.24% 

75+ 9.20% 18.83% 19.74% 5.75% 9.26% 5.78% 85.06% 71.91% 74.49% 

All 

ages 16.33% 19.55% 20.31% 6.90% 9.44% 5.79% 76.77% 71.01% 73.91% 

* Model 1 assumes that patients must transition from Stage I ovarian cancer through Stage II before progressing to 

Stage III. Model 2 assumes that some proportion of patients proceed directly to Stage III ovarian cancer from Stage I. 

Although many of the parameters of the model are known (stage-specific mortality, agespecific mortality from other causes, etc.), there are several parameters which are “unknowable 

unknowns.” In particular, we can never know with any degree of precision two key variables: 

(1) the rate at which an ovarian cancer at a given stage will proceed to the next stage; and (2) the 

probability that, within a given time period, a woman with a given stage of ovarian cancer will 

have her cancer detected and thus become an incident case. These probabilities must be imputed 

from available data – we know what the cancer incidence and stage distribution should be, and 

the values for these “unknowable” parameters are adjusted until a reasonable approximation is 

achieved. Table 13 shows the current values for these imputed probabilities. 

Table 13. Baseline estimates for annual probability of progressing between stages and stage-specific 

probability of progression 

Parameter Value: Model 1 Value: Model 2 

Annual probability of progression 

Stage I to Stage II or III 0.75 0.725 

Stage II to Stage III 0.925 0.75 

Stage III to Stage IV 0.35 0.35 

Proportion of Stage I tumors progressing directly to Stage III 0 0.75 

Annual probability of detection 

Stage I 0.25 0.25 

Stage II 0.25 0.4 

Stage III 0.7 0.7 

Stage IV 1 1 
68

The wider variation in stage distribution observed between age groups within the SEER data 

compared to model predictions may be due to any of several factors: 

(1) There may be age-specific variations in the rates of progression between stages; for 

example, age-related changes in hormonal status or immune function may affect the 

likelihood of metastasis. 

(2) There may be age-specific variations in the rates of detection within stages; for example, 

older women may have their cancer detected at later stages because of less access to 

physicians, different thresholds for seeking care for symptoms, or delayed diagnosis 

because the non-specific symptoms of ovarian cancer frequently mimic other conditions 

common in older women. 

(3) SEER data is cross-sectional, and the model is simulating a cohort. There may be 

unmeasured cohort effects in exposure to risk factors for ovarian cancer, competing risks, 

etc., which are not captured. 

Future versions of the model will explore these possibilities by allowing the probabilities of 

progression and detection to vary with age. Allowing either more rapid progression to Stage 

III/IV or lower probability of detection among older women, in particular, would result in a 

greater proportion of advanced stage disease and higher mortality rates, and result in a closer 

match to SEER data. However, the greater “precision” of such an approach must be balanced 

against the risks of introducing inaccuracies by “overfitting” a cohort model which does not 

incorporate potential cohort effects into cross-sectional data. 

Impact of Different Strategies 

In order to compare the relative impact of different strategies and the conditions under which 

they would be effective, sensitivity analyses were conducted to determine the values for specific 

parameters that would result in a 20 percent reduction in ovarian cancer mortality. Table 14 

summarizes these results. 

Table 14. Parameter estimates resulting in 20% reduction in ovarian cancer mortality under different 

strategies 

Strategy Model 1 Model 2 

Primary prevention Efficacy of primary prevention must be greater than 20% 

Interval screening Screening (with 95% specific and 90% 

sensitive test) should be every 33 

months or less 

Screening (with 95% specific and 90% 

sensitive test) should be every 31 

months or less 

Interval screening Assuming every 2-year screening, 

sensitivity must be greater than 67% 

Assuming every 2-year screening, 

sensitivity must be greater than 69% 

Genetic screening and 

interval screening of 

women with the mutation 

Assuming every 2-year screening for 

positive patients, a genetic mutation 

needs to confer at least a 30x risk 

increase 

Assuming every 2-year screening for 

positive patients, a genetic mutation 

needs to confer at least a 32x risk 

increase 
69

Table 14. Parameter estimates resulting in 20% reduction in ovarian cancer mortality under different 

strategies (continued) 

Strategy Model 1 Model 2 

Genetic screening and 

interval screening 

Assuming every 2-year screening for 

positive patients, the genetic mutation 

needs to be prevalent in 60% of the 

population 

Assuming every 2-year screening for 

positive patients, the genetic mutation 

needs to be prevalent in 64% of the 

population 

Genetic screening and 

primary prevention in 

women with the mutation 

(effectiveness of primary 

prevention = 20%) 

A genetic mutation needs to confer at 

least a 30x risk increase 

A genetic mutation needs to confer at 

least a 30x risk increase 

Genetic screening and 

primary prevention in 

women with the mutation 

(effectiveness of primary 

prevention = 20%) 

The genetic mutation needs to be 

prevalent in 51% of the population 

The genetic mutation needs to be 

prevalent in 52% of the population 

Targeted treatment If the targeted treatment reduces cancer 

mortality by 67% then the targeted 

risk factor needs to be prevalent in 

approximately 80% of the population. 

An 89% reduction would require 35% 

prevalence of the targeted risk factor. 

If the targeted treatment reduces cancer 

mortality by 67% then the targeted risk 

factor needs to be prevalent in 

approximately 89% of the population. 

An 89% reduction would require 35% 

prevalence of the targeted risk factor. 

Addressing the original questions: 

(1) How effective would a primary prevention intervention need to be to reduce ovarian 

cancer deaths by 20 percent? 

Not surprisingly, a primary intervention in the entire population that reduces ovarian cancer 

incidence by 20 percent should reduce mortality by 20 percent. This level of reduction could be 

achieved either by an intervention with 20 percent reduction used in 100 percent of the 

population, or by an intervention with higher efficacy used in a smaller proportion (for example, 

a 20 percent overall reduction would be achieved by an intervention with 40 percent efficacy 

used in 50 percent of the population). 

(2) What combinations of test sensitivity and frequency result in at least a 20 percent 

reduction in mortality? 

At a sensitivity of 90 percent, screening could be done relatively infrequently (every 33 

months) and still result in a predicted decrease of 20 percent in mortality. For biannual 

screening, sensitivity could be as low as 67 percent. 

 

(3) What combinations of (a) prevalence of a genetic mutation in the population and 

(b) relative risk associated with that mutation would result in the target 20 percent reduction in 

ovarian cancer deaths with either primary prevention (at various levels of effectiveness) or 

interval screening (at varying levels of sensitivity and specificity)? 

At a population level, very high relative risks and prevalences are required to have a 

substantial impact on overall ovarian cancer mortality; primary prevention or screening could, in 

theory, be highly effective for individuals with the genetic predisposition, but this would have a 

relatively small impact on overall population mortality. 
70

(4) How effective would a targeted treatment for ovarian cancer need to be (and in what 

proportion of the population would the marker for that treatment need to exist)? Note that we 

assume that targeted therapy would be equally effective across all stages of disease. 

If the targeted treatment reduces cancer mortality by 67 percent, then the targeted risk factor 

needs to be prevalent in approximately 80 percent of the population. An 89 percent reduction 

would require 35 percent prevalence of the targeted risk factor. 

(5) How do the test characteristics for targeted treatment or genetic screening affect the 

results? 

(6) How do the above results differ under the assumption that cancer must progress from 

Stage I to II (Model 1) and then III versus that assumption that ovarian cancer may progress 

directly from Stage I to Stage III (Model 2)? 

(7) What effect does the assumption about natural history have on the relative efficacy of 

screening? 

Figure 12 illustrates the effect of varying screening intervals at three different levels of test 

sensitivity, and for the two different models. Reductions of less than 20 percent occur at 

intervals greater than 30 months. 

Figure 12. Effect of screening frequency on reduction in cancer mortality at different levels of test sensitivity

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

3 6 9 12 15 18 21 24

Screening Frequency (Months)

% Reduction in Cancer Mortality

80%-Model 2

90%-Model 2

99%-Model 2

80%-Model 1

90%-Model 1

99%-Model 1
71

Of note: 

• At any given level of sensitivity, Model 1 (which assumes that Stage II is required prior to 

development of Stage III) results in greater relative reduction in cancer mortality than Model 

2, where cancer can progress directly from Stage I to Stage III. 

• This difference is relatively small, largely because the duration of Stage II in Model 1 

needed to result in stage distributions similar to observed data is short. 

 

• This effect is somewhat ameliorated by decreasing the screening interval (allowing more 

opportunities to detect the Stage I cancer prior to progression to Stage III). 

• Under both models, the difference in relative cancer reduction at a screening interval at 12 

months between a test with a sensitivity of 80 percent and one with a sensitivity of 99 

percent is approximately 7 percent. At any given level of sensitivity, this difference can also 

be achieved by reducing the screening interval by 3 months. 

• Reductions in mortality greater than 50 percent require screening at most every 12 months; 

at sensitivities below 99 percent, screening needs to be at less than annual intervals. 

• The finding that mortality reduction is highly sensitive to screening intervals of less than 12 

months, but relatively insensitive to screening intervals between 12 and 30 months, suggests 

that screening at less frequent intervals is detecting a population of tumors which are 

relatively indolent (length bias). 

 

Figure 13 illustrates the impact of screening frequency and test sensitivity under the 

assumptions of Model 2 (some cancers progress directly to Stage III from Stage I) on the lifetime 

negative predictive value of screening. 
72

Figure 13. Effect of screening frequency on population negative predictive value at different levels of test 

sensitivity (specificity fixed at 99%) 

0.99986

0.99988

0.9999

0.99992

0.99994

0.99996

0.99998

1

3 6 9 12 15 18 21 24

Screening Interval (Months)

Negative Predictive Value

80%

90%

99

Note that, although the relationship between negative predictive value and screening interval 

increases as test sensitivity decreases, the negative predictive value is still greater than 99.99 

percent at a sensitivity of 80 percent with biennial screening; this is due to the relatively rarity of 

ovarian cancer. 

 Conversely, positive predictive value over the lifetime of the cohort is quite sensitive to 

screening frequency (Figure 14). 
73

Figure 14. Effect of screening frequency on population positive predictive value at different levels of test 

specificity (sensitivity fixed at 99%) 

0.00%

1.00%

2.00%

3.00%

4.00%

5.00%

3 6 9 12 15 18 21 24

Screening Frequency (Months)

Positive Predictive Value

97.5%

99%

95%

Even at high levels of specificity, the positive predictive value remains below four percent. 

At the screening frequencies required for 50 percent reduction in mortality, the positive 

predictive value is less than three percent. Although this does not have a substantial impact on 

overall mortality, because of the relatively low mortality associated with laparotomy, it does 

have a substantial impact on the efficiency of screening (Table 15). At intervals of 12 months or 

less, and specificities less than 97.5 percent, the average woman is guaranteed to have at least 

one false positive test result over her lifetime. Given the above findings regarding sensitivity and 

screening frequency, and that there is an almost inevitable tradeoff between sensitivity and 

specificity, it seems likely that screening with the frequency required to have a substantial impact 

on mortality will result in a very high number of false-positive results. 

Table 15. Estimated lifetime number of false positive results at different screening frequencies and test 

specificities 

Screening interval Test specificity 

(months) 95% 97.50% 99% 

3 8.11 4.05 1.62 

6 4.07 2.03 0.81 

9 2.72 1.36 0.54 

12 2.05 1.02 0.41 
74

Table 15. Estimated lifetime number of false positive results at different screening frequencies and test 

specificities (continued) 

Screening interval Test specificity 

(months) 95% 97.50% 99% 

15 1.64 0.82 0.33 

18 1.37 0.69 0.27 

21 1.18 0.59 0.24 

24 1.04 0.52 0.21 

 

Summary 

• The current model closely approximates lifetime cancer incidence, mortality, and stage 

distribution. Differences between observed and predicted values for age-specific stage 

distribution and mortality will require further imputation of values for stage-specific 

progression and detection probabilities.

• Strategies which seek to identify high-risk groups are likely to have relatively small impact 

on overall ovarian cancer mortality, even if they are highly successful in reducing mortality 

in the risk group.

• Therapies after diagnosis which are based on genomic targets may reduce mortality 

substantially, but only if the targets are common and treatment highly effective. 

• Reductions in ovarian cancer mortality of greater than 50 percent through screening require 

testing at intervals of 12 months or less, and are relatively independent of test sensitivity. 

Conversely, the number of false-positive results is quite high at these screening intervals 

unless test specificity is quite high.

• These findings suggest that the failure to identify effective strategies for ovarian cancer 

screening may be due at least in part to the natural history of the disease, rather than the 

failure of the tests evaluated. 
75

Chapter 4. Discussion 

Limitations of the Report 

There are several limitations to this evidence report: 

• We did not review articles published in languages other than English because of a lack of 

resources for translation. It is possible that this led to failure to include some relevant 

studies. 

• We did not attempt to perform meta-analysis of specific tests, because of the considerable 

heterogeneity in design and patient populations. 

• Although we attempted to provide some sense of study quality, the validity and 

reproducibility of measures of study quality is uncertain. 

• Many of the key parameters used in modeling ovarian cancer incidence and mortality are 

unknown, and, in some cases, unknowable. In particular, this is true for the probability of 

progression between stages, and the probability that a woman with ovarian cancer at a 

given stage will have her cancer detected on the basis of symptoms. Although the model 

can be calibrated to provide a good fit to current data, it is possible that choices about the 

imputed values used for these parameters are incorrect in ways that affect the validity of 

the model. 

• The model is calibrated against reported age-specific incidence. Because the model 

simulates a cohort but is calibrated against cross-sectional age-specific data, it is possible 

that cohort effects in important variables, such as exposure to causes of ovarian cancer, 

exposure to risk modifiers such as pregnancy or contraceptive use, or competing risks 

such as other cause mortality or oophorectomy rates, play important roles in the observed 

incidence in specific age groups. Failure to take these into account during calibration 

may result in errors in the model (this is a common but rarely discussed issue with almost 

all cohort models of cancer incidence). 

Methodological Issues in the Literature 

Description of the Patient Population 

Many of the issues identified in the evidence report on adnexal mass14 were found in this 

literature as well. The majority of the papers reviewed failed to adequately describe the patient 

population; in particular, for those studies that included women with both benign and malignant 

ovarian disease, the manner in which the mass was originally detected and the subsequent 

evaluation can affect the probability of underlying disease, and thus predictive values. 

Depending on study design, prevalence may also indirectly affect estimates of sensitivity and 
76

specificity, especially in cases where women who have a negative test do not undergo the 

reference standard evaluation. Thus, even though the performance of a given test may vary 

depending on whether the patient is symptomatic or asymptomatic, the failure of studies to 

describe this aspect of their population makes drawing inference about applicability in specific 

clinical settings difficult. 

 Another common shortcoming was the failure of many studies to describe potential 

differences in results stratified by age or menopausal status. Given the clear and widely 

recognized relationship between age and ovarian cancer risk, as well as the effect of menopausal 

status on the prevalence of biological processes that may affect the levels of some tumor markers 

such as cancer antigen 125 (CA-125), we believe that this should be standard in all studies of 

potential ovarian cancer tests. This is especially true for studies of complex phenomena such as 

multiple gene or protein expression patterns, where the discovery process is based on identifying 

differences in patterns between populations; if some of the identified differences between cancer 

patients and controls in gene expression or protein profiles are in fact differences related to 

aging, menopausal status, or other processes unrelated to ovarian cancer, the ultimate sensitivity 

and specificity of tests based on these pattern recognitions may be substantially worse than in 

preliminary reports. 

Sample Size 

Few of the studies we reviewed included a priori sample size calculations, and use of 

confidence intervals for parameter estimates was uncommon. Our calculated confidence 

intervals were, for the most part, quite wide. 

Prevalence of Ovarian Cancer in Samples 

 The majority of the studies we reviewed included prevalence of ovarian cancer of 30 to 60 

percent. In a screening setting, this is several orders of magnitude higher than the observed 

prevalence in screening studies in the U.S. of 0.05 to 0.2 percent.14 This higher prevalence leads 

to falsely decreased confidence intervals for the estimates of sensitivity. Even more importantly, 

as only one author59 pointed out, this prevalence lowers the positive predictive value of tests to 

substantially less than 5 percent. 

 The prevalence in studies of the diagnostic use of a test would be expected to be higher, but, 

as discussed above, how much higher is dependent on the age, menopausal status, symptom 

status, etc., of the patient. Failure to describe these characteristics prevents assessment of how 

closely the study population reflects a likely clinical population. 

Stage Distribution within Samples 

 Given that most ovarian cancer presents in later stages, the stage distribution of samples used 

for test development and validation is likely to be skewed towards later stages. Any 

abnormalities identified may be more common in advanced cancers than early cancers; since the 

goal of screening is to identify early stage (or even preinvasive cancers), the sensitivity for tests 

derived from these types of samples may be quite lower in real-world settings. This is especially 

true for tests which identify simultaneous multiple changes in a variety of markers (such as gene 
77

arrays or studies of protein patterns) without a clear understanding of the underlying biological 

significance of the changes. Changes associated with late stage cancer may not be seen in early 

cancers. 

Biological and Observer Variability in Test Results 

Many studies, especially of tests for single gene products, reported measures of assay 

reproducibility. However, we identified only one series of studies40,41 that reported on the impact 

of both test and biological variability on interpretation of test results, in this case the significance 

of changes in CA-125 levels. Since both test reproducibility and biological variations may affect 

test characteristics (especially in applications where serial measurements are used to make 

clinical decisions), documentation of these effects for other tests should be required. 

 Similarly, for studies of multiple gene or protein expression, demonstration of reproducibility 

of results by different groups using similar analytic approaches is necessary; the available 

evidence suggests that reproducibility is still an issue.55,59

Use of Tests for Decisionmaking in Management 

The majority of the studies we identified on the use of genomic tests in patients already 

diagnosed with ovarian cancer reported on associations between test results and certain clinical 

outcomes, such as lack of response to chemotherapy, positive second look laparotomy, or length 

of overall or disease-free survival. We did not identify any studies which explicitly discussed 

how these results could be used, let alone any studies which formally tested the impact of use of 

the tests on patient outcomes. For example, if the results of a genomic test indicate a greater 

likelihood of failure to respond to standard chemotherapy, should that patient be offered only 

experimental therapies, or comfort care, rather than undergoing the effects of therapy which is 

unlikely to work? There are obvious ethical and feasibility issues involved in designing studies 

of such an approach, but if a patient will undergo the same therapy regardless of the results of the 

genomic tests, there seems to be little clinical value to performing the test. It is possible that 

there is some value to the patient and her family in having greater information about the 

probability of various outcomes, even if therapy is not affected, but this should be demonstrated 

using appropriate study designs and instruments. 

Selection of Cases and Controls in Initial Test Development 

The most common approach to initial test development in the studies we reviewed was to use 

serum or tissue from patients with cancer and compare results to a comparison group with no 

disease, or with non-ovarian cancer disease. In some cases, an attempt was made to discriminate 

between normal women, women with early stage ovarian cancer, and women with late stage 

ovarian cancer, in the hopes of identifying markers of early stage disease. 

 There are several implicit assumptions involved with this type of study design: 

• If attempts are not made to discriminate between stages, then the assumption is that all 

cancers, regardless of stage, exhibit a similar pattern. However, if there are changes in 
78

gene and/or protein patterns which are associated with advancing stage, failure to 

examine differences between stages may affect test accuracy. 

• If stages are examined differently, then the assumption is that all of the advanced stage 

cancers must have “looked” like the early stage cancers at some point. However, if 

“early” stage cancers represent cancers which are biologically different, rather than an 

early, necessary step in the development of ovarian cancer,136 then identification of 

markers of early stage cancer may not result in substantial reductions in mortality. 

• There are no other factors other than ovarian cancer itself which can explain observed 

differences between cancers and controls. The effect of potential confounders such as 

age, menopausal status, or other factors on gene or protein patterns would affect test 

specificity substantially. 

Ultimately, the ideal approach is to use prospectively collected sera to attempt to identify 

markers for those patients who subsequently developed advanced stage ovarian cancer, an 

approach which may be achievable in some of the large ongoing studies of ovarian cancer 

screening.137

 Natural History of Ovarian Cancer 

The search for better screening tests for ovarian cancer has been based on the implicit 

assumption that ovarian cancer progresses through a series of stages in a fashion analogous to 

that of cervical cancer. Alternative models are biologically plausible, and, as demonstrated by 

our simulation models, mathematical models can be “fitted” to match reported data under both 

alternatives. Our modeling suggests that, even under a model that assumes that all cancers 

progress through Stage II, screening at intervals more frequent than every 12 months is needed to 

reduce mortality by greater than 50 percent, even with a highly sensitive test, and that such 

screening would have a very low positive predictive value, even with a highly specific test. 

 If this is the case, then alternative methods for reducing ovarian cancer morbidity and 

mortality, such as improved methods for primary prevention and improved therapies, may 

ultimately offer more promise than the search for the Pap test equivalent in ovarian cancer. 

Implications of Findings 

Question 1 (Analytic Validity) 

With the exception of studies of radioimmunoassay, there is little available literature on the 

analytic validity of genomic tests for ovarian cancer, especially for use in commercial or clinical 

laboratories. 
79

Question 2 (Sensitivity and Specificity) 

Sensitivity and specificity of genomic tests in clinical practice are difficult to estimate, since 

there have been few well-designed studies in typical clinical situations, and estimates for 

available tests have wide confidence intervals. In particular, studies of proteomic tests have not 

been performed in realistic clinical scenarios; even with high specificity, positive predictive 

values in a screening setting are likely to be very low. 

Question 3 (Clinical Management of Asymptomatic Women) 

We did not identify any articles which provided evidence on the use of genomic tests in 

asymptomatic women. 

Question 4 (Clinical Management of Diagnosed Women) 

Although we identified articles reporting an association with various genomic test results and 

different clinical outcomes, we did not identify any studies which evaluated any change in 

management based on those test results. 

Question 5 (Potential Harms) 

The potential harms of genomic testing fall into two categories: (1) those related to 

identification of inherited susceptibility to ovarian cancer, which include the psychological 

impact of test results, decisionmaking regarding reproduction, and decisionmaking regarding 

prophylaxis; and (2) those related to test results in a screening, diagnostic, or clinical setting, 

which primarily include the risks of diagnosis for false-positive results and the risks of delayed 

or inappropriate treatment of false-negative results. We did not identify any studies of the 

potential harms of testing for genetic susceptibility for genes uniquely associated with ovarian 

cancer; data from two small studies suggest that, among women at risk for breast cancer gene 1/2 

(BRCA1/2) mutations, psychosocial/quality-of-life implications of testing are different for 

ovarian cancer compared with breast cancer. We also did not identify any literature on the harms 

of the use of genomic testing in screening, diagnosis, and treatment. Conceptually, there is no 

reason to think that these harms would be qualitatively any different for genomic tests than for 

other modalities such as pelvic examination, CA-125, or ultrasound; any differences between 

genomics-based tests and other would lie in the quantitative risks of false-positive and falsenegative results. 

Question 6 (Direct Marketing) 

We identified two studies on the impact of direct-to-consumer advertising for BRCA1 and 2 

testing for susceptibility to both breast and ovarian cancer, which suggested increased utilization 

by both physicians and patients. Although one of the studies suggested that there was an 

increased use of the test among lower-risk women (as evidenced by a decrease in test positive 

predictive value), it is unclear whether this was truly “inappropriate.” 
80

Modeling 

 We were able to closely approximate reported ovarian cancer incidence and mortality using a 

simulation model. This model can be used to identify test and treatment characteristics that 

would result in substantial reductions in ovarian cancer mortality. The most striking finding of 

the model is that the effect of screening frequency in achieving large-scale reductions in ovarian 

cancer mortality is greater than that of test sensitivity; achieving mortality reductions greater 

than 50 percent requires screening frequencies of less than 12 months. This is problematic for 

several reasons. First, a pilot study suggests that women are unlikely to be compliant with more 

frequent screening intervals.138 Second, more frequent screening results in lower overall positive 

predictive value, even with a highly specific test. Finally, if effective primary prevention 

strategies are identified which lower the incidence of ovarian cancer, the positive predictive 

value of screening will be lowered to an even larger extent. 
81

Chapter 5. Future Research 

This chapter outlines research priorities identified through the review, both in terms of 

fundamental gaps in knowledge and in addressing methodological issues in existing studies. 

Minimal Data Reporting 

We suggest that future studies relevant to screening and diagnosis provide data on, and 

present results stratified by, the following minimal subject characteristics: 

• Subject age and/or menopausal status; 

• Subject race and ethnicity; 

• Presence or absence of known risk factors for ovarian cancer, particularly family history; 

• For subjects with cancer or adnexal masses, the means by which the mass was initially 

diagnosed; 

• For subjects with cancer or adnexal masses, the reason for the initial examination which 

led to diagnosis of a mass: symptoms referable to a mass or ovarian cancer, evaluation for 

other symptoms, asymptomatic screening for ovarian cancer, or asymptomatic screening 

for other conditions. 

We recognize that, when using large databases for initial analysis, such as those used in 

many early proteomics studies, such detail may not be available; however, researchers should 

recognize and discuss the potential biases introduced by these factors. 

Test Reproducibility 

• Data on test reproducibility – such as coefficients of variation, inter- and intra-observer 

agreement, or concordance of results across laboratories – should be consistently reported or 

referenced. 

• Whenever possible, the potential impact of this reproducibility on test characteristics should 

be estimated. For example, given a coefficient of variation of some percent, what proportion 

of test results will fall on the other side of the threshold between positive and negative due to 

chance alone? 

• The potential impact of reproducibility on interpretation of serial test results should also be 

estimated where appropriate. 
82

Biological Variability 

• The effect of variation with time, either randomly or in relation to cyclic changes such as the 

menstrual cycle, should also be reported for tests which have potential use as serial markers. 

 

• Any variability due to age, menopausal status, or other biological processes should be tested 

for and noted. 

Test-Negative Subjects 

• Since in many studies “control” patients never undergo the reference standard (histological 

examination of the ovaries), there is the potential for verification bias. Although, given the 

relatively low incidence of ovarian cancer, the probability of misclassification is fairly low, 

studies should ideally have some followup on test-negative subjects to ensure that ovarian 

cancer has not developed within a short time after the test was performed. 

Evaluation of Tests 

• Ultimately, tests need to be evaluated based on their intended use and at the stage in the 

clinical pathway where they will be used. Therefore, potential screening tests must be 

evaluated in screening settings, with a realistic underlying prevalence of cancer. Similarly, 

potential diagnostic tests must be tested in settings where there is uncertainty about the 

diagnosis of ovarian cancer.

 

• Ideally, test characteristics for a variety of tests will be compared within the same study 

population, in order to avoid the inherent difficulties of comparing results across studies. At 

a minimum, given that the performance characteristics of cancer antigen 125 (CA-125) are 

well established, new tests should be directly compared to CA-125.

 

• Although retrospective studies based on sera or other tissues are useful for establishing 

estimates of test performance for sample size considerations, new screening and diagnostic 

tests need to be evaluated prospectively. For example:

- For screening tests, prospective demonstration of at least one important outcome, 

such as (a) reduced ovarian cancer-specific mortality, or (b) improved quality of life 

as documented by a validated instrument. Ideally, this would be done via randomized 

trials; however, alternative study designs (such as prospective cohort studies with 

appropriate adjustment for potential confounders) are reasonable for rarer primary 

outcomes (such as ovarian cancer mortality). In the screening context, given the 

relatively low positive predictive value of any screening test, documenting the effect 

of the test on overall quality of life at the population level should be easily 

demonstrated within the context of a randomized trial. 
83

• Evaluation of the use of tests in predicting outcomes must ultimately be linked to some 

change in patient outcomes; at the least, there should be some measure of the value of the 

information gained from the test result is helpful in some way to the patient. Ideally, the 

effect of changes in management based on test results should be evaluated in properly 

designed studies. For example:

- For tests which appear to reliably predict failure to respond to conventional therapies, 

studies should prospectively document improved patient outcomes based on this 

knowledge (such as improved quality of life based on more precise prognosis, or 

improved quality of life due to avoidance of side effects from ineffective therapy). 

Ideally, this would be based on randomized trials – patients could be randomized to 

testing with treatment based on test results, versus no testing; alternatively, testing 

could be done, with randomized allocation to usual care versus no care for those with 

test results predicting poor response.

- For tests which predict greater response to specific agents, improved survival and 

quality of life need to be documented using randomized trials of those agents in those 

with specific test results. 

Natural History of Ovarian Cancer 

• Underlying assumptions about the natural history of ovarian cancer can have a large effect 

on the estimated impact of screening compared to other strategies for prevention of ovarian 

cancer morbidity and mortality. Every effort should be made towards a better understanding 

of whether ovarian cancer “behaves” like cervical cancer in the sense of progressing through 

different stages, or whether rapid progression is the most common biological behavior.

• The implications of these assumptions on the relative efficacy of screening compared to 

other strategies needs to be evaluated by more sophisticated simulation models. 

85

Chapter 6. Conclusions 

 Ovarian cancer remains a significant cause of morbidity and mortality in women, and efforts 

at reducing its toll have been relatively unsuccessful, especially when compared with other 

causes of cancer death in women. Unlike lung cancer or cervical cancer, there does not appear to 

be a common causal exposure which can be addressed through various public health 

interventions; unlike cervical, breast, or colorectal cancer, effective screening methods have not 

yet been identified; unlike breast cancer, markers of response to specific treatments have not yet 

been discovered and proven to improve patient outcomes. 

 The ever-increasing knowledge of the role of genes in health and disease offers the promise 

of greater understanding of the biology of ovarian cancer, and evidence-based strategies for 

prevention based on that understanding. Understanding of the causal mechanisms could 

potentially lead to population-based primary prevention strategies which preserve ovarian 

function, while identification of markers of increased risk in addition to breast cancer genes 1/2 

(BRCA 1/2) offers the potential for more radical preventive measures such as prophylactic 

oophorectomy. Improved understanding of the molecular changes leading to cancer may lead to 

screening tests of very high sensitivity and specificity. Identification of markers of response to 

therapy could lead to improved survival, or reduced side effects from current treatment. 

 Unfortunately, our review found that there is limited evidence for the utility of genomic tests 

other than cancer antigen 125 (CA-125) or BRCA1/2 in the prevention of ovarian cancer. Other 

than commercially approved radioimmunoassay tests for single gene products, there is little 

available literature on the analytic validity of potential genomic tests in typical clinical 

laboratories. There are almost no data on the sensitivity and specificity of genomic tests for 

screening or diagnosis in clinically realistic settings. Although results of some genomic tests 

have been shown to be associated with certain outcomes of treatment, there are no data on how 

changes in management based on those test results would lead to improved patient outcomes. 

 New genomic tests do not appear to have any qualitative risks beyond those of other tests for 

inherited susceptibility for cancer, or other tests used in screening, management, and treatment. 

Depending on the ultimate sensitivity and specificity of the tests in typical practice, the 

quantitative probability of these harms may differ from existing tests. 

 The use of direct-to-consumer advertising has the potential to increase utilization of these 

tests, but, in the absence of criteria for “appropriate use,” it is unclear how to evaluate this 

increased utilization. 

 Ultimately, the clinical utility of genomic tests in the prevention of morbidity and mortality 

from ovarian cancer will depend not only on the sensitivity and specificity of a given test in a 

specific clinical situation, but on the underlying natural history of ovarian cancer. If the 

biological features of ovarian cancer predispose most cancers to rapid dissemination within the 

abdominal cavity, then strategies which emphasize primary prevention and/or improved 

treatment efficacy may ultimately be more effective than the most sensitive and specific test. 

87

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122. Berchuck A, Iversen ES, Lancaster JM, et al. 

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123. Gemer O, Segal S, Kopmar A. Preoperative CA-125 

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advanced epithelial ovarian cancer. Acta Obstet 

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124. Memarzadeh S, Lee SB, Berek JS, et al. CA125 

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125. Obeidat B, Latimer J, Crawford R. Can optimal 

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126. Saygili U, Guclu S, Uslu T, et al. Can serum CA125 levels predict the optimal primary 

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127. Gemer O, Lurian M, Gdalevich M, et al. A 

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128. Greer BE, Bundy BN, Ozols RF, et al. Implications 

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129. Vergote I, van Gorp T, Amant F, et al. Neoadjuvant 

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130. McInerney-Leo A, Biesecker BB, Hadley DW, et 

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131. McInerney-Leo A, Biesecker BB, Hadley DW, et 

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132. Bish A, Sutton S, Jacobs C, et al. Changes in 
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133. Claes E, Evers-Kiebooms G, Denayer L, et al. 

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134. Centers for Disease Control and Prevention (CDC). 

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135. Mouchawar J, Hensley-Alford S, Laurion S, et al. 

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136. Berchuck A, Iversen ES, Lancaster JM, et al. 

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137. Jacobs IJ, Menon U. Progress and challenges in 

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138. Drescher CW, Nelson J, Peacock S, et al. 

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Berchuck A, Iversen ES, Lancaster JM, et al. Prediction of 

optimal versus suboptimal cytoreduction of advanced-stage 

serous ovarian cancer with the use of microarrays. Am J 

Obstet Gynecol 2004;190(4):910-25. 

Berek JS, Chung C, Kaldi K, et al. Serum interleukin-6 

levels correlate with disease status in patients with 

epithelial ovarian cancer. Am J Obstet Gynecol 

1991;164(4):1038-42; discussion 1042-3. 

Bish A, Sutton S, Jacobs C, et al. Changes in psychological 

distress after cancer genetic counselling: a comparison of 

affected and unaffected women. Br J Cancer 

2002;86(1):43-50. 

Bon GG, Verheijen RH, Zuetenhorst JM, et al. Mucin-like 

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Gynecol Obstet Invest 1996;42(1):58-62. 

Brinkmann D, Ryan A, Ayhan A, et al. A molecular genetic 

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Centers for Disease Control and Prevention (CDC). Genetic 

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Raleigh-Durham, and Seattle, 2003. Morb Mortal Wkly 

Rep Surveill Summ 2004;53(27):603-6. 

Chang HW, Lee SM, Goodman SN, et al. Assessment of 

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2002;94(22):1697-703. 

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5. 

Claes E, Evers-Kiebooms G, Denayer L, et al. Predictive 

genetic testing for hereditary breast and ovarian cancer: 

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Conrads TP, Fusaro VA, Ross S, et al. High-resolution 

serum proteomic features for ovarian cancer detection. 

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adhesion molecules in serum and cyst fluid from patients 

with cystic tumours of the ovary. Hum Reprod 

1998;13(10):2831-5. 

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2003;21(6):1035-43. 

Fayed ST, Ahmad SM, Kassim SK, et al. The value of CA 
95

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Filella X, Ballesta AM, Fox M, et al. Multicentre clinical 

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Folk JJ, Botsford M, Musa AG. Monitoring cancer antigen 

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Gadducci A, Baicchi U, Marrai R, et al. Preoperative 

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1996;60(2):197-202. 

Gadducci A, Cosio S, Fanucchi A, et al. The predictive and 

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Gadducci A, Ferdeghini M, Cosio S, et al. The clinical 

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Gemer O, Lurian M, Gdalevich M, et al. A multicenter 

study of CA 125 level as a predictor of non-optimal 

primary cytoreduction of advanced epithelial ovarian 

cancer. Eur J Surg Oncol 2005;31(9):1006-10. 

Gemer O, Segal S, Kopmar A. Preoperative CA-125 level 

as a predictor of non optimal cytoreduction of advanced 

epithelial ovarian cancer. Acta Obstet Gynecol Scand 

2001;80(6):583-5. 

Gorelik E, Landsittel DP, Marrangoni AM, et al. 

Multiplexed immunobead-based cytokine profiling for 

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Gronlund B, Hansen HH, Hogdall C, et al. Do CA125 

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Hasholzner U, Baumgartner L, Stieber P, et al. Clinical 

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Heinzelmann-Schwarz VA, Gardiner-Garden M, Henshall 

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Hellström I, Raycraft J, Hayden-Ledbetter M, et al. The 

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Hibbs K, Skubitz KM, Pambuccian SE, et al. Differential 

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Hofmann M, Ruschenburg I. mRNA detection of tumorrejection genes BAGE, GAGE, and MAGE in peritoneal 

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Hogdall CK, Hogdall EV, Hording U, et al. Use of 

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Hubl W, Chan DW, Van Ingen HE, et al. Multicenter 

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Hurteau JA, Woolas RP, Jacobs IJ, et al. Soluble 

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Inaba N, Negishi Y, Fukasawa I, et al. Cytokeratin 

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Izquierdo MA, van der Zee AG, Vermorken JB, et al. Drug 

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Janatova M, Pohlreich P, Matous B. Detection of the most 

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Kamazawa S, Kigawa J, Kanamori Y, et al. Multidrug 

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Kenemans P, Verstraeten AA, van Kamp GJ, et al. The 

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1995;59(2):251-4. 

Kim JH, Herlyn D, Wong KK, et al. Identification of 

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Kozak KR, Amneus MW, Pusey SM, et al. Identification of 

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Lee H, Park G, Jung JH, et al. Diagnostic approach using 

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Mabrouk RA, Ali-Labib R. Detection of urokinase 

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McInerney-Leo A, Biesecker BB, Hadley DW, et al. 

BRCA1/2 testing in hereditary breast and ovarian cancer 

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McInerney-Leo A, Biesecker BB, Hadley DW, et al. 

BRCA1/2 testing in hereditary breast and ovarian cancer 

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McIntosh MW, Drescher C, Karlan B, et al. Combining CA 

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Obeidat B, Latimer J, Crawford R. Can optimal primary 

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List of Acronyms/Abbreviations 

AHRQ Agency for Healthcare Research and Quality 

β-hCG Beta human chorionic gonadotropin 

Bcl-2 (Anti-apoptosis protein) 

BRCA1/2 Breast cancer gene 1/2 

CA-125 Cancer antigen 125 

CA-15-3 Cancer antigen 15-3 

CA-19-9 Cancer antigen 19-9 

CA-27-29 Cancer antigen 27-29 

CA-72-4 Cancer antigen 72-4 

CASA Cancer-associated serum antigen 

CDC Centers for Disease Control and Prevention 

CEA Carcinoembryonic antigen 

c-erb-B2 (Same as HER-2) 

c-erb-2 (Same as HER-2) 

CI Confidence interval 

CK19 Cytokeratin 19 

CYFRA 21-1 Cytokeratin fragment 21 

EGAPP Evaluation of Genomic Applications in Practice and Prevention 

EGFR Epidermal growth factor receptor 

FAS Fatty acid synthase 

FIGO International Federation of Obstetrics and Gynecology 

GAT Galactosyltransferase associated with tumor 

G-CSF Granulocyte-colony stimulating factor 

HE4 Human epididymis protein 4 

HER-2 Human epidermal growth factor receptor 2 

hK6 Human kallikrein 6 

hK10 Human kallikrein 10 

IL-2 Interleukin 2 

IL-6 Interleukin 6 

IL-8 Interleukin 8 

LASA Lipid-associated sialic acid 

LMP Low malignant potential 

LRP Low-density lipoprotein receptor-related protein 

LSA Lysophospholipids 

M-CSF Macrophage colony stimulating factor 

Mdm2 Murine double minute protein 

MDR-1 Multidrug resistance gene 1 

MeSH Medical Subject Headings 

MMP Matrix metalloproteinases 

MRP1/2 Multidrug resistance protein 1/2 

MW Molecular weight 

M/Z Mass-to-charge 

nm23 (Metastasis suppressor) 

OVX1 (Monoclonal antibody raised against a human ovarian carcinoma cell line) 
100

p53 (Transcription factor) 

p55, p75 (Tumor necrosis factor receptors) 

Pgp P-glycoprotein 

PLCO Prostate, Lung, Colon, and Ovarian screening trial (National Cancer Institute) 

RCT Randomized controlled trial 

RIA Radioimmunoassay 

ROC Receiver operating characteristic 

SAX2 Strong anionic exchanger 

SEER Surveillance, Epidemiology, and End Results 

SELDI-TOF Surfance-enhanced laser desorption inonization time-of-flight 

SLL Second-look laparotomy 

SVM-GA Support vector machine with genetic algorithm 

SVM-ST Support vector machine statistical testing 

TATI Tumor-associated trypsin inhibitor 

TN Tetranectin 

TP53 (Same as p53) 

TPA Tissue plasminogen activator 

TPS Tissue polypeptide-specific antigen 

USCS United States Cancer Statistics 

USPSTF U.S. Preventive Services Task Force 

VEGF Vascular endothelial growth factor 

WCX2 Weak cationic exchanger 
APPENDIXES 
A-1

Appendix A: Exact Search String 

Database: Ovid MEDLINE® <1966 to May Week 2 2006> 

Search Strategy: 

-------------------------------------------------------------------------------- 

1. liotta l$.au. 

2. Ovarian Neoplasms/ 

3. 1 and 2 

4. exp Ovarian Neoplasms/ 

5. exp Genomics/ 

6. exp Genetic Phenomena/ 

7. ovacheck.mp. 

8. myriad.mp. 

9. Chorionic Gonadotropin, beta Subunit, Human/ 

10. GENES, BRCA1/ or BRCA1 PROTEIN/ 

11. GENES, BRCA2/ or BRCA2 PROTEIN/ 

12. CA-125 Antigen/ 

13. Antigens, Tumor-Associated, Carbohydrate/ 

14. Carcinoembryonic Antigen/ 

15. Receptor, erbB-2/ 

16. Tumor Markers, Biological/ 

17. Antigens, Neoplasm/ 

18. 4 and (or/5-17) 

19. correlogic.mp. 

20. 4 and (or/5,7-17) 

21. 18 not 20 

22. limit 20 to (humans and english language and abstracts) 

23. exp Diagnosis/ 

24. exp "Sensitivity and Specificity"/ 

25. di.fs. 

26. 22 and (or/24-25) 

27. 3 and 26 

28. 3 and 22 

29. 3 not 28 

30. 28 not 27 

31. *"Proteome"/ 

32. oligonucleotide array sequence analysis/ or protein array analysis/ 

33. 4 and (or/5,7-17,32) 

34. 33 not 20 

35. 3 and 34 

36. 2 and (or/5,7-17,32) 

37. *ovarian neoplasms/ 

38. 37 and (or/5,7-17,32) 

39. 2 and (or/7-8,19,32) 
A-2

40. 3 and 39 

41. 39 not 26 

42. 3 not 26 

43. 41 or 42 

44. limit 43 to (humans and english language) 

45. limit 44 to abstracts 

46. from 45 keep 1-10 

47. from 45 keep 1-167 

48. "Reproducibility of Results"/ 

49. reference standards/ 

50. quality control/ 

51. reference values/ 

52. or/48-51 

53. 52 and (or/5,7-17,19,32) and 4 

54. 52 and (or/5,7-17,19,32) and 4 

55. 52 and (or/5,7-17,19,32) 

56. Genetic Screening/ 

57. Genetic Counseling/ 

58. 4 and (or/56-57) 

59. limit 58 to (humans and english language and abstracts) 

60. 59 not (26 or 45) 

61. 54 not (26 or 45) 

62. limit 61 to (humans and abstracts) 

63. genes, brca1/ or genes, brca2/ 

64. 60 not 63 

65. 60 not 64 

66. from 64 keep 1-155 

67. from 62 keep 1-76 
A-3

Appendix B: List of Excluded Studies 

All excluded studies listed below were reviewed in their full-text version. Following each 

reference, in italics, is the reason for exclusion. Reasons for exclusion signify only the 

usefulness of the articles for this study and are not intended as criticisms of the articles. 

Abendstein B, Daxenbichler G, Windbichler G, et al. 

Predictive value of uPA, PAI-1, HER-2 and VEGF in the 

serum of ovarian cancer patients. Anticancer Res 

2000;20(1B):569-72. Exclude: non-specific general 

prognosis.

Adib TR, Henderson S, Perrett C, et al. Predicting 

biomarkers for ovarian cancer using gene-expression 

microarrays. Br J Cancer 2004;90(3):686-92. Exclude: cell 

line only.

Afify AM, al-Khafaji BM. Diagnostic utility of thyroid 

transcription factor-1 expression in adenocarcinomas 

presenting in serous fluids. Acta Cytol 2002;46(4):675-8. 

Exclude: no 2x2 table.

Afify AM, Ferguson AW, Davila RM, et al. Expression of 

CD44S and CD44v5 is more common in stage III than in 

stage I serous ovarian carcinomas. Appl Immunohistochem 

Mol Morphol 2001;9(4):309-14. Exclude: denominator 

NOT patients. 

Ahmed N, Oliva KT, Barker G, et al. Proteomic tracking of 

serum protein isoforms as screening biomarkers of ovarian 

cancer. Proteomics 2005;5(17):4625-36. Exclude: no 2x2 

table.

Ahmed N, Riley C, Rice GE, et al. Alpha(v)beta(6) 

integrin-A marker for the malignant potential of epithelial 

ovarian cancer. J Histochem Cytochem 2002;50(10):1371-

80. Exclude: denominator NOT patients.

Akahiro J, Konno R, Ito K, et al. Impact of serum 

interleukin-18 level as a prognostic indicator in patients 

with epithelial ovarian carcinoma. Int J Clin Oncol 

2004;9(1):42-6. Exclude: no 2x2 table. 

Ala-Fossi SL, Aine R, Punnonen R, et al. Is potential to 

produce inhibins related to prognosis in ovarian granulosa 

cell tumors? Eur J Gynaecol Oncol 2000;21(2):187-9. 

Exclude: only germ cell or stromal.

Alexe G, Alexe S, Liotta LA, et al. Ovarian cancer 

detection by logical analysis of proteomic data. Proteomics 

2004;4(3):766-83. Exclude: mathematical model.

Ali-Fehmi R, Che M, Khalifeh I, et al. The effect of 

cyclooxygenase-2 expression on tumor vascularity in 

advanced stage ovarian serous carcinoma. Cancer 

2003;98(7):1423-9. Exclude: no 2x2 table.

Altavilla G, Marchetti M, Padovan P, et al. Predictive value 

of proliferative cellular nuclear antigen (PCNA) and Ki-67 

antigen in advanced stage serous papilliferous ovarian 

cancer. Eur J Gynaecol Oncol 1996;17(6):524-8. Exclude: 

prognosis only, no link to mgmt or outcomes. 

Altevogt P, Fogel M. The role of L1 in the progression of 

ovarian carcinomas. Zentralbl Gynakol 2004;126(5):323-5. 

Exclude: relevant review. 

Alvarez Secord A, Sayer R, Snyder SA, et al. The 

relationship between serum vascular endothelial growth 

factor, persistent disease, and survival at second-look 

laparotomy in ovarian cancer. Gynecol Oncol 

2004;94(1):74-9. Exclude: no 2x2 table. 

Andersen MR, Nelson J, Peacock S, et al. Worry about 

ovarian cancer risk and use of screening by high-risk 

women: how you recruit affects what you find. Am J Med 

Genet A 2004;129(2):130-5. Exclude: Background only. 

Antonic J, Rakar S. Validity of colour and pulsed Doppler 

US and tumour marker CA 125 in differentiation between 

benign and malignant ovarian masses. Eur J Gynaecol 

Oncol 1996;17(1):29-35. Exclude: no 2x2 table. 

Aranganathan S, Senthil K, Nalini N. A case control study 

of glycoprotein status in ovarian carcinoma. Clin Biochem 

2005;38(6):535-9. Exclude: no 2x2 table. 

Aris VM, Cody MJ, Cheng J, et al. Noise filtering and 

nonparametric analysis of microarray data underscores 

discriminating markers of oral, prostate, lung, ovarian and 

breast cancer. BMC Bioinformatics 2004;5(1):185. 

Exclude: denominator NOT patients. 

Arnold JM, Cummings M, Purdie D, et al. Reduced 

expression of intercellular adhesion molecule-1 in ovarian 

adenocarcinomas. Br J Cancer 2001;85(9):1351-8. 

Exclude: cell line only. 

Arslan AA, Zeleniuch-Jacquotte A, Lundin E, et al. Serum 

follicle-stimulating hormone and risk of epithelial ovarian 

cancer in postmenopausal women. Cancer Epidemiol 

Biomarkers Prev 2003;12(12):1531-5. Exclude: not 

diagnostic test. 

Attanoos RL, Webb R, Dojcinov SD, et al. Value of 

mesothelial and epithelial antibodies in distinguishing 

diffuse peritoneal mesothelioma in females from serous 
A-4

papillary carcinoma of the ovary and peritoneum. 

Histopathology 2002;40(3):237-44. Exclude: pathology 

study.

Auranen A, Grenman S, Kleml PJ. Immunohistochemically 

detected p53 and HER-2/neu expression and nuclear DNA 

content in familial epithelial ovarian carcinomas. Cancer 

1997;79(11):2147-53. Exclude: no 2x2 table.

Ayhan A, Ertunc D, Tok EC, et al. Expression of the c-Met 

in advanced epithelial ovarian cancer and its prognostic 

significance. Int J Gynecol Cancer 2005;15(4):618-23. 

Exclude: no 2x2 table.

Baekelandt M, Holm R, Nesland JM, et al. Expression of 

apoptosis-related proteins is an independent determinant of 

patient prognosis in advanced ovarian cancer. J Clin Oncol 

2000;18(22):3775-81. Exclude: non-specific general 

prognosis. 

Baekelandt MM, Holm R, Nesland JM, et al. Pglycoprotein expression is a marker for chemotherapy 

resistance and prognosis in advanced ovarian cancer. 

Anticancer Res 2000;20(2B):1061-7. Exclude: non-specific 

general prognosis. 

Bajetta E, Di Leo A, Biganzoli L, et al. Phase II study of 

vinorelbine in patients with pretreated advanced ovarian 

cancer: activity in platinum-resistant disease. J Clin Oncol 

1996;14(9):2546-51. Exclude: unrelated. 

Bandopadhyay M, Ganguly AK. Putrescine, DNA, RNA 

and protein contents in human uterine, breast and rectal 

cancer. J Postgrad Med 2000;46(3):172-5. Exclude: 

denominator NOT patients.

Banks RE, Gearing AJ, Hemingway IK, et al. Circulating 

intercellular adhesion molecule-1 (ICAM-1), E-selectin and 

vascular cell adhesion molecule-1 (VCAM-1) in human 

malignancies. Br J Cancer 1993;68(1):122-4. Exclude: no 

OVCA-specific data. 

Barbati A, Mariani L, Porpora MG, et al. Serum evaluation 

of P53 protein in patients with gynaecological cancer. 

Anticancer Res 2000;20(2A):1033-5. Exclude: no 2x2 

table.

Barbieri F, Lorenzi P, Ragni N, et al. Overexpression of 

cyclin D1 is associated with poor survival in epithelial 

ovarian cancer. Oncology 2004;66(4):310-5. Exclude: 

prognosis only, no link to mgmt or outcomes. 

Bard MP, Hegmans JP, Hemmes A, et al. Proteomic 

analysis of exosomes isolated from human malignant 

pleural effusions. Am J Respir Cell Mol Biol 

2004;31(1):114-21. Exclude: N=1 case report. 

Baron AT, Lafky JM, Boardman CH, et al. Serum sErbB1 

and epidermal growth factor levels as tumor biomarkers in 

women with stage III or IV epithelial ovarian cancer. 

Cancer Epidemiol Biomarkers Prev 1999;8(2):129-37. 

Exclude: prognosis only, no link to mgmt or outcomes. 

Baron-Hay S, Boyle F, Ferrier A, et al. Elevated serum 

insulin-like growth factor binding protein-2 as a prognostic 

marker in patients with ovarian cancer. Clin Cancer Res 

2004;10(5):1796-806. Exclude: no 2x2 table. 

Barton DP, Blanchard DK, Michelini-Norris B, et al. High 

serum and ascitic soluble interleukin-2 receptor alpha 

levels in advanced epithelial ovarian cancer. Blood 

1993;81(2):424-9. Exclude: no 2x2 table. 

Bayani J, Brenton JD, Macgregor PF, et al. Parallel 

analysis of sporadic primary ovarian carcinomas by 

spectral karyotyping, comparative genomic hybridization, 

and expression microarrays. Cancer Res 2002;62(12):3466-

76. Exclude: cell line only. 

Bellotti T, Luo Z, Gammerman A, et al. Qualified 

predictions for microarray and proteomics pattern 

diagnostics with confidence machines. Int J Neural Syst 

2005;15(4):247-58. Exclude: no 2x2 table. 

Ben-Arie A, Hagay Z, Ben-Hur H, et al. Elevated serum 

alkaline phosphatase may enable early diagnosis of ovarian 

cancer. Eur J Obstet Gynecol Reprod Biol 1999;86(1):69-

71. Exclude: N=1 case report.

Ben-Hur H, Gurevich P, Ben-Arie A, et al. Apoptosis and 

apoptosis-related proteins (Fas, Fas ligand, bcl-2, p53) in 

macrophages of human ovarian epithelial tumors. Eur J 

Gynaecol Oncol 2000;21(2):141-5. Exclude: cell line only. 

Berchuck A, Iversen ES, Lancaster JM, et al. Patterns of 

gene expression that characterize long-term survival in 

advanced stage serous ovarian cancers. Clin Cancer Res 

2005;11(10):3686-96. Exclude: no 2x2 table. 

Bergen HR 3rd, Vasmatzis G, Cliby WA, et al. Discovery 

of ovarian cancer biomarkers in serum using NanoLC 

electrospray ionization TOF and FT-ICR mass 

spectrometry. Dis Markers 2003-2004;19(4-5):239-49. 

Exclude: no quantification of reproducibility. 

Berker B, Dunder I, Ensari A, et al. Prognostic significance 

of apoptotic index and bcl-2 and p53 expression in 

epithelial ovarian carcinoma. Eur J Gynaecol Oncol 

2002;23(6):505-10. Exclude: prognosis only, no link to 

mgmt or outcomes. 

Berker B, Dunder I, Ensari A, et al. Prognostic value of p53 

accumulation in epithelial ovarian carcinomas. Arch 

Gynecol Obstet 2002;266(4):205-9. Exclude: prognosis 

only, no link to mgmt or outcomes.

Bernardini M, Lee CH, Beheshti B, et al. High-resolution 

mapping of genomic imbalance and identification of gene 

expression profiles associated with differential 

chemotherapy response in serous epithelial ovarian cancer. 
A-5

Neoplasia 2005;7(6):603-13. Exclude: no 2x2 table.

Bese T, Nomir SK. The importance of serum insulin-like 

growth factor-I level determination in the follow-up of 

patients with epithelial ovarian cancer. Eur J Gynaecol 

Oncol 2001;22(5):372-6. Exclude: prognosis only, no link 

to mgmt or outcomes. 

Bild AH, Yao G, Chang JT, et al. Oncogenic pathway 

signatures in human cancers as a guide to targeted 

therapies. Nature 2006;439(7074):353-7. Exclude: no 2x2 

table.

Bildrici K, Tel N, Ozalp SS, et al. Prognostic significance 

of DNA topoisomerase II-alpha (Ki-S1) immunoexpression 

in endometrial carcinoma. Eur J Gynaecol Oncol 

2002;23(6):540-4. Exclude: not OVCA.

Blandy C, Chabal F, Stoppa-Lyonnet D, et al. Testing 

participation in BRCA1/2-positive families: initiator role of 

index cases. Genet Test 2003;7(3):225-33. Exclude: BRCAQ5.

Bluman LG, Rimer BK, Regan Sterba K, et al. Attitudes, 

knowledge, risk perceptions and decision-making among 

women with breast and/or ovarian cancer considering 

testing for BRCA1 and BRCA2 and their spouses. 

Psychooncology 2003;12(5):410-27. Exclude: BRCA-Q5.

Boente MP, Berchuck A, Rodriguez GC, et al. The effect of 

interferon gamma on epidermal growth factor receptor 

expression in normal and malignant ovarian epithelial cells. 

Am J Obstet Gynecol 1992;167(6):1877-82. Exclude: cell 

line only.

Bon GG, Kenemans P, Verstraeten R, et al. Serum tumor 

marker immunoassays in gynecologic oncology: 

establishment of reference values. Am J Obstet Gynecol 

1996;174(1 Pt 1):107-14. Exclude: no 2x2 table.

Bonadona V, Saltel P, Desseigne F, et al. Cancer patients 

who experienced diagnostic genetic testing for cancer 

susceptibility: reactions and behavior after the disclosure of 

a positive test result. Cancer Epidemiol Biomarkers Prev 

2002;11(1):97-104. Exclude: Q5 pre-10-2004. 

Bonome T, Lee JY, Park DC, et al. Expression profiling of 

serous low malignant potential, low-grade, and high-grade 

tumors of the ovary. Cancer Res 2005;65(22):10602-12. 

Exclude: no 2x2 table.

Boran N, Kayikcioglu F, Yalvac S, et al. Significance of 

serum and peritoneal fluid lactate dehydrogenase levels in 

ovarian cancer. Gynecol Obstet Invest 2000;49(4):272-4. 

Exclude: no 2x2 table.

Borgono CA, Grass L, Soosaipillai A, et al. Human 

kallikrein 14: a new potential biomarker for ovarian and 

breast cancer. Cancer Res 2003;63(24):9032-41. Exclude: 

cell line only.

Bosserhoff AK, Lederer M, Kaufmann M, et al. MIA, a 

novel serum marker for progression of malignant 

melanoma. Anticancer Res 1999;19(4A):2691-3. Exclude: 

not OVCA.

Bourdon V, Naef F, Rao PH, et al. Genomic and expression 

analysis of the 12p11-p12 amplicon using EST arrays 

identifies two novel amplified and overexpressed genes. 

Cancer Res 2002;62(21):6218-23. Exclude: cell line only.

Broadstock M, Michie S, Gray J, et al. The psychological 

consequences of offering mutation searching in the family 

for those at risk of hereditary breast and ovarian cancer--a 

pilot study. Psychooncology 2000;9(6):537-48. Exclude: 

Q5 pre-10-2004.

Brown RW, Campagna LB, Dunn JK, et al. 

Immunohistochemical identification of tumor markers in 

metastatic adenocarcinoma. A diagnostic adjunct in the 

determination of primary site. Am J Clin Pathol 

1997;107(1):12-9. Exclude: no 2x2 table. 

But I. Influence of tumor grade on expression of CA 125 in 

serum and tissue in patients with serous ovarian carcinoma. 

Wien Klin Wochenschr 2003;115(7-8):255-8. Exclude: no 

2x2 table. 

Cajigas HE, Fariza E, Scully RE, et al. Enhancement of 

tumor-associated glycoprotein-72 antigen expression in 

hormone-related ovarian serous borderline tumors. Cancer 

1991;68(2):348-54. Exclude: no 2x2 table. 

Callet N, Delaunay J, Pichon MF. Serum sICAM-1 (soluble 

intercellular adhesion molecule-1) and M-CSF 

(macrophage colony-stimulating growth factor) throughout 

monitoring of 34 non-serous ovarian cancers. Eur J 

Gynaecol Oncol 2000;21(2):135-40. Exclude: denominator 

NOT patients. 

Cameron RI, Ashe P, O'Rourke DM, et al. A panel of 

immunohistochemical stains assists in the distinction 

between ovarian and renal clear cell carcinoma. Int J 

Gynecol Pathol 2003;22(3):272-6. Exclude: pathology 

study.

Candido Dos Reis FJ, Moreira de Andrade J, Bighetti S. 

CA 125 and vascular endothelial growth factor in the 

differential diagnosis of epithelial ovarian tumors. Gynecol 

Obstet Invest 2002;54(3):132-6. Exclude: N=7.

Cane P, Azen C, Lopez E, et al. Tumor marker trends in 

asymptomatic women at risk for ovarian cancer: relevance 

for ovarian cancer screening. Gynecol Oncol 

1995;57(2):240-5. Exclude: no 2x2 table.

Canzonieri V, Viel A, Visentin MC, et al. Diagnostic and 

biological determinants in undifferentiated and poorly 

differentiated ovarian carcinomas. Pathologica 

1995;87(5):492-7. Exclude: no 2x2 table.
A-6

Cao D, Ji H, Ronnett BM. Expression of mesothelin, fascin, 

and prostate stem cell antigen in primary ovarian mucinous 

tumors and their utility in differentiating primary ovarian 

mucinous tumors from metastatic pancreatic mucinous 

carcinomas in the ovary. Int J Gynecol Pathol 

2005;24(1):67-72. Exclude: no 2x2 table.

Carico E, Chicchirichi R, Atlante M, et al. CA 125 in 

ovarian cysts, serous effusions and peritoneal washings: 

immunocytochemical expression. Anticancer Res 

1995;15(2):631-4. Exclude: CA125 for diagnosis.

Ceccaroni M, Chieco P, Alboni C, et al. p53 expression, 

DNA ploidy and mitotic index as prognostic factors in 

patients with epithelial ovarian carcinoma. Tumori 

2004;90(6):600-6. Exclude: no 2x2 table.

Chan JK, Loizzi V, Magistris A, et al. Differences in 

prognostic molecular markers between women over and 

under 45 years of age with advanced ovarian cancer. Clin 

Cancer Res 2004;10(24):8538-43. Exclude: no 2x2 table.

Chang HW, Ali SZ, Cho SK, et al. Detection of allelic 

imbalance in ascitic supernatant by digital single nucleotide 

polymorphism analysis. Clin Cancer Res 2002;8(8):2580-5. 

Exclude: not OVCA. 

Chatterjee M, Mohapatra S, Ionan A, et al. Diagnostic 

markers of ovarian cancer by high-throughput antigen 

cloning and detection on arrays. Cancer Res 

2006;66(2):1181-90. Exclude: no 2x2 table.

Chen LM, Yamada SD, Fu YS, et al. Molecular similarities 

between primary peritoneal and primary ovarian 

carcinomas. Int J Gynecol Cancer 2003;13(6):749-55. 

Exclude: no 2x2 table.

Cheng L, Thomas A, Roth LM, et al. OCT4: a novel 

biomarker for dysgerminoma of the ovary. Am J Surg 

Pathol 2004;28(10):1341-6. Exclude: only germ cell or 

stromal.

Chhieng DC, Yee H, Cangiarella JF, et al. Use of Ecadherin and CD44 aids in the differentiation between 

reactive mesothelial cells and carcinoma cells in pelvic 

washings. Cancer 2000;90(5):299-306. Exclude: nonspecific general prognosis.

Chien CH, Huang CC, Lin YH, et al. Detection of serum 

transforming growth factor-alpha in patients of primary 

epithelial ovarian cancers by enzyme immunoassay. 

Gynecol Oncol 1997;66(3):405-10. Exclude: non-specific 

general prognosis.

Chou YY, Jeng YM, Kao HL, et al. Differentiation of 

ovarian mucinous carcinoma and metastatic colorectal 

adenocarcinoma by immunostaining with beta-catenin. 

Histopathology 2003;43(2):151-6. Exclude: stain study.

Chudecka-Glaz A, Rzepka-Gorska I, Kosmowska B. 

Gonadotropin (LH, FSH) levels in serum and cyst fluid in 

epithelial tumors of the ovary. Arch Gynecol Obstet 

2004;270(3):151-6. Exclude: no 2x2 table.

Cioffi M, Fratta M, Gazzerro P, et al. OVCA (CA125) 

second generation: technical aspects and serum levels in 

controls, patients with liver disease, pregnant women and 

patients with ovarian disease. Tumori 1997;83(2):594-8. 

Exclude: Background only.

Ciris M, Erhan Y, Zekioglu O, et al. Inhibin alpha and beta 

expression in ovarian stromal tumors and their histological 

equivalences. Acta Obstet Gynecol Scand 2004;83(5):491-

6. Exclude: only germ cell or stromal.

Codegoni AM, Bertoni F, Patregnani C, et al. Allelic 

expression of p73 in human ovarian cancers. Ann Oncol 

1999;10(8):949-53. Exclude: no 2x2 table.

Codegoni AM, Castagna S, Mangioni C, et al. DNAtopoisomerase I activity and content in epithelial ovarian 

cancer. Ann Oncol 1998;9(3):313-9. Exclude: no 2x2 table. 

Collins Y, Tan DF, Pejovic T, et al. Identification of 

differentially expressed genes in clinically distinct groups 

of serous ovarian carcinomas using cDNA microarray. Int J 

Mol Med 2004;14(1):43-53. Exclude: non-specific general 

prognosis. 

Colombet I, Xu Y, Jaulent MC, et al. A generic 

computerized method for estimate of familial risks. Proc 

AMIA Symp 2002;175-9. Exclude: Background only.

Cooke I, O'Brien M, Charnock FM, et al. Inhibin as a 

marker for ovarian cancer. Br J Cancer 1995;71(5):1046-

50. Exclude: no 2x2 table. 

Cooper BC, Sood AK, Davis CS, et al. Preoperative CA 

125 levels: an independent prognostic factor for epithelial 

ovarian cancer. Obstet Gynecol 2002;100(1):59-64. 

Exclude: no 2x2 table. 

Coyne JC, Kruus L, Racioppo M, et al. What do ratings of 

cancer-specific distress mean among women at high risk of 

breast and ovarian cancer? Am J Med Genet A 

2003;116(3):222-8. Exclude: BRCA-Q5. 

Cruickshank DJ, Haites N, Anderson S, et al. The 

multidisciplinary management of a family with epithelial 

ovarian cancer. Br J Obstet Gynaecol 1992;99(3):226-31. 

Exclude: no data on harms. 

Crump C, McIntosh MW, Urban N, et al. Ovarian cancer 

tumor marker behavior in asymptomatic healthy women: 

implications for screening. Cancer Epidemiol Biomarkers 

Prev 2000;9(10):1107-11. Exclude: no 2x2 table. 

Davidson B, Goldberg I, Givant-Horwitz V, et al. 

Caveolin-1 expression in ovarian carcinoma is MDR1 

independent. Am J Clin Pathol 2002;117(2):225-34. 
A-7

Exclude: no 2x2 table. 

Davidson B, Goldberg I, Gotlieb WH, et al. High levels of 

MMP-2, MMP-9, MT1-MMP and TIMP-2 mRNA 

correlate with poor survival in ovarian carcinoma. Clin Exp 

Metastasis 1999;17(10):799-808. Exclude: non-specific 

general prognosis. 

Davidson B, Gotlieb WH, Ben-Baruch G, et al. Expression 

of carbohydrate antigens in advanced-stage ovarian 

carcinomas and their metastases-A clinicopathologic study. 

Gynecol Oncol 2000;77(1):35-43. Exclude: non-specific 

general prognosis. 

de Bruijn HW, ten Hoor KA, van der Zee AG. Serum and 

cystic fluid levels of soluble interleukin-2 receptor-alpha in 

patients with epithelial ovarian tumors are correlated. 

Tumour Biol 1998;19(3):160-6. Exclude: no 2x2 table. 

De Cecco L, Marchionni L, Gariboldi M, et al. Gene 

expression profiling of advanced ovarian cancer: 

characterization of a molecular signature involving 

fibroblast growth factor 2. Oncogene 2004;23(49):8171-83. 

Exclude: non-specific general prognosis. 

De Jaco P, Asselain B, Orlandi C, et al. Evaluation of 

circulating tumor necrosis factor-alpha in patients with 

gynecological malignancies. Int J Cancer 1991;48(3):375-

8. Exclude: no 2x2 table. 

de la Cuesta R, Maestro ML, Solana J, et al. Tissue 

quantification of CA 125 in epithelial ovarian cancer. Int J 

Biol Markers 1999;14(2):106-14. Exclude: no 2x2 table. 

Dehaven K, Taylor DD, Gercel-Taylor C. Comparison of 

serum vascular endothelial growth levels between patients 

with and without ovarian malignancies. Int J Gynecol 

Cancer 2002;12(6):715-9. Exclude: no 2x2 table. 

Dehn H, Hogdall EV, Johansen JS, et al. Plasma YKL-40, 

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Acta Obstet Gynecol Scand 2003;82(3):287-93. Exclude: 

recurrence prognosis. 

Demirkiran F, Kumbak B, Bese T, et al. Vascular 

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Obstet 2003;83(1):53-8. Exclude: no 2x2 table. 

den Ouden M, Ubachs JM, Stoot JE, et al. Thrombinantithrombin III and D-dimer plasma levels in patients with 

benign or malignant ovarian tumours. Scand J Clin Lab 

Invest 1998;58(7):555-9. Exclude: no 2x2 table.

Deng HB, Parekh HK, Chow KC, et al. Increased 

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Denison SR, Becker NA, Ferber MJ, et al. Transcriptional 

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Cancer 2002;34(4):406-15. Exclude: no 2x2 table.

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Diamandis EP, Borgono CA, Scorilas A, et al. 

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Exclude: cell line only. 

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1994;9(2):115-20. Exclude: no 2x2 table. 

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Oncogene 2004;23(49):8065-77. Exclude: no 2x2 table.

Donovan JT, Shiraki W, Pekow PS, et al. Reliability of 

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Exclude: non-specific general prognosis.

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general prognosis.

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Exclude: not asymptomatic women.

Emmert-Buck MR, Chuaqui R, Zhuang Z, et al. Molecular 

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not asymptomatic women.
A-8

Engelen MJ, de Bruijn HW, Hollema H, et al. Serum CA 

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Fajac A, Benard J, Lhomme C, et al. c-erbB2 gene 

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Flemming P, Wellmann A, Maschek H, et al. Monoclonal 

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A report of three cases with late metastasis, being 

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Cancer 1996;77(6):1122-30. Exclude: mathematical model.

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Gadducci A, Marrai R, Baicchi U, et al. The measurement 

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Exclude: multiple samples per patient.

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Garcia MJ, Pole JC, Chin SF, et al. A 1 Mb minimal 

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Exclude: cell line only.

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Gebauer G, Rieger M, Jager W, et al. Prognostic relevance 

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non-specific general prognosis.

Geisler JP, Geisler HE, Miller GA, et al. p53 and bcl-2 in 

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Ghimenti C, Sensi E, Presciuttini S, et al. Germline 
A-9

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Goff BA, Shy K, Greer BE, et al. Overexpression and 

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1996;17(6):487-92. Exclude: prognosis only, no link to 

mgmt or outcomes. 

Goldstein NS, Bassi D, Uzieblo A. WT1 is an integral 

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Am J Clin Pathol 2001;116(2):246-52. Exclude: no 2x2 

table.

Griaznova IM, Borisenko SA, Markarov OV, et al. [Serum 

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Exclude: non-English article.

Groisman GM, Meir A, Sabo E. The value of Cdx2 

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pathology study. 

Grosen EA, Granger GA, Gatanaga M, et al. Measurement 

of the soluble membrane receptors for tumor necrosis factor 

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not OVCA. 

Halbert CH, Schwartz MD, Wenzel L, et al. Predictors of 

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Exclude: Q5 pre-10-2004. 

Halbert CH, Wenzel L, Lerman C, et al. Predictors of 

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Hallowell N. A qualitative study of the information needs 

of high-risk women undergoing prophylactic 

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Exclude: no data on harms. 

Hanemaaijer R, Sier CF, Visser H, et al. MMP-9 activity in 

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2x2 table. 

Hartmann LC, Lu KH, Linette GP, et al. Gene expression 

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mgmt or outcomes. 

Hata K, Nagami H, Iida K, et al. Expression of thymidine 

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with microvessel density and an ultrasound-derived index 

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1998;12(3):201-6. Exclude: cell line only. 

Hazelton D, Nicosia RF, Nicosia SV. Vascular endothelial 

growth factor levels in ovarian cyst fluid correlate with 

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2x2 table. 

He QY, Zhou Y, Wong E, et al. Proteomic analysis of a 

preneoplastic phenotype in ovarian surface epithelial cells 

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2005;98(1):68-76. Exclude: cell line only. 

Hefler L, Tempfer C, Heinzl H, et al. M3/M21 serum levels 

in women with adnexal masses and inflammatory diseases. 

Int J Cancer 1998;79(4):434-8. Exclude: non-specific 

general prognosis. 

Heinzelmann-Schwarz VA, Gardiner-Garden M, Henshall 

SM, et al. A distinct molecular profile associated with 

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Hendrix ND, Wu R, Kuick R, et al. Fibroblast growth 

factor 9 has oncogenic activity and is a downstream target 

of Wnt signaling in ovarian endometrioid 

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Exclude: no 2x2 table. 

Hengstler JG, Pilch H, Schmidt M, et al. Metallothionein 

expression in ovarian cancer in relation to histopathological 

parameters and molecular markers of prognosis. Int J 

Cancer 2001;95(2):121-7. Exclude: non-specific general 

prognosis.

Hickey KP, Boyle KP, Jepps HM, et al. Molecular 

detection of tumour DNA in serum and peritoneal fluid 

from ovarian cancer patients. Br J Cancer 

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Hiorns LR, Bradshaw TD, Skelton LA, et al. Variation in 

RNA expression and genomic DNA content acquired 

during cell culture. Br J Cancer 2004;90(2):476-82. 

Exclude: cell line only. 
A-10

Hoffman BR, Katsaros D, Scorilas A, et al. 

Immunofluorometric quantitation and histochemical 

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a new independent unfavourable prognostic biomarker. Br J 

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Hogdall CK, Norgaard-Pedersen B, Mogensen O. The 

prognostic value of pre-operative serum tetranectin, CA125 and a combined index in women with primary ovarian 

cancer. Anticancer Res 2002;22(3):1765-8. Exclude: nonspecific general prognosis. 

Hogdall EV, Hogdall CK, Blaakaer J, et al. P53 

autoantibodies in sera from Danish ovarian cancer patients 

and their correlation with clinical data and prognosis. 

APMIS 2002;110(7-8):545-53. Exclude: no 2x2 table. 

Hogdall EV, Hogdall CK, Tingulstad S, et al. Predictive 

values of serum tumour markers tetranectin, OVX1, CASA 

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2000;89(6):519-23. Exclude: non-specific general 

prognosis.

Horak P, Pils D, Haller G, et al. Contribution of epigenetic 

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Exclude: no 2x2 table. 

Horak P, Pils D, Kaider A, et al. Perturbation of the tumor 

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table.

Hornung R, Urs E, Serenella E, et al. Analysis of potential 

prognostic factors in 111 patients with ovarian cancer. 

Cancer Lett 2004;206(1):97-106. Exclude: no 2x2 table.

Hough CD, Cho KR, Zonderman AB, et al. Coordinately 

up-regulated genes in ovarian cancer. Cancer Res 

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Howell D, Fitch MI, Deane KA. Impact of ovarian cancer 

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Exclude: no relevant data. 

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Huang KC, Rao PH, Lau CC, et al. Relationship of XIST 

expression and responses of ovarian cancer to 

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Exclude: cell line only.

Huddleston HG, Wong KK, Welch WR, et al. Clinical 

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83. Exclude: no 2x2 table.

Huiart L, Eisinger F, Stoppa-Lyonnet D, et al. Effects of 

genetic consultation on perception of a family risk of 

breast/ovarian cancer and determinants of inaccurate 

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2002;55(7):665-75. Exclude: BRCA-Q5. 

Hurley KE, Miller SM, Costalas JW, et al. 

Anxiety/uncertainty reduction as a motivation for interest 

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Ishikawa M, Hayashi H, Saitoh S, et al. [Clinical 

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Zasshi 1991;43(5):509-15. Exclude: non-English article.

Ishikawa M, Yaginuma Y, Hayashi H, et al. Reactivity of a 

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Ishioka S, Sagae S, Sugimura M, et al. Clinical factors and 

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Ismail RS, Baldwin RL, Fang J, et al. Differential gene 

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cell line only.

Israeli O, Goldring-Aviram A, Rienstein S, et al. In silico 

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Ivarsson K, Runesson E, Sundfeldt K, et al. The 

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Jazaeri AA, Awtrey CS, Chandramouli GV, et al. Gene 

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to mgmt or outcomes.

Jazaeri AA, Lu K, Schmandt R, et al. Molecular 

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Exclude: cell line only.

Jazaeri AA, Yee CJ, Sotiriou C, et al. Gene expression 

profiles of BRCA1-linked, BRCA2-linked, and sporadic 

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A-11

Exclude: BRCA-Q5.

Johann DJ Jr, McGuigan MD, Tomov S, et al. Novel 

approaches to visualization and data mining reveals 

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Markers 2003-2004;19(4-5):197-207. Exclude: Mass 

Spectro.

Jones MB, Krutzsch H, Shu H, et al. Proteomic analysis 

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Jones MB, Michener CM, Blanchette JO, et al. The 

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Kalir T, Wang BY, Goldfischer M, et al. 

Immunohistochemical staining of GLUT1 in benign, 

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Karppinen SM, Vuosku J, Heikkinen K, et al. No evidence 

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Katnik I, Jadach J, Kmieciak K, et al. Measurements of 

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Biochem 1995;33(10):727-32. Exclude: no 2x2 table.

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Keenan LA, Lesniak KT, Guarnaccia CA, et al. Family 

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Khalifa MA, Abdoh AA, Mannel RS, et al. P-glycoprotein 

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Kim H, Scorilas A, Katsaros D, et al. Human kallikrein 

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prognosis only, no link to mgmt or outcomes.

Kim JH, Skates SJ, Uede T, et al. Osteopontin as a 

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Exclude: Drug mech ab action. 

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no 2x2 table. 

Kohn EC, Reed E, Sarosy GA, et al. A phase I trial of 

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Kohn EC, Sandeen MA, Liotta LA. In vivo efficacy of a 

novel inhibitor of selected signal transduction pathways 

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Cancer Res 1992;52(11):3208-12. Exclude: cell line only. 

Kong F, Nicole White C, Xiao X, et al. Using proteomic 

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Koper NP, Thomas CM, Massuger LF, et al. Comparison 

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Kozlowski P, Krzyzosiak WJ. Combined SSCP/duplex 

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Exclude: background Q1. 

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Exclude: no 2x2 table. 

Kringen P, Bergamaschi A, Due EU, et al. Evaluation of 

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Krishna CM, Sockalingum GD, Venteo L, et al. Evaluation 

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Biopolymers 2005;79(5):269-76. Exclude: not genomic 

test.

Kristiansen G, Denkert C, Schluns K, et al. CD24 is 
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2002;161(4):1215-21. Exclude: cell line only.

Kumar A, Soprano DR, Parekh HK. Cross-resistance to the 

synthetic retinoid CD437 in a paclitaxel-resistant human 

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Kumar P, Rehani MM, Kumar L, et al. Tumor marker CA125 as an evaluator and response indicator in ovarian 

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Lahti-Domenici J, Rapakko K, Paakkonen K, et al. 

Exclusion of large deletions and other rearrangements in 

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families. Cancer Genet Cytogenet 2001;129(2):120-3. 

Exclude: no 2x2 table.

Lambros MB, Fiegler H, Jones A, et al. Analysis of ovarian 

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line only. 

Lancaster JM, Dressman HK, Whitaker RS, et al. Gene 

expression patterns that characterize advanced stage serous 

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Exclude: no 2x2 table. 

Lapchenkov VI, Dudarev AL, Vinokurov VL, et al. 

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Lassus H, Sihto H, Leminen A, et al. Genetic alterations 

and protein expression of KIT and PDGFRA in serous 

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Exclude: prognosis only, no link to mgmt or outcomes.

Le Page C, Ouellet V, Madore J, et al. From gene profiling 

to diagnostic markers: IL-18 and FGF-2 complement 

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cancer. Int J Cancer 2006;118(7):1750-8. Exclude: no 2x2 

table.

Lee BC, Cha K, Avraham S, et al. Microarray analysis of 

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A-13

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A-14

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A-15

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A-16

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A-17

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A-18

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A-20

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A-21

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A-22

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A-23

Appendix C: Sample Data Abstraction Forms Question 1: What is the evidence that ovarian cancer genomic tests performed in a typical clinical laboratory actually meapurported to measure?Study Study Design Description of 

Test(s) 

Patient Characteristics Results Comm

StudyID Geographical location

[city & state (U.S.) or city & country (foreign)]: Study dates [month & year]: Size of population

[give num/denom for screening studies]: Type of laboratory [delete all that do not apply]: Clinical lab Commercial lab Hospital-based clinical samples Research lab Not reported 

Genomic test(s) used:Type(s) of samples [delete all that do not apply]: Blood or tissue Cyst fluid Ascites 

Age:Mean (SD): Median: Range: Race/ethnicity (n [%]):Diagnoses (n [%]): Ovarian cancer: Borderline: Benign ovarian mass: Other (specify): Healthy controls: Inclusion criteria:Exclusion criteria:

[For each test reported, please provide a 2x2 table and report or calculate sensitivity, specificity, NPV, and PPV (all with confidence intervals); alternatively, for continuous variables, report the correlation coefficient or other measure of association. Also include data on reproducibility (interand intra-assay coefficient of variation, kappa, etc.).] 1) [2x2 table – use this header space to provide information needed for reader to interpret “Test +,” “Test -,” “Ref stand +,” and “Ref stand -“ headings in following table.] 

2) Data on other test accuracy measures (correlation coefficient, interclass correlation, etc.) 

[IF ARTEXCLUWHY H[COMMAFFECINTERPQuality [+ if apptext to dReferenVerificaTest reSample StatisticBlindingDefinitioGrade: This ar[delete QuestioQuestioQuestioQuestioQuestio
A-24

Question 2: What is the sensitivity and specificity of genomic tests in detecting ovarian cancer in asymptomatic and sympincluding high-risk women?Study Study Design Patients Clinical Presentation Results CommStudyID Geographical location

[city & state (U.S.) or city & country (foreign)]: Study dates [month & year]: Size of population

[give num/denom for screening studies]: Type of population [delete all that do not apply]: Screening Adnexal mass Other (specify) Genomic test(s) used: Reference standard [delete all that do not apply]:Surgical pathology Clinical outcome (specify) Reference standard applied to all test negatives?: Test reliability established?:

Statistical tests used:

Blinding: Definition of positive and negative on screening test: 

Age:Mean (SD): Median: Range: Menopausal status (n [%]):Pre (< 45): Peri (45-55): Post (> 55): Race/ethnicity (n [%]):Risk factors (n [%]):Family history: Genotype: Other (specify): Diagnoses (n [%]): Ovarian cancer: Borderline: Benign ovarian mass: Other (specify): Healthy controls: Inclusion criteria:Exclusion criteria:

Screening only (n [%]): Diagnosis of mass: - Symptomatic (n [%]): - Asymptomatic, detected by exam (n [%]): - Asymptomatic, detected by imaging (n [%]): Additional data used for diagnosis:

[For each test reported, please provide a 2x2 table and report or calculate sensitivity, specificity, NPV, and PPV (all with confidence intervals). Also include data on reproducibility (inter- and intra-assay coefficient of variation, kappa, etc.).] 1) [2x2 table – use this header space to provide information needed for reader to interpret Test +, Test -, Disease +, and 

Disease - headings in following table.] 

2) Data on other test accuracy measures (correlation coefficient, interclass correlation, etc.) 

[IF ARTEXCLUWHY H[COMMAFFECINTERPQuality [+ if apptext to dReferenVerificaTest reSample StatisticBlindingDefinitioGrade: This ar[delete QuestioQuestioQuestioQuestioQuestio
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Question 3: What is the evidence that genomic testing to detect ovarian cancer in asymptomatic women, including high-riclinical management and leads to improved health outcomes?Study Study Design Patients Outcome Measures Results CommStudyID Geographical location 

[city & state (U.S.) or city & country (foreign)]:

Study dates [month & 

year]: Study type [delete all but one]: RCT Cohort Case-control Other (specify) Size of population:

Genomic test(s) used: Reference standard: [delete all that do not apply]Surgical pathology Clinical outcome (specify) Test reliability established?:

Statistical tests used:

Definition of positive and negative on screening test:

Age:Mean (SD): Median: Range: Menopausal status (n [%]):Pre (< 45): Peri (45-55): Post (> 55): Race/ethnicity (n [%]):

Risk factors (n [%]):Family history: Genotype: Other (specify): Inclusion criteria:Exclusion criteria:

Use of test results: [e.g., change in screening test or frequency] Outcomes measured: [delete all that do not apply] Cancer incidence Cancer mortality Quality of life Other (specify) 

For each outcome measured, report outcomes based on test result; include 95% confidence intervals if available. 

[IF ARTEXCLUWHY H[COMMAFFECINTERPQuality [+ if apptext to dFor RCRandomBlindingDropouAdequaconceaFor cohUnbiase(prospesubjectLarge sAdequacohort: Use of test: Use of ascertaAdequaCompleAnalysiand repFor casValid asUnbiasApprop
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Study Study Design Patients Outcome Measures Results Comm

populatVerificacancer: Compawith resconfounValidatemethodAppropanalyseGrade: This ar[delete QuestioQuestioQuestioQuestioQuestio
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Question 4: What is the evidence that genomic testing in women with clinical suspicion of ovarian cancer or with already-cancer changes clinical management and leads to improved health outcomes?Study Study Design Patients Outcome Measures Results CommStudyID Geographical location 

[city & state (U.S.) or city & country (foreign)]: Study dates [month & year]: Study type [delete all but one]: RCT Cohort Case-control Other (specify) Size of population:

Genomic test(s) used: Reference standard: [delete all that do not apply]Surgical pathology Clinical outcome (specify) Test reliability established?:

Statistical tests used:

Definition of positive and negative on screening test:

Age:Mean (SD): Median: Range: Menopausal status (n [%]):Pre (< 45): Peri (45-55): Post (> 55): Race/ethnicity (n [%]):

Risk factors (n [%]):Family history: Genotype: Other (specify): Diagnoses (n [%]): Ovarian cancer: Borderline: Benign ovarian mass: Other (specify): Healthy controls: Treatment (n [%]): Surgery: Chemotherapy:  Platinum:  Taxol:  Other (specify): Other (specify): Inclusion criteria:Exclusion criteria:

Use of test results: [e.g., 

change in screening test or frequency] Outcomes measured: [delete all that do not apply] Cancer incidence Cancer mortality Quality of life Other (specify) 

[For each outcome measured, report outcomes based on test result. Note that you should only abstract data when 2x2 tables can be constructed. Articles that report only Kaplan Meier curves or Hazard 

Ratios should not be abstracted.] 1) [2x2 table - use this space to provide information needed for reader to interpret Test +, Test -, Outcome +, and Outcome - headings in following table.] 

2) [2x2 table - use this space to provide information needed for reader to interpret Test +, Test -, Outcome +, and Outcome - headings in following table.] 

[IF ARTEXCLUWHY H[COMMAFFECINTERPQuality [+ if apptext to dFor RCRandomBlindingDropouAdequaconceaFor cohUnbiase(prospesubjectLarge sAdequacohort: Use of test: Use of ascertaAdequaCompleAnalysiand repFor casValid asUnbiasApprop
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Study Study Design Patients Outcome Measures Results Comm

3) Hazard Ratio or other relevant information: 

populatVerificacancer: Compawith resconfounValidatemethodAppropanalyseGrade: This ar[delete QuestioQuestioQuestioQuestioQuestio
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Question 5: What are the harms of using genomic tests for ovarian cancer prevention and management?Study Study Design Patients Outcome Measures Results CommStudyID Geographical location 

[city & state (U.S.) or city & country (foreign)]: Study dates [month & year]: Study type [delete all but one]: RCT Cohort Case-control Other (specify) Size of population:

Genomic test(s) used: Reference standard: [delete all that do not apply]Surgical pathology Clinical outcome (specify) Test reliability established?:

Statistical tests used:

Definition of positive and negative on screening test:

Age:Mean (SD): Median: Range: Menopausal status (n [%]):Pre (< 45): Peri (45-55): Post (> 55): Race/ethnicity (n [%]):

Risk factors (n [%]):Family history: Genotype: Other (specify): Diagnoses (n [%]): Ovarian cancer: Borderline: Benign ovarian mass: Other (specify): Healthy controls: Inclusion criteria:Exclusion criteria:

Use of test results: [e.g., change in screening test or frequency] Outcomes measured: [delete all that do not apply] Complications Quality of life Other (specify) 

For each outcome measured, report outcomes based on test result. 1) 

2) 3) 4) 5) 6) 7) 

[IF ARTEXCLUWHY H[COMMAFFECINTERPQuality [+ if apptext to dFor RCRandomBlindingDropouAdequaconceaFor cohUnbiase(prospesubjectLarge sAdequacohort: Use of test: Use of ascertaAdequaCompleAnalysiand repFor casValid asUnbiasApproppopulatVerifica
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Study Study Design Patients Outcome Measures Results Comm

cancer: Compawith resconfounValidatemethodAppropanalyseGrade: This ar[delete QuestioQuestioQuestioQuestioQuestio
A-31

Appendix D: Evidence Tables Evidence Table 1 – Question 1: What is the evidence that ovarian cancer genomic tests performed in a typical clinical la

measure what they are purported to measure?

Study Study Design Description of 

Test(s) 

Patient Characteristics Results Comm

Benetkiewicz, Wang, Schaner, et al., 2005 #8570 

Geographical location:

Uppsala, Sweden; Oslo, Norway; Palo Alto, CA Study dates:

NR Size of population:

18 Type of laboratory: Hospital-based clinical samples Research lab 

Genomic test(s) used:Microarray of chromosome 22 Two methods used: DNA copy number counted, mRNA expression measured Type(s) of samples: Blood or tissue Cyst fluid Ascites 

Age: NR, but referenced

Race/ethnicity (n [%]):All Norwegian Diagnoses (n [%]): Ovarian cancer: 18 (100%) Borderline: 0 Benign ovarian mass: 0 Other: 0 Healthy controls: 0 Inclusion criteria:NR Exclusion criteria:NR 

1) Data on other test accuracy measures (correlation coefficient, interclass correlation, etc.): Abnormalities detected in 12 of 18 tumors. 21 frequently deleted genes with low mRNA expression, 12 amplified genes with elevated mRNA. 

Comme- Cance- Small Quality ReferenVerificaTest reSample StatisticBlindingDefinitioGrade: 

Brinkmann, Ryan, Ayhan, et al., 2004 #1400 

Geographical location:

London, UK; Belfast, Northern Ireland; Berlin, Germany Study dates: NR Size of population:62 Type of laboratory: Hospital-based clinical samples Research lab 

Genomic test(s) used:DNA: loss of heterozygosity, microsatellite instability Based on findings, odds that two tumors represented either single primary with metastasis or dual primary calculated. Classified as single primary if odds > 1, dual primary if odds < 1. Type(s) of samples: Blood or tissue 

Age: NR Race/ethnicity (n [%]):NR Diagnoses (n [%]): Ovarian cancer: 69 (100%)  - 38 synchronous endometrial/ovarian - 15 bilateral ovarian - 9 synchronous endometrial/bilateral ovarian Inclusion criteria:Diagnosis of synchronous tumors Exclusion criteria: 

NR

1) Data on other test accuracy measures (correlation coefficient, interclass correlation, etc.): Concordance between genetic and histopathologic diagnosis:  Endometrial/ovarian: 42%  Bilateral ovarian: 67% 

Comme- No lin- The ppatholotesting are not primary asynchQuality ReferenVerificaTest reSample StatisticBlindingDefinitio- Grade: 
A-32

Evidence Table 1 – Question 1 (continued) Study Study Design Description of 

Test(s) 

Patient Characteristics Results Comm

Conrads, Fusaro, Ross, et al., 2004 #1150 

Geographical location:

Frederick and Bethesda, MD; Chicago, IL Study dates: NR Size of population:248 Type of laboratory Hospital-based clinical samples Research lab 

Genomic test(s) used:Mass spectroscopy of protein expression using ProteinChip arrays 1) High vs. low resolution spectrometers compared 2) Candidate patterns determined using Proteome Quest software; algorithm combines elements of genetic algorithms and selforganizing adaptive pattern-recognition 3) Candidates selected in training set evaluated in blinded training set 4) Variances between assays compared to a reference standard Type(s) of samples: Blood or tissue 

Age: NRRace/ethnicity (n [%]):NR Diagnoses (n [%]): NR for entire study; for training set, 28 healthy, 49 cancer; two testing sets 37 healthy, 63 cancer; 37 healthy, 40 cancer Of total 103 cancer, 22 (20%) stage I Inclusion criteria:NR Exclusion criteria:NR 

1) Low resolution spectrometer, cancer vs. healthy: 

 Ref+ Ref- Tot T+ 103 0 103 

T- 0 67 67 Tot 103 67 170  Lower Upper  Value 95% CI 95% CI Se 100.0% 97.1% 100.0% Sp 100.0% 95.5% 100.0% PPV 100.0% 97.1% 100.0% NPV 100.0% 95.5% 100.0% 

2) High resolution spectrometer, cancer vs. healthy: 

 Ref+ Ref- Tot T+ 68 0 68 T- 0 43 43 Tot 68 43 111  Lower Upper  Value 95% CI 95% CI Se 100.0% 95.6% 100.0% Sp 100.0% 93.0% 100.0% PPV 100.0% 95.6% 100.0% NPV 100.0% 93.0% 100.0% 

3) Data on other test accuracy measures (correlation coefficient, interclass correlation, etc.): “Comparisons...revealed that the variation in the mass spectra (overall amplitude, total record count and deviation in mean amplitudes) between ovarian cancer cases and control samples was statistically indistinguishable from the variance within the process itself, as indicated by the serum reference standard.” 

Comme- Full spreporte- Prevagreater screenisituatio- ConfidsensitivpresentQuality ReferenVerificaTest re(reporteSample StatisticBlindingDefinitio+ Grade: 
A-33

Evidence Table 1 – Question 1 (continued) Study Study Design Description of 

Test(s) 

Patient Characteristics Results Comm

Filella, Ballesta, Fox, et al., 1996 #7620 

Geographical location:

Barcelona, Spain; London, UK; Graz, Austria; Chambery, France Study dates: NR Size of population:239 normal, 167 cancers Type of laboratory: Clinical lab 

Genomic test(s) used:COBAS CORE automated immunoassay analyzer Type(s) of samples: Blood or tissue 

Age:

Range: 17-89 for controls Race/ethnicity (n [%]):

NR Diagnoses (n [%]): Ovarian cancer: 167 (41.1%) Borderline: 0 Benign ovarian mass: 0 Other: 0 Healthy controls: 239 (58.9%) Inclusion criteria:NR Exclusion criteria:NR 

1) Data on other test accuracy measures (correlation coefficient, interclass correlation, etc.): Coefficent of variation: level 1 (mean 17.2) 3.8 to 6.1%, level 2 (mean 47.1) 2.8 to 6.4%, level 3 (mean 164.3) 1.8 to 4%. 97.5 percentile for CA-125 assay: 36.7; median 8.8. CA-125 levels correlated with disease stage in ovarian cancer, show response to treatment. 

CommeNone 

Quality ReferenVerificaTest reSample StatisticBlindingDefinitioGrade: 
A-34

Evidence Table 1 – Question 1 (continued) Study Study Design Description of 

Test(s) 

Patient Characteristics Results Comm

Hasholzner, Baumgartner, Steiber, et al., 1996 #7520 

Geographical location:

Munich, Germany Study dates:

NR Size of population:

426 Type of laboratory: Clinical lab Hospital-based specimens 

Genomic test(s) used:CA 72-4, CA-125 II using COBAS-CORE EIA kit Type(s) of samples: Blood or tissue 

Age: NRRace/ethnicity (n [%]):NR Diagnoses (n [%]): Ovarian cancer: At time of primary diagnosis: 123 (28.9%); during follow-up: 236 (55.4%) Borderline: 0 Benign ovarian mass: 37 (8.7%) Other: 0 Healthy controls: 30 (7.0%) Inclusion criteria:NR Exclusion criteria:NR 

1) CA-125, 31 U/mL threshold, specificity fixed at for healthy controls; controls and benign masses vs. cancer: 

 Ref+ Ref- Tot T+ 105 16 121 

T- 18 51 69 Tot 123 67 190  Lower Upper  Value 95% CI 95% CI Se 85.0% 78.7% 91.3% Sp 76.8% 66.7% 86.9% PPV 86.8% 80.7% 92.8% NPV 73.9% 63.6% 84.3% 

2) CA-72.4, threshold 2.9 U/mL, specificity fixed at 95% for healthy controls; healthy controls plus benign masses vs. cancer: 

 Ref+ Ref- Tot T+ 66 4 70 T- 57 63 120 Tot 123 67 190  Lower Upper  Value 95% CI 95% CI Se 54.0% 45.2% 62.8% Sp 94.5% 89.0% 100.0% PPV 94.3% 88.8% 99.7% NPV 52.5% 43.6% 61.4% 

3) Data on other test accuracy measures (correlation coefficient, interclass correlation, etc.): Intra-assay coefficient of variation: CA 72.4: 3.5 to 4% CA-125 II: 3.4% Inter-assay coefficient of variation: CA 72.4: 5 to 7.4% 

Comme- Prevahigher tQuality ReferenVerificaTest reSample StatisticBlindingDefinitio+ Grade: 
A-35

Evidence Table 1 – Question 1 (continued) Study Study Design Description of 

Test(s) 

Patient Characteristics Results Comm

CA-125 II: 8.4% Correlation between markers: -0.066 (controls) 0.576 (serous ovarian cancer) 

HeinzelmannSchwarz, GardinerGarden, Henshall, et al., 2004 #8650 

Geographical location:

Randwick, Australia Study dates:

NR Size of population:

158 Type of laboratory: Hospital-based clinical samples Research lab 

Genomic test(s) used:Microarray and immunohistochemistry 3 cell adhesion molecules identified Type(s) of samples: Blood or tissue 

Age:

23 (20%) < 50 Race/ethnicity (n [%]):

NR Diagnoses (n [%]): Ovarian cancer: 158 (100%) Borderline: 0 Benign ovarian mass: 0 Other: 0 Healthy controls: 0 Inclusion criteria:NR Exclusion criteria:NR 

1) Data on other test accuracy measures (correlation coefficient, interclass correlation, etc.): In univariate analysis, no marker was significantly associated with either relapsefree survival or disease-specific survival. Closest was CLDN3 (HR 0.63, 95% CI 0.4 to 1.0, p = 0.068). Scoring of expression by 2 independent readers, discrepancies resolved by consensus. 

CommeNone Quality ReferenVerificaTest reSample StatisticBlindingDefinitioGrade: 
A-36

Evidence Table 1 – Question 1 (continued) Study Study Design Description of 

Test(s) 

Patient Characteristics Results Comm

Hellström, Raycraft, HaydenLedbetter, et al., 2003 #2560 

Geographical location:

Seattle, WA Study dates: NR Size of population: 121 Type of laboratory: Research lab 

Genomic test(s) used:HE4: Gene identified through microarray as overexpressed in ovarian cancer; monoclonal antibody and ELISA generated Type(s) of samples: Blood or tissue 

Age: NRRace/ethnicity (n [%]):NR Diagnoses (n [%]): Ovarian cancer: 37 (30.6%) Borderline: 0 Benign ovarian mass: 19 (15.7%) Other: 0 Healthy controls: 65 (53.7%)Inclusion criteria:Stored sera at NCI Exclusion criteria:NR 

1) HE4 for early stage cancer vs. normal at specificity of 96% (sensitivity of CA-125 71% at this level of specificity): 

 Ref+ Ref- Tot T+ 6 3 9 

T- 1 62 63 Tot 7 65 72  Lower Upper  Value 95% CI 95% CI Se 85.7% 59.8% 100.0% Sp 95.4% 90.3% 100.0% PPV 66.7% 35.9% 97.5% NPV 98.4% 95.3% 100.0% 

2) HE4 for late stage cancer vs normal at specificity of 96% (sensitivity of CA-125 at same specificity = 80%): 

 Ref+ Ref- Tot T+ 24 3 27 T- 6 62 68 Tot 30 65 95  Lower Upper  Value 95% CI 95% CI Se 80.0% 65.7% 94.3% Sp 95.4% 90.3% 100.0% PPV 88.9% 77.0% 100.0% NPV 91.2% 84.4% 97.9% 

3) HE4 for all cancer cases vs. all benign diseases at specificity of 96% (sensitivity of CA-125 at same specificity = 40%): 

 Ref+ Ref- Tot T+ 20 1 21 T- 17 18 35 Tot 37 19 56  Lower Upper 

CommeNone Quality ReferenVerificaTest reSample StatisticBlindingDefinitio+ Grade: 
A-37

Evidence Table 1 – Question 1 (continued) Study Study Design Description of 

Test(s) 

Patient Characteristics Results Comm

 Value 95% CI 95% CI Se 54.1% 38.0% 70.1% Sp 94.7% 84.7% 100.0% PPV 95.2% 86.1% 100.0% NPV 51.4% 34.9% 68.0% 

4) Data on other test accuracy measures (correlation coefficient, interclass correlation, etc.): Sensitivity/specificity compared to CA-125 reported for fixed levels of specificity for each marker; in general, similar performance. 

Hubl, Chan, Van Ingen, et al., 1999 #5520 

Geographical location:

Dresden, Göttingen, and Mannheim, Germany; Baltimore, MD; Tokai, Japan; Rotterdam, The Netherlands; Barcelona and Asturias, Spain; Creteil, France Study dates:

NR Size of population:

NR (593 controls, N for other diseases not specified) Type of laboratory: Clinical lab Hospital-based clinical samples 

Genomic test(s) used:Elecsys® CA-125 II AssayType(s) of samples: Blood or tissue 

Age: NRRace/ethnicity (n [%]):NR Diagnoses (n [%]): Ovarian cancer: NR Borderline: NR Benign ovarian mass: NR Other: 0 Healthy controls: 593; % not calculable Inclusion criteria:NR Exclusion criteria:NR 

1) Data on other test accuracy measures (correlation coefficient, interclass correlation, etc.): Low range (10 to 20 U/mL): Intra-assay CV: 1.0 to 3.0% Interassay CV: 3.0 to 10.9% Mid-range (40 U/mL): Intra-assay CV: 0.8 to 4.6% Interassay CV: 2.4 to 8.7% Correlation with other immunoassays: 0.932 to 0.989 95 percentile for healthy subjects: 35 U/mL 

CommeNone Quality ReferenVerificaTest reSample StatisticBlindingDefinitioGrade: 
A-38

Evidence Table 1 – Question 1 (continued) Study Study Design Description of 

Test(s) 

Patient Characteristics Results Comm

Janatova, Pohlreich, and Matous, 2003 #2410 

Geographical location:

Prague, Czech Republic Study dates: NR Size of population:26 Type of laboratory: Research lab 

Genomic test(s) used:Spreadex Polymer NAB (electrophoresis gels) Type(s) of samples: Blood or tissue 

Age: NRRace/ethnicity (n [%]):NR Diagnoses (n [%]): NR: 13 (50%) with known BrCA1/2 mutations, 13 controls Inclusion criteria:NR Exclusion criteria:NR 

1) Data on other test accuracy measures (correlation coefficient, interclass correlation, etc.): Mutations detected using technique in subjects with known mutations; none in controls 

Comme- Small - No forQuality ReferenVerificaTest reSample StatisticBlindingDefinitio- Grade: 

Kenemans, Vestraeten, van Kamp, et al., 1995 #8200 

Geographical location:

Amsterdam, The Netherlands Study dates:

NR Size of population:

417 samples, from 285 patients Type of laboratory: Clinical lab Hospital-based clinical samples 

Genomic test(s) used:2nd generation CA-125 

Centocor CA-125 II BYK LIamat Boehriner Mannheim (use mouse monoclonal antibody as capture) Type(s) of samples: Blood or tissue 

Age: NR

Race/ethnicity (n [%]):NR Diagnoses (n [%]): Ovarian cancer: 216 (51.8%) Borderline: 0 Benign ovarian mass: 111 (26.6%) Other: - Endometrial CA: 24 (5.8%) - Colon CA: 22 (5.3%) - Pregnant: 44 (10.6%) Healthy controls: 0 Inclusion criteria:NR Exclusion criteria:NR 

1) Data on other test accuracy measures (correlation coefficient, interclass correlation, etc.): Regression coefficients from 0.88 to 1.17. Centocor CA-125 II: intra-assay CV 5%, inter-assay CV 7%. No differences in ROC curves. 

Comme- High pQuality ReferenVerificaTest reSample StatisticBlindingDefinitioGrade: 
A-39

Evidence Table 1 – Question 1 (continued) Study Study Design Description of 

Test(s) 

Patient Characteristics Results Comm

Li, Tang, Wu, et al., 2004 #1160 

Geographical location:

Tampa, FL Study dates: NR Size of population:3 public access data bases: I: 216 II: 216 III: 253 Type of laboratory: Research lab 

Genomic test(s) used:SELDI proteomics, analyzed using - Filtered approach with statistical testing - Wapper approach using genetic algorithms Type(s) of samples: Blood or tissue 

Age: NRRace/ethnicity (n [%]):NR Diagnoses (n [%]): Datasets I and II: Ovarian cancer: 100 (46.3%) Benign ovarian mass: 7.4% Healthy controls: 100 (46.3%) Dataset III: Ovarian cancer: 162 (64.0%) Healthy controls: 91 (36.0%)Inclusion criteria:NR Exclusion criteria:NR 

1) Filter approach (all 3 datasets pooled): 

 Dis+ Dis- Tot T+ 337 32 369 

T- 25 291 316 Tot 362 323 685  Lower Upper  Value 95% CI 95% CI Se 93.1% 90.5% 95.7% Sp 90.1% 86.8% 93.4% PPV 91.3% 88.5% 94.2% NPV 92.1% 89.1% 95.1% 

Individual datasets specificity ranged from 80.1 to 96.7%; estimated PPV based on prevalence of 0.05% ranged from 0.2 to 1.48% 2) Genetic algorithm approach (all 3 datasets pooled): 

 Dis+ Dis- Tot T+ 356 7 363 T- 6 316 322 Tot 362 323 685  Lower Upper  Value 95% CI 95% CI Se 98.3% 97.0% 99.7% Sp 97.8% 96.2% 99.4% PPV 98.1% 96.7% 99.5% NPV 98.1% 96.7% 99.6% 

Individual data sets specificity ranged from 95.4 to 100%; estimated PPV based on prevalence of 0.05% ranged from 0.92 to 100% 

Comme- Approlow prePPV; esbased o- Reporacross datasetQuality ReferenVerificaTest reSample StatisticBlindingDefinitio+ Grade: 
A-40

Evidence Table 1 – Question 1 (continued) Study Study Design Description of 

Test(s) 

Patient Characteristics Results Comm

Liu, 2006 #12810 

Geographical location:

Dallas, TX Study dates: NR Size of population:253 Type of laboratory: Research lab 

Genomic test(s) used:Type(s) of samples: Blood or tissue (reanalysis of data from Clinical Proteomic Program Databank) 

Age: NRRace/ethnicity (n [%]):NR Diagnoses (n [%]): Ovarian cancer: 162 (64%) Healthy controls: 91 (36%) Inclusion criteria:NR Exclusion criteria:NR 

1) Support Vector Machine (SVM) – Linear kernel: 

 Dis+ Dis- Tot T+ 160 7 167 

T- 2 84 86 Tot 162 91 253  Lower Upper  Value 95% CI 95% CI Se 99.0% 97.5% 100.0% Sp 92.0% 86.4% 97.6% PPV 95.8% 92.8% 98.8% NPV 97.7% 94.5% 100.0% 

2) SVM – Polynomial kernel: 

 Dis+ Dis- Tot T+ 162 19 181 T- 0 72 72 Tot 162 91 253  Lower Upper  Value 95% CI 95% CI Se 100.0% 98.1% 100.0% Sp 79.0% 70.6% 87.4% PPV 89.5% 85.0% 94.0% NPV 100.0% 95.8% 100.0% 

3) SVM – Radial kernel: 

 Dis+ Dis- Tot T+ 162 91 253 T- 0 0 0 Tot 162 91 253  Lower Upper  Value 95% CI 95% CI Se 100.0% 98.1% 100.0% Sp 0.0% 0.0% 0.0% PPV 64.0% 58.1% 69.9% NPV - - - 

CommeNone Quality ReferenVerificaTest reSample StatisticBlindingDefinitioGrade: 
A-41

Evidence Table 1 – Question 1 (continued) Study Study Design Description of 

Test(s) 

Patient Characteristics Results Comm

MeinholdHeerlein, Bauerschlag, Hilpert, et al., 2005 #8710 

Geographical location:

Kiel, Frieburg, Bonn, and Berlin, Germany; San Diego, CA Study dates: NR Size of population:57 Type of laboratory: Hospital-based clinical samples Research lab 

Genomic test(s) used:Microarray Type(s) of samples: Blood or tissue 

Age: NRRace/ethnicity (n [%]):NR Diagnoses (n [%]): Ovarian cancer: 49 (86.0%) Borderline: 8 (14.0%) Benign ovarian mass: 0 Other: 0 Healthy controls: 0 Inclusion criteria:NR Exclusion criteria:NR 

1) Data on other test accuracy measures (correlation coefficient, interclass correlation, etc.): Sensitivity/specificity not reported; predictive model based on gene expression correctly discriminated between low malignant potential/grade 1 invasive vs. grade 2 or 3 invasive 54/57 (95%). 

Comme- Survivpatients - SurvivmicroarQuality ReferenVerificaTest reSample StatisticBlindingDefinitio+ Grade: 

Mok, Chao, Skates, et al., 2001 #4140 

Geographical location:

Boston, MA; Charleston, SC Study dates: NR Size of population:201 Type of laboratory: Hospital-based clinical samples Research lab 

Genomic test(s) used:Microarray used to identify prostatin (secreted protein); antibody/ELISA developed Type(s) of samples: Blood or tissue 

Age:

< 55: 102 (55.7%) ≥ 55: 89 (44.3%) Race/ethnicity (n [%]):

NR Diagnoses (n [%]): Ovarian cancer: 64 (31.8%) Borderline: 0 Benign ovarian mass: 42 (20.9%) Other (other GYN cancers): 24 (11.9%) Healthy controls: 71(35.3%) Inclusion criteria:NR Exclusion criteria:NR 

1) Data on other test accuracy measures (correlation coefficient, interclass correlation, etc.): Correlation with CA-125 0.217 (p = 0.2) in nonmucinous cancers, -0.004 (p = -0.97) in controls; markers together had better sensitivity (92%) at specificity of 94% than either alone (CA-125 sensitivity 64.9%; prostatin 51.4% at 94% specificity). 

Comme- Small - EnrichcomparQuality ReferenVerificaTest reSample StatisticBlindingDefinitioGrade: 
A-42

Evidence Table 1 – Question 1 (continued) Study Study Design Description of 

Test(s) 

Patient Characteristics Results Comm

Mor, Visintin, Lai, et al., 2005 #240 

Geographical location:

New Haven, CT; Washington, DC; Las Vegas, NV Study dates: NR Size of population:Validation set: 206 Type of laboratory: Hospital-based clinical samples Research lab Commercial lab 

Genomic test(s) used:Microarray (cytokine rolling circle) EIA Classification by Support vector machine k-nearest neighbors classification tree Validation run 1000 times Score based classification method also used Markers selected: Leptin Prolactin OPN IGF-II Type(s) of samples: Blood or tissue 

Age:

Mean ages of groups in validation set 58.4-63 years Race/ethnicity (n [%]):

NR Diagnoses (n [%]): Ovarian cancer: 100 (48.5%) Borderline: 0 Benign ovarian mass: 0 Other (family history): 40 (19.4%) Healthy controls: 66 (32.0%)Inclusion criteria:NR Exclusion criteria:NR 

1) Scoring rule based on leptin, prolactin, OPN, and IGF-II for diagnosis of ovarian cancer (cut points empirically derived): 

 Ref+ Ref- Tot T+ 96 6 102 

T- 4 100 104 Tot 100 106 206  Lower Upper  Value 95% CI 95% CI Se 96.0% 92.2% 99.8% Sp 94.3% 89.9% 98.7% PPV 94.1% 89.6% 98.7% NPV 96.2% 92.5% 99.8% 

Comme- High pQuality ReferenVerificaTest reSample StatisticBlindingDefinitioGrade: 
A-43

Evidence Table 1 – Question 1 (continued) Study Study Design Description of 

Test(s) 

Patient Characteristics Results Comm

Petricoin, Ardekani, Hitt, et al., 2002 #3870 

Geographical location:

Bethesda, MD; Houston, TX; Lawrenceville, NJ; Chicago, IL Study dates:

NR Size of population:

117 Type of laboratory: Hospital-based clinical samples Research lab 

Genomic test(s) used:Protein profiling using mass spectroscopy Patterns identified through genetic algorithm, closer analysis Type(s) of samples: Blood or tissue 

Age:

Development set: Median: 49 Range: 21-75 Validation set: Median: 48 Range: 25-73 Race/ethnicity (n [%]):

NR Diagnoses (n [%]): Development set: Ovarian cancer: 50 (50%) Borderline: 0 Benign ovarian mass: 13 (13%) Other: 0 Healthy controls: 37 (37%) Validation set: Ovarian cancer: 50 (43.1%) Borderline: 0 Benign ovarian mass: 25 (21.6%) Other (non-gyn inflammatory disease): 7 (6.0%) Healthy controls: 24 (20.7%)Inclusion criteria:Controls: 5 years follow-up without cancer Exclusion criteria:NR 

1) Cancer vs. benign disease, peak identified: 

 Ref+ Ref- Tot T+ 50 3 53 

T- 0 63 63 Tot 50 66 116  Lower Upper  Value 95% CI 95% CI Se 100.0% 94.0% 100.0% Sp 95.5% 90.4% 100.0% PPV 94.3% 88.1% 100.0% NPV 100.0% 95.2% 100.0% 

2) Data on other test accuracy measures (correlation coefficient, interclass correlation, etc.): Coefficient of variation < 10% if specimens not frozen and thawed more than twice, and, once thawed, kept at 4°C for < 24 hours. Sera from one unaffected, one Stage III cancer run 100 times; 100% concordance. 

Comme- Popul- Prevahigher tQuality ReferenVerificaTest reSample StatisticBlindingDefinitioGrade: 
A-44

Evidence Table 1 – Question 1 (continued) Study Study Design Description of 

Test(s) 

Patient Characteristics Results Comm

Rai, Zhang, Rosenzweig, et al., 2002 #3180 

Geographical location:

Baltimore, MD; Fremont, CA Study dates: NR Size of population:81 Type of laboratory: Hospital-based clinical samples Research lab 

Genomic test(s) used:Protein profile using ProteinChip Patterns selected by classification and regression tree (CART) and unified maximum separability analysis (UMSA) Type(s) of samples: Blood or tissue 

Age:

Cancer cases: Median: 53 Range: 36-84 Controls: Median: 57 Range: 45-75 Race/ethnicity (n [%]):

NR Diagnoses (n [%]): Ovarian cancer: 43 (53%) Borderline: 0 Benign ovarian mass: 0 Other (“nongynecologic diseases”): 38 (47%) Healthy controls: 0 Inclusion criteria:NR Exclusion criteria:NR 

1) Logistic regression using 60, 79 kd peaks:

 Ref+ Ref- Tot T+ 25 2 27 

T- 17 36 53 Tot 42 38 80  Lower Upper  Value 95% CI 95% CI Se 59.5% 44.7% 74.3% Sp 94.7% 87.6% 100.0% PPV 92.6% 82.7% 100.0% NPV 67.9% 55.4% 80.5% 

2) Logistic regression model using biomarkers at 60, 79 kD plus CA-125 (>35 U/mL cutoff): 

 Ref+ Ref- Tot T+ 30 2 32 T- 2 34 36 Tot 32 36 68  Lower Upper  Value 95% CI 95% CI Se 93.8% 85.4% 100.0% Sp 94.4% 86.9% 100.0% PPV 93.8% 85.4% 100.0% NPV 94.4% 87.0% 100.0% 

Sensitivity of CA-125 alone 65.6% (95% CI 49.2 to 82.1%), specificity 97.2% (91.9 to 100%). 

Comme- Very hrelative - ReproQuality ReferenVerificaTest reSample reporteStatisticBlindingDefinitio+ Grade: 
A-45

Evidence Table 1 – Question 1 (continued) Study Study Design Description of 

Test(s) 

Patient Characteristics Results Comm

Riisbro, Stephens, Brunner, et al., 2001 #4190 

Geographical location:

Copenhagen and Hvidovre, Denmark; Russelsheim, Germany Study dates:

NR Size of population:

129 Type of laboratory: Hospital-based clinical samples Research lab 

Genomic test(s) used:RIA for soluble urokinase plasminogen activator receptor Type(s) of samples: Blood or tissue 

Age:

Median (range) for: Healthy controls: 36 (29-84) Benign gyn disease: 50 (22-73) Ovarian cancer: 63 (22-82) Race/ethnicity (n [%]):

NR Diagnoses (n [%]): Ovarian cancer: 53 (41.1%) Borderline: 0 Benign ovarian mass: 17 (13.2%) Other (benign endometrial conditions): 28 (21.0%) Healthy controls: 31 (24.0%)Inclusion criteria:NR Exclusion criteria:NR 

1) Data on other test accuracy measures (correlation coefficient, interclass correlation, etc.): Interassay CV: 7.6% Intra-assay CV: 4.6% Levels correlated with malignancy, stage of disease, but not significantly in multivariate analysis. 

Comme- High pQuality ReferenVerificaTest re

Sample StatisticBlindingDefinitioGrade: 
A-46

Evidence Table 1 – Question 1 (continued) Study Study Design Description of 

Test(s) 

Patient Characteristics Results Comm

Sapi, Okpokwasili, and Rutherford, 2002 #3630 

Geographical location:

New Haven, CT Study dates:

NR Size of population:

58 Type of laboratory: Research lab Hospital-based clinical samples 

Genomic test(s) used:Telomerase activity in peripheral cells (blood or ascites) after methods to remove peripheral leukocytes Type(s) of samples: Blood or tissue Ascites 

Age: NRRace/ethnicity (n [%]):NR Diagnoses (n [%]): Ovarian cancer: 28 (48.3%) Borderline: 0 Benign ovarian mass: 0 Other: 0 Healthy controls: 30 (51.7%)Inclusion criteria:NR Exclusion criteria:NR 

1) Data on other test accuracy measures (correlation coefficient, interclass correlation, etc.): Telomerase activity in 8/8 Stage IV, 7/20 Stage III, 0/30 controls (after purification). CA-125 levels higher in patients with positive telomerase. 

CommeNone Quality ReferenVerificaTest reSample StatisticBlindingDefinitioGrade: 

Sorace and Zahn, 2003 #9210 

Geographical location:

Baltimore, MD Study dates: NR 

Size of population:

253 Type of laboratory: Research lab 

Genomic test(s) used:Analysis of serum mass spectrometry data from Clinical Proteomics Program Databank Training set of 45 spectra from 91 controls, 80 spectra from 162 cases Test set consisted of those not selected for training set Two-sided Wilcoxon tests used to compare intensity between cancer, controls Varying decision rules applied to data Type(s) of samples: Blood or tissue 

Age: NRRace/ethnicity (n [%]):NR Diagnoses (n [%]): Ovarian cancer: 162 (64%) Controls: 91 (36%) Inclusion criteria:NR Exclusion criteria:NR 

1) Data on other test accuracy measures (correlation coefficient, interclass correlation, etc.): Sensitivity/specificity 100% in training set with different rules, but varied in test sets. Much of discrimination lies in low mass to charge (M/Z) region, which is problematic because of potential for experimental bias, technical issues. 

Comme- Prevahigher tQuality ReferenVerificaTest reSample StatisticBlindingDefinitio+ Grade: 
A-47

Evidence Table 1 – Question 1 (continued) Study Study Design Description of 

Test(s) 

Patient Characteristics Results Comm

Tamakoshi, Kikkawa, Hasegawa, et al., 1995 #8210 

Geographical location:

Nagoya, Japan Study dates:

NR Size of population:

NR (593 controls, N for other diseases not specified) Type of laboratory: Clinical lab Hospital-based clinical samples 

Genomic test(s) used:Centocore CA-125 II Assay Type(s) of samples: Blood or tissue 

Age: NRRace/ethnicity (n [%]):NR Diagnoses (n [%]): Ovarian cancer: 59 (39.6%) Borderline: 0 Benign ovarian mass: 49 (30.9%) Other: - Endometrial ca: 10 (6.7%) - Cervical ca: 14 (9.4%) Healthy controls: 20 (13.4%)Inclusion criteria:NR Exclusion criteria:NR 

1) CA-125 II: Ovarian cancer vs. benign and normal (other cancers not included): 

 Ref+ Ref- Tot T+ 53 13 66 

T- 6 66 72 Tot 59 79 138  Lower Upper  Value 95% CI 95% CI Se 89.8% 82.1% 97.5% Sp 83.5% 75.4% 91.7% PPV 80.3% 70.7% 89.9% NPV 91.7% 85.3% 98.1% 

2) CA-125 I: Ovarian cancer vs. benign and normal (other cancers not included): 

 Ref+ Ref- Tot T+ 50 9 59 T- 9 70 79 Tot 59 79 138  Lower Upper  Value 95% CI 95% CI Se 84.7% 75.6% 93.9% Sp 88.6% 81.6% 95.6% PPV 84.7% 75.6% 93.9% NPV 88.6% 81.6% 95.6% 

Difference in tests: more positives with CA125 II in endometrial cyst (?endometriosis), fewer in non-serous cancers. 3) Data on other test accuracy measures (correlation coefficient, interclass correlation, etc.): Correlation coefficient: 0.86. Coefficient of variation of CA-125 II smaller, especially at lower concentrations. 

CommeNone Quality ReferenVerificaTest reSample StatisticBlindingDefinitioGrade: 
A-48

Evidence Table 1 – Question 1 (continued) Study Study Design Description of 

Test(s) 

Patient Characteristics Results Comm

Thougaard, Hogdall, Kjaer, et al., 1998 #6610 

Geographical location:

Frederiksberg, Copenhagen, and Aarhus, Denmark Study dates: NR 

Size of population:

153 (67 men) Type of laboratory: Research lab 

Genomic test(s) used:Three different antibody (2 monoclonal, 1 polyclonal) for tetranectin Type(s) of samples: Blood or tissue 

Age:

Controls (women): Median: 36 Range: 20-59 Cancer: Median: 57.5 Range: 35-76 Race/ethnicity (n [%]):

NR Diagnoses (n [%]): Ovarian cancer: 43 (28% of total study pop; 50% of women) Borderline: 0 Benign ovarian mass: 0 Other: 0 Healthy controls: 110 (67 men, 43 women) Inclusion criteria:NR Exclusion criteria:NR 

1) Data on other test accuracy measures (correlation coefficient, interclass correlation, etc.): Coefficient of variation by level of tetranectin: Intra-assay  Hyb 130-13 Hby 130-14 A371 Low 5.6% 2.9% 7.5% Med 3.3% 2.4% 5.5% High 3.2% 1.9% 8.3% Inter-assay  Hyb 130-13 Hby 130-14 A371 Low 11.1% 12.1%  6.2% Med 8.3% 5.4%  4.9% High 8.2% 4.4%  7.9% Difference between assays 10% or less. Similar performance in ovarian cancer patients—with decreasing levels with increasing FIGO stage. 

CommeNone 

Quality ReferenVerificaTest reSample StatisticBlindingDefinitioNA Grade: 
A-49

Evidence Table 1 – Question 1 (continued) Study Study Design Description of 

Test(s) 

Patient Characteristics Results Comm

Tuxen, Soletormos, Peterson, et al., 2001 #4200 

Geographical location:

Copenhagen, Hillerod, and Odense, Denmark Study dates: NR Size of population:31 Type of laboratory: Clinical lab 

Genomic test(s) used:CA-125, CEA, TPA 16 samples obtained over course of 1 year; 4 samples within 2-3 week time period x 4 Median interval between series 12 weeks (range 9-15 weeks) Each sample run in duplicate, assays by same analyst Commercial kits used Values transformed to natural logarithm Type(s) of samples: Blood or tissue 

Age:

Median: 55 Range: 32-71 Other (menopausal status):11 (35.5%) premenopausal 20 (64.5%) postmenopausal Race/ethnicity (n [%]):

NR Diagnoses (n [%]): Ovarian cancer: 0 Borderline: 0 Benign ovarian mass: 0 Other: 0 Healthy controls: 31 (100%) Inclusion criteria:NR Exclusion criteria:NR 

1) Data on other test accuracy measures (correlation coefficient, interclass correlation, etc.): CA-125 II – contribution of different components to imprecision: Mean analytic imprecision: 7.8% Mean intraindividual variability:  Short term: 11.8%  Long term: 16.0%  Combined: 20% Change in reference value needed to be significant after accounting for imprecision: 50% Imprecision greatest in premenopausal women (69.5% compared to 35.7% in postmenopausal women), due to larger intra-individual biological variability. CEA – change in reference value needed to be significant after accounting for imprecision: 44.8% TPA – change in reference value needed to be significant after accounting for imprecision: 67.9% Differences by menopausal status not seen with CEA and TPA. 

CommeNone Quality ReferenVerificaTest reSample StatisticBlindingDefinitioGrade: 
A-50

Evidence Table 1 – Question 1 (continued) Study Study Design Description of 

Test(s) 

Patient Characteristics Results Comm

Tuxen, Soletormos, Rustin, et al., 2000 #4670 

Geographical location:

Copenhagen and Hvidovre, Denmark; Middlesex, UK Study dates:

Dec 1989-Apr 1994 Size of population:

26 subjects (225 samples) Type of laboratory: Clinical lab 

Genomic test(s) used:Cobas Core CA-125 II Collected from 3 months post-chemotherapy to 12 months before last clinical evaluation Natural logarithm transformed Type(s) of samples: Blood or tissue 

Age:

Median: 64 Range: 24-75 Race/ethnicity (n [%]):

NR Diagnoses (n [%]): Ovarian cancer: 26 (100%) Borderline: 0 Benign ovarian mass: 0 Other: 0 Healthy controls: 0 Inclusion criteria:No evidence of progression of disease using radiological or surgical follow-up Exclusion criteria:Early death Secondary cancer Treatment with monoclonal antibody Rising CA-125 levels Less than 5 available samples Continuously falling levels 

1) Data on other test accuracy measures (correlation coefficient, interclass correlation, etc.): Contribution to variability in values: Analytic imprecison: 12% Intra-individual variation: 32.0% (24.0% after exclusion of one outlier) Inter-individual variation: 43.6% Change in reference value needed to be significant after accounting for imprecision: 79.7% (62.6% after excluding one patient with outlier values). 

CommeNone Quality ReferenVerificaTest reSample StatisticBlindingDefinitioGrade: 
A-51

Evidence Table 1 – Question 1 (continued) Study Study Design Description of 

Test(s) 

Patient Characteristics Results Comm

van Ingen, Chan, Hubl, et al., 1998 #6490 

Geographical locationRotterdam,Tthe Netherlands; Baltimore, MD; Dresden, Göttingen, and Mannheim, Germany; Isehara, Japan; Barcelona and Madrid, Spain; Creteil Cedex, France Study dates: NR Size of population:1879 Type of laboratory: Clinical lab Commercial lab 

Genomic test(s) used:Automated CA-125 II assay using Elecsys 2010 (Boehringer Mannheim) Type(s) of samples: Blood or tissue 

Age:NR: all greater than 18; of normal women, 49.4% postmenopausal Race/ethnicity (n [%]):NR Diagnoses (n [%]): Ovarian cancer: 150 (7.9%) Benign ovarian mass: NR; 80 (4.2%) had benign gynecologic diseases, including cervical and endometrial conditions Other: - Benign disease (including 

non-gynecologic): 342 (18.2%) - Other cancers: 505 (26.9%) Healthy controls: - Women: 593 (31.6%) - Men: 289 (15.4%) Inclusion criteria:

NR Exclusion criteria:NR 

1) Data on other test accuracy measures (correlation coefficient, interclass correlation, etc.): Intra-assay CVs: 0.8 to 3.3% Inter-assay CVs: 2.4 to 10.9% Correlations with other assays: 0.932 to 0.989 No interference observed with high levels of bilirubin, hemoglobin, or triglycerides. 

Comme- Populcharact- Consianalytic labs Quality 

ReferenVerificaTest reSample StatisticBlindingDefinitio+ Grade: 
A-52

Evidence Table 1 – Question 1 (continued) Study Study Design Description of 

Test(s) 

Patient Characteristics Results Comm

Wen, Bernstein, Lescallett, et al., 2000 #9830 

Geographical location:

Los Angeles, CA Study dates: NR 

Size of population: 108 Type of laboratory: Hospital-based clinical samples Research lab 

Genomic test(s) used:Microarray and gel-based DNA sequencing for p53 mutations Type(s) of samples: Blood or tissue 

Age: NR, but referenced

Race/ethnicity (n [%]):NR, but ?referenced Diagnoses (n [%]): Ovarian cancer: 108 (100%)Borderline: 0 Benign ovarian mass: 0 Other: 0 Healthy controls: 0 Inclusion criteria:NR, but referenced Exclusion criteria:NR 

1) Microarray, mutation or no mutation: 

 Ref+ Ref- Tot T+ 71 0 71 

T- 6 31 37 Tot 77 31 108  Lower Upper  Value 95% CI 95% CI Se 92.2% 86.2% 98.2% Sp 100.0% 90.3% 100.0% PPV 100.0% 95.8% 100.0% NPV 83.8% 71.9% 95.7% 

2) Conventional sequence analysis, mutation or no mutation: 

 Dis+ Dis- Tot T+ 63 0 63 T- 14 31 45 Tot 77 31 108  Lower Upper  Value 95% CI 95% CI Se 81.8% 73.2% 90.4% Sp 100.0% 90.3% 100.0% PPV 100.0% 95.2% 100.0% NPV 68.9% 55.4% 82.4% 

3) Data on other test accuracy measures (correlation coefficient, interclass correlation, etc.): Mutations detected by both methods in 57 cancers, no mutations in 31, concordance 81%. 

Comme- Direct microardiscussdifferenQuality ReferenVerificaTest reSample StatisticBlindingDefinitioGrade: 
A-53

Evidence Table 1 – Question 1 (continued) Study Study Design Description of 

Test(s) 

Patient Characteristics Results Comm

Wu, Abbott, Fishman, et al., 2003 #2360 

Geographical location:

New Haven, CT; Chicago, IL Study dates: NR Size of population:91 (2 specimens not used in final analysis) Type of laboratory: Hospital-based clinical samples Research lab 

Genomic test(s) used: Mass spectroscopy for protein profiles Several different methods for selecting variables compared: 1) Random forest algorithm (bootstrap aggregation with random feature selection) 2) Classification trees (CART) 3) Linear discriminant analysis and quadratic discriminant analysis Type(s) of samples: Blood or tissue 

Age: NRRace/ethnicity (n [%]):NR Diagnoses (n [%]): Ovarian cancer: 47 (51.6%) Borderline: 0 Benign ovarian mass: 0 Other: 0 Healthy controls: 44 (47.4%)Inclusion criteria:NR Exclusion criteria:NR 

1) Data on other test accuracy measures (correlation coefficient, interclass correlation, etc.): Error rate using random forest algorithm is lower, more stable compared to CART or linear discriminant analysis. Other methods not stable using large number of variables. 

Comme- High p- Small Quality ReferenVerificaTest reSample StatisticBlindingDefinitioGrade: 

Yu, Ongarello, Fiedler, et al., 2005 #190 

Geographical locationPeking, China; Graz, Austria; Lawrence, Kansas Study dates: NR Size of population:216 Type of laboratory: Hospital-based clinical samples Research lab 

Genomic test(s) used:Mass spectrometry of proteins 4 step statistical procedure: 1) Binning, using CART 2) Kolmogorov-Smirnov (KS)-test based feature selection (comparing distribution of values) 3) Restriction of coefficient of variation 4) Wavelet transformation of data Type(s) of samples: Blood or tissue 

Age: NR

Race/ethnicity (n [%]):

NR Diagnoses (n [%]): Ovarian cancer: 121 (56%) Borderline: 0 Benign ovarian mass: 0 Other: 0 Healthy controls: 95 (44%) Inclusion criteria:NR Exclusion criteria:NR 

1) Cancer vs. control, based on classification results of described procedure:

 Ref+ Ref- Tot T+ 119 9 128 

T- 2 86 88 Tot 121 95 216  Lower Upper  Value 95% CI 95% CI Se 98.3% 96.1% 100.0% Sp 90.5% 84.6% 96.4% PPV 93.0% 88.5% 97.4% NPV 97.7% 94.6% 100.0% 

Comme- Small - Prevahigh Quality ReferenVerificaTest reSample StatisticBlindingDefinitio+ Grade: 
A-54

Evidence Table 1 – Question 1 (continued) Study Study Design Description of 

Test(s) 

Patient Characteristics Results Comm

Yu, Zheng, Tang, et al., 2005 #310 

Geographical location:

Hangzhou, China Study dates: NR Size of population:61 Type of laboratory: Hospital-based clinical samples Research lab 

Genomic test(s) used:SELDI protein profiling SVM classification used to identify candidates 90% of samples blinded training set, 10% test set; procedure repeated 10 times Type(s) of samples: Blood or tissue 

Age:

Cancer: Median: 57 Range: 14-68 Control: “Age and sex matched” Race/ethnicity (n [%]):

NR Diagnoses (n [%]): Ovarian cancer: 31 (50.8%) Borderline: 0 Benign ovarian mass: 0 Other: 0 Healthy controls: 29 (49.2%)Inclusion criteria:NR Exclusion criteria:NR 

1) Results for test set (60 iterations, but only 6 cases): 

 Ref+ Ref- Tot T+ 29 1 30 

T- 1 29 30 Tot 30 30 60  Lower Upper  Value 95% CI 95% CI Se 96.7% 90.2% 100.0% Sp 96.7% 90.2% 100.0% PPV 96.7% 90.2% 100.0% NPV 96.7% 90.2% 100.0% 

Comme- Histolo- High pset - Only 6 Quality ReferenVerificaTest reSample StatisticBlindingDefinitio- Grade: 

Zarrinkar, Mainquist, Zamora, et al., 2001 #9750 

Geographical location:

San Diego and Santa Clara, CA Study dates: 

NR Size of population:

31 patients Ovarian cancer, normal prostate, and fibroblast cell lines Type of laboratory: Commercial lab Hospital-based clinical samples Research lab 

Genomic test(s) used:High-throughput microarray using parallel analysis Results compared to single sample processing Type(s) of samples: Tissue Cell lines 

Age: NRRace/ethnicity (n [%]):NR Diagnoses (n [%]): Ovarian cancer: 27 (87.1%) Borderline: 0 Benign ovarian mass: 0 Other: 0 Healthy controls: 4 (12.9%) Inclusion criteria:NR Exclusion criteria:NR 

1) Data on other test accuracy measures (correlation coefficient, interclass correlation, etc.): Correlation between wafer vs. individual chips 0.980 (CAOV-3). Similar correlation (0.982) for a mixture of breast and prostate cell lines. False positives 31 of approximately 6800 in ovarian cell line validation. 

Comme- Few n- TissueQuality ReferenVerificaTest reSample StatisticBlindingDefinitioGrade: 
A-55

Evidence Table 1 – Question 1 (continued) Study Study Design Description of 

Test(s) 

Patient Characteristics Results Comm

Zhang, Bast, Yu, et al., 2004 #790 

Geographical location:

Baltimore, MD; Houston, Tex; Fremont, CA; Durham, NC; Randwick, Australia; Groningen, the Netherlands; London, UK Study dates: NR Size of population:Development set: 503 Type of laboratory: Hospital-based clinical samples Research lab 

Genomic test(s) used:ProteinChip Biomarker System Unified maximum separability analysis used to select peaks Identified proteins purified Testing and validation sets used Type(s) of samples: Blood or tissue 

Age: NRRace/ethnicity (n [%]):NR Diagnoses (n [%]): Ovarian cancer: 167 (33.2%) Borderline: 28 (2.8%) Benign ovarian mass: 166 (33.0%) Other: 0 Healthy controls: 142 (28.2%) Inclusion criteria:NR Exclusion criteria:NR 

1) Results of validation set, CA-125 alone, specificity fixed at 97% for healthy controls (disease – includes benign pelvic mass): 

 Ref+ Ref- Tot T+ 106 42 148 

T- 32 187 219 Tot 138 229 367  Lower Upper  Value 95% CI 95% CI Se 76.8% 69.8% 83.9% Sp 81.7% 76.6% 86.7% PPV 71.6% 64.4% 78.9% NPV 85.4% 80.7% 90.1% 

2) Results of validation set, logistic model with 3 biomarkers identified in study, specificity fixed at 97% for healthy controls (disease – includes benign pelvic mass): 

 Ref+ Ref- Tot T+ 107 82 189 T- 31 147 178 Tot 138 229 367  Lower Upper  Value 95% CI 95% CI Se 77.5% 70.6% 84.5% Sp 64.2% 58.0% 70.4% PPV 56.6% 49.5% 63.7% NPV 82.6% 77.0% 88.2% 

3) Results of validation set, logistic model with 3 biomarkers identified in study plus CA-125, specificity fixed at 97% for healthy controls (disease – includes benign pelvic mass): 

 Ref+ Ref- Tot T+ 108 93 201 T- 30 136 166 Tot 138 229 367 

Comme- High p- PopulQuality ReferenVerificaTest reSample StatisticBlindingDefinitioGrade: 
A-56

Evidence Table 1 – Question 1 (continued) Study Study Design Description of 

Test(s) 

Patient Characteristics Results Comm

 Lower Upper  Value 95% CI 95% CI Se 78.3% 71.4% 85.1% Sp 59.4% 53.0% 65.7% PPV 53.7% 46.8% 60.6% NPV 81.9% 76.1% 87.8% 

Zhu, Wang, Ma, et al., 2003 #2100 

Geographical location:

Stony Brook and Upton, NY Study dates: NR Size of population:Test set: 216 Validation: 253 Type of laboratory: Research lab 

Genomic test(s) used:Mass spectrometry (SELDI) from FDA/NCI database Random field selection of markers Type(s) of samples :Blood or tissue 

Age: NRRace/ethnicity (n [%]):NR Diagnoses (n [%]): Test set: Ovarian cancer: 100 (46.3%) Borderline: 0 Benign ovarian mass: 0 Other: 0 Healthy controls: 116 (53.7%) Validation set: Ovarian cancer: 162 (64.0%) Borderline: 0 Benign ovarian mass: 0 Other: 0 Healthy controls: 91 (36.0%)Inclusion criteria:NR Exclusion criteria:NR 

1) Validation set: 

 Ref+ Ref- Tot T+ 162 0 162 

T- 0 91 91 Tot 162 91 253  Lower Upper  Value 95% CI 95% CI Se 100.0% 98.1% 100.0% Sp 100.0% 96.7% 100.0% PPV 100.0% 98.1% 100.0% NPV 100.0% 96.7% 100.0% 

Using different training set to identify markers and 50 iterations, 50 perfect classifications, although best subset of markers differed between iterations. 

Comme- High pQuality ReferenVerificaTest reSample StatisticBlindingDefinitio+ Grade: 
A-57

Evidence Table 2 – Question 2: What is the sensitivity and specificity of genomic tests in detecting ovarian cancer in asysymptomatic women, including high-risk women?Study Study Design Patients Clinical Presentation Results CommAbdelAleem, Ahmed, Sabra, et al., 1996 #7330 

Geographical location:

Assiut, Egypt Study dates: Jun 1994-Dec 1995 Size of population:151 total, including 101 patients (48 with ovarian carcinoma; 26 with epithelial ovarian cancer) and 50 healthy controls Type of population: Adnexal mass Genomic test(s) used: Alpha-L-fucosidase Reference standard: Surgical pathology Reference standard applied to all test negatives?: Yes Test reliability established?:

2 references to assay methods Statistical tests used:

Se, Sp, PPV, NPV Blinding: No Definition of positive and negative on screening test:≤ 275 U/mL

Age:Mean (SD): 46.9 ± 1.6 

Menopausal status (n [%]):NRRace/ethnicity (n [%]):NR Risk factors (n [%]):Family history: 3 (6%) Genotype: NR Diagnoses (n [%]): Ovarian cancer: 48 (32%) Benign ovarian mass: 28 (18.5%) Healthy controls: 50 (33%) Inclusion criteria:- Women with genital tract tumors - Controls: women admitted for genital prolapse or dysfunctional uterine bleeding Exclusion criteria:NR 

Screening only (n [%]): NR Diagnosis of mass: - Symptomatic (n [%]): NR - Asymptomatic, detected by exam (n [%]): NR - Asymptomatic, detected by imaging (n [%]): NR Additional data used for diagnosis:None 

1) alpha-L-fucosidase ≤ 275 U/mL for 

diagnosis of ovarian cancer (all women with tumors): 

 Dis+ Dis- Tot T+ 43 0 43 

T- 5 28 33 Tot 48 28 76  Lower Upper  Value 95% CI 95% CI Se 89.6% 80.9% 98.2% Sp 100.0% 89.3% 100.0% PPV 100.0% 93.0% 100.0% NPV 84.8% 72.6% 97.1% 

2) alpha-L-fucosidase ≤ 275 U/mL for 

diagnosis of ovarian cancer (Dis- are healthy controls): 

 Dis+ Dis- Tot T+ 43 1 44 T- 5 49 54 Tot 48 50 98  Lower Upper  Value 95% CI 95% CI Se 89.6% 80.9% 98.2% Sp 98.0% 94.1% 100.0% PPV 97.7% 93.3% 100.0% NPV 90.7% 83.0% 98.5% 

Comme- Study comparQuality ReferenVerificaTest reSample StatisticBlindingDefinitioGrade: 
A-58

Evidence Table 2 – Question 2 (continued) Study Study Design Patients Clinical Presentation Results CommBaron, Boardman, Lafky, et al., 2005 #450 

Geographical location:

Multiple sites in U.S. Study dates:

1985-94 Size of population:

- Serum samples from 225 women with incident epithelial ovarian cancer - 246 benign ovarian neoplasm - 253 benign gynecologic condition Type of population: Screening Adnexal mass Genomic test(s) used: sEGFR/sErbB1 Reference standard: Surgical pathology Reference standard applied to all test negatives?: No Test reliability established?:

Yes Statistical tests used:

Se, Sp Blinding: No Definition of positive and negative on screening test:sEGFR < 1000 fmol/mL

Age:Ovarian cancer: Median: 61 Range: 24-87 Benign ovarian neoplasm:Median: 51 Range: 18-88 Benign gynecological conditions: Median: 42 Range: 18-84 Menopausal status (n [%]):Ovarian cancer:Pre (< 45): 35 Post (> 55): 183 Indeterminate: 7 Benign ovarian neoplasm: Pre (< 45): 108 Post (> 55): 123 Indeterminate: 15 Benign gynecological condition: Pre (< 45): 187 Post (> 55): 53 Indeterminate: 13 Race/ethnicity (n [%]):NR Risk factors (n [%]):NR Diagnoses (n [%]): Ovarian cancer: 225 Benign ovarian mass: 246Benign gynecologic condition: 253 

Screening only (n [%]): NR Diagnosis of mass: - Symptomatic (n [%]): NR - Asymptomatic, detected by exam (n [%]): NR - Asymptomatic, detected by imaging (n [%]): NR Additional data used for diagnosis:NR 

1) sEGFR < 1000 fmol/mL for diagnosis of ovarian cancer vs. patients with benign ovarian neoplasms:

 Dis+ Dis- Tot T+ 141 86 227 T- 84 160 244 Tot 225 246 471  Lower Upper  Value 95% CI 95% CI Se 62.7% 56.3% 69.0% Sp 65.0% 59.1% 71.0% PPV 62.1% 55.8% 68.4% NPV 65.6% 59.6% 71.5% 

2) sEGFR < 1000 fmol/mL OR CA-125 ≥ 50 

U/mL for diagnosis of ovarian cancer vs. patients with benign ovarian neoplasms: 

 Dis+ Dis- Tot T+ 190 90 280 T- 34 156 190 Tot 224 246 470  Lower Upper  Value 95% CI 95% CI Se 84.8% 80.1% 89.5% Sp 63.4% 57.4% 69.4% PPV 67.9% 62.4% 73.3% NPV 82.1% 76.7% 87.6% 

3) sEGFR < 1000 fmol/mL AND CA-125 ≥

50 U/mL for diagnosis of ovarian cancer vs. patients with benign ovarian neoplasms: 

 Dis+ Dis- Tot T+ 113 0 113 

T- 109 246 355 Tot 222 246 468 

CommeNone Quality ReferenVerificaTest reSample StatisticBlindingDefinitioGrade: 
A-59

Evidence Table 2 – Question 2 (continued) Study Study Design Patients Clinical Presentation Results Comm

Inclusion criteria:- Incident EOC and serum sample in repository - Controls having surgery at Mayo for benign ovarian neoplasm or other benign gynecologic condition Exclusion criteria:NR 

 Lower Upper  Value 95% CI 95% CI Se 50.9% 44.3% 57.5% 

Sp 100.0% 98.8% 100.0% PPV 100.0% 97.3% 100.0% NPV 69.3% 64.5% 74.1% 

4) Data on other test accuracy measures (correlation coefficient, interclass correlation, etc.): “Interassay biological detection limit (4.5 SD above the zero calibrator) for the ALISA done in this study was 7.5 fmol/mL sEGFR.”Not abstracted 2x2 tables also reported for:  CA-125 ≥ 33 U/L 

 CA-125 ≥ 50 U/mL 

 CA-125 ≥ 135 U/mL 
A-60

Evidence Table 2 – Question 2 (continued) Study Study Design Patients Clinical Presentation Results CommBaron, Cora, Lafky, et al., 2003 #2960 

Geographical location:

Multiple sites in U.S. (tissue banks) Study dates:

1985-2001; date ranges varied by site Size of population:

144 healthy women 225 epithelial ovarian cancer (EOC) cases Type of population: Known cancer cases and healthy controls Genomic test(s) used: sEGFR/sErbB1 Reference standard: Surgical pathology Reference standard applied to all test negatives?: No Test reliability established?:

No Statistical tests used:

Se, Sp, ROC, regression modelling Blinding: No Definition of positive and negative on screening test:Cut-off is 95% lower limit in healthy women for each group (not fixed) 

Age: NRMenopausal status (n [%]):EOC cases:Pre (< 45): 35 Post (> 55): 183 Healthy controls: Pre (< 45): 81 Post (> 55): 59 Race/ethnicity (n [%]):

NR Risk factors (n [%]):NR Diagnoses (n [%]): Ovarian cancer: 225 Healthy controls: 144 Inclusion criteria:Serum in tissue bank collected from women within 30 days of primary cytoreductive surgery for EOC Exclusion criteria:Previous cytoreductive surgery, radiation, or chemotherapy 

Screening only (n [%]): NR Diagnosis of mass: - Symptomatic (n [%]): NR - Asymptomatic, detected by exam (n [%]): NR - Asymptomatic, detected by imaging (n [%]): NR Additional data used for diagnosis:NR 

1) p110 sEGFR < 624 fmol/mL for diagnosis of EOC (all stages, all women): 

 Dis+ Dis- Tot T+ 125 8 133 T- 100 136 236 Tot 225 144 369  Lower Upper  Value 95% CI 95% CI Se 55.6% 49.1% 62.0% Sp 94.4% 90.7% 98.2% PPV 94.0% 89.9% 98.0% NPV 57.6% 51.3% 63.9% 

2) p110 sEGFR < 1185 fmol/mL for diagnosis of EOC (all stages, premenopausal women): 

 Dis+ Dis- Tot T+ 26 5 31 T- 9 76 85 Tot 35 81 116  Lower Upper  Value 95% CI 95% CI Se 74.3% 59.8% 88.8% Sp 93.8% 88.6% 99.1% PPV 83.9% 70.9% 96.8% NPV 89.4% 82.9% 96.0% 

Additional tables reported for D+ = Stage I/II or Stage III/IV and for postmenopausal women, women ages 20-40, ages 41-60 and ages 61-87 years. 

Comme- Healthmore ofEOC ca- Cut-ofanalysis Quality ReferenVerificaTest reSample StatisticBlindingDefinitioGrade: 
A-61

Evidence Table 2 – Question 2 (continued) Study Study Design Patients Clinical Presentation Results CommBerek, Chung, Kaldi, et al., 1991 #12230 

Geographical location:

Los Angeles, CA Study dates: NR 

Size of population:

36 women with EOC 12 controls Type of population:Adnexal mass Histologically proven cancer Genomic test(s) used: IL-6 CA-125 Reference standard: Surgical pathology Reference standard applied to all test negatives?: Yes Test reliability established?:

Yes (references) Statistical tests used:

Se Blinding: Yes Definition of positive and negative on screening test:“On the basis of IL-6 value of 0.12 ± 0.03 in healthy adult women.” Threshold used is unclear. 

Age: NRMenopausal status (n [%]): NRRace/ethnicity (n [%]):

NR Risk factors (n [%]):NR Diagnoses (n [%]): Ovarian cancer: 36 Healthy controls: 12 Inclusion criteria:Histologically documented epithelial ovarian cancer Exclusion criteria:NR 

Screening only (n [%]): 0 Diagnosis of mass: - Symptomatic (n [%]): NR - Asymptomatic, detected by exam (n [%]): NR - Asymptomatic, detected by imaging (n [%]): NR Additional data used for diagnosis:NR 

1) Elevated IL-6 for diagnosis of macroscopic EOC (microscopic EOC and 

control patients = Dis-): 

 Dis+ Dis- Tot T+ 16 4 20 

T- 5 23 28 Tot 21 27 48  Lower Upper  Value 95% CI 95% CI Se 76.2% 58.0% 94.4% Sp 85.2% 71.8% 98.6% PPV 80.0% 62.5% 97.5% NPV 82.1% 68.0% 96.3% 

2) Elevated IL-6 for diagnosis of EOC (microscopic or macroscopic EOC = Dis+ and control patients = Dis-): 

 Dis+ Dis- Tot T+ 18 2 20 T- 18 10 28 Tot 36 12 48  Lower Upper  Value 95% CI 95% CI Se 50.0% 33.7% 66.3% Sp 83.3% 62.2% 100.0% PPV 90.0% 76.9% 100.0% NPV 35.7% 18.0% 53.5% 

3) Data on other test accuracy measures (correlation coefficient, interclass correlation, etc.): Interassay variation 20%. Correlation of serum IL-6 levels and CA-125 levels in pts with EOC = 0.84. 

CommeNone Quality ReferenVerificaTest reSample StatisticBlindingDefinitioGrade: 
A-62

Evidence Table 2 – Question 2 (continued) Study Study Design Patients Clinical Presentation Results CommBon, Verheijen, Zuetenhorst, et al., 1996 #7470 

Geographical location:

Amsterdam, The Netherlands Study dates: NR 

Size of population:

76 malignant ovarian tumor 70 benign ovarian tumor 962 healthy controls Type of population: ScreeningGenomic test(s) used: Mucin-like Carcinomaassociated antigen Reference standard: Surgical pathology Reference standard applied to all test negatives?: No Test reliability established?:

NR Statistical tests used:

ROC. Se, Sp Blinding: NR Definition of positive and negative on screening test:14 U/mL, based on 95% in healthy controls of 19.2 U/mL 

Age:Median: 45-49 Menopausal status (n [%]):Controls n = 962Pre (< 45): 279 Peri (45-55): 503 Post (> 55): 180 Race/ethnicity (n [%]):NR Risk factors (n [%]):NR Diagnoses (n [%]): Ovarian cancer: 76 Benign ovarian mass: 70 Healthy controls: 962 Inclusion criteria:- Controls – asymptomatic volunteers participating in a screening study for early detection of ovarian cancer - Known benign ovarian tumor or ovarian carcinoma Exclusion criteria:Abnormal pelvic exam 

Screening only (n [%]): 962 (86.8%); used only to define cut-off value Diagnosis of mass: - Symptomatic (n [%]): NR - Asymptomatic, detected by exam (n [%]): NR - Asymptomatic, detected by imaging (n [%]): NR Additional data used for diagnosis:

NR 

1) CA > 14 U/mL for diagnosis of ovarian cancer (vs. benign ovarian tumors): 

 Dis+ Dis- Tot T+ 29 0 29 

T- 47 70 117 Tot 76 70 146  Lower Upper  Value 95% CI 95% CI Se 38.2% 27.2% 49.1% Sp 100.0% 95.7% 100.0% PPV 100.0% 89.7% 100.0% NPV 59.8% 50.9% 68.7% 

2) CA-125 > 35 U/mL for diagnosis of ovarian cancer (vs. benign ovarian tumors): 

 Dis+ Dis- Tot T+ 61 54 115 T- 15 16 31 Tot 76 70 146  Lower Upper  Value 95% CI 95% CI Se 80.3% 71.4% 89.2% Sp 23.2% 13.3% 33.1% PPV 53.0% 43.9% 62.2% NPV 51.6% 34.0% 69.2% 

Comme- PrevadiagnosQuality ReferenVerificaTest reSample StatisticBlindingDefinitio+/- Grade: 
A-63

Evidence Table 2 – Question 2 (continued) Study Study Design Patients Clinical Presentation Results CommChang, Lee, Goodman, et al., 2002 #3230 

Geographical location:

Baltimore, MD Study dates: NR (tumor 

bank) Size of population:

54 ovarian tumor Type of population: Adnexal mass Genomic test(s) used: Allelic Imbalance (AI) Plasma DNA levels CA-125 Reference standard: Surgical pathology Reference standard applied to all test negatives?: No Test reliability established?:

No Statistical tests used:

ROC curves, logistic regression models Blinding: No Definition of positive and negative on screening test:No 

Age: NR Menopausal status (n [%]):NRRace/ethnicity (n [%]):

NR Risk factors (n [%]):NR Diagnoses (n [%]): Ovarian cancer: 41 Borderline: 6 Other: 3 - 3 endometrioid - 2 clear cell - 1 granulosa cell - 1 immature teratoma Healthy controls: 44 164 patients with nonneoplastic diseases Inclusion criteria:Sample in tumor bank Exclusion criteria:None specified 

Screening only (n [%]): NR Diagnosis of mass: - Symptomatic (n [%]): NR - Asymptomatic, detected by exam (n [%]): NR - Asymptomatic, detected by imaging (n [%]): NR Additional data used for diagnosis:NR 

1) Allelic imbalance for diagnosis of ovarian cancer: 

 Dis+ Dis- Tot T+ 50 0 50 

T- 4 31 35 Tot 54 31 85  Lower Upper  Value 95% CI 95% CI Se 92.6% 85.6% 99.6% Sp 100.0% 90.3% 100.0% PPV 100.0% 94.0% 100.0% NPV 88.6% 78.0% 99.1% 

2) Plasma DNA concentration for diagnosis of ovarian cancer (cut off set to achieve 100% specificity): 

 Dis+ Dis- Tot T+ 29 0 29 T- 25 31 56 Tot 54 31 85  Lower Upper  Value 95% CI 95% CI Se 54.0% 40.7% 67.3% Sp 100.0% 90.3% 100.0% PPV 100.0% 89.7% 100.0% NPV 55.4% 42.3% 68.4% 

3) CA-125 > 35 U/mL for diagnosis of ovarian cancer: 

 Dis+ Dis- Tot T+ 30 2 32 T- 15 16 31 Tot 45 18 63  Lower Upper  Value 95% CI 95% CI Se 67.0% 53.3% 80.7% 

Comme- This swide rawas the data on separatQuality ReferenVerificaTest reSample StatisticBlindingDefinitioGrade: 
A-64

Evidence Table 2 – Question 2 (continued) Study Study Design Patients Clinical Presentation Results Comm

Sp 89.0% 74.5% 100.0% 

PPV 93.8% 85.4% 100.0% NPV 51.6% 34.0% 69.2% 

Cherchi, Capobianco, Ambrosini, et al., 2002 #3780 

Geographical location:

Sassari, Italy Study dates: NR 

Size of population:

44 women benign 20 women malignant Type of population: Adnexal mass Genomic test(s) used: CA-125 CA-19.9 CEA TPA CA-15.3 Reference standard: Surgical pathology Reference standard applied to all test negatives?: Yes Test reliability established?:

Referenced Statistical tests used:

Se Blinding: No Definition of positive and negative on screening test:Yes, provided (see 

Age: NR Menopausal status (n [%]): NR

Race/ethnicity (n [%]):NR Risk factors (n [%]):NR Diagnoses (n [%]): NR Inclusion criteria:Cystic ovarian tumors Exclusion criteria:NR 

Screening only (n [%]): NR Diagnosis of mass: - Symptomatic (n [%]): NR - Asymptomatic, detected by exam (n [%]): NR - Asymptomatic, detected by imaging (n [%]): NR Additional data used for diagnosis:NR 

1) CA-125 (serum) > 35 IU/mL for diagnosis of ovarian cancer (vs. benign ovarian tumor or functional ovarian cyst): 

 Dis+ Dis- Tot T+ 16 4 20 

T- 4 40 44 Tot 20 44 64  Lower Upper  Value 95% CI 95% CI Se 80.0% 62.5% 97.5% Sp 90.9% 82.4% 99.4% PPV 80.0% 62.5% 97.5% NPV 90.9% 82.4% 99.4% 

2) CA 15.3 (serum) > 30 IU/mL for diagnosis of ovarian cancer (vs. benign ovarian tumor or functional ovarian cyst): 

 Dis+ Dis- Tot T+ 10 6 16 T- 10 38 48 Tot 20 44 64  Lower Upper  Value 95% CI 95% CI Se 50.0% 28.1% 71.9% Sp 86.4% 76.2% 96.5% PPV 62.5% 38.8% 86.2% NPV 79.2% 67.7% 90.7% 

3) TPA (serum) > 70 IU/mL for diagnosis of ovarian cancer (vs. benign ovarian tumor or functional ovarian cyst): 

CommeNone Quality ReferenVerificaTest reSample StatisticBlindingDefinitioGrade: 
A-65

Evidence Table 2 – Question 2 (continued) Study Study Design Patients Clinical Presentation Results Comm

Results) Dis+ Dis- Tot 

T+ 10 8 18 

T- 10 36 46 Tot 20 44 64  Lower Upper  Value 95% CI 95% CI Se 50.0% 28.1% 71.9% Sp 81.8% 70.4% 93.2% PPV 55.6% 32.6% 78.5% NPV 78.3% 66.3% 90.2% 

4) CA 19.9 (serum) > 35 IU/mL for diagnosis of ovarian cancer (vs. benign ovarian tumor or functional ovarian cyst): 

 Dis+ Dis- Tot T+ 13 2 15 T- 7 42 49 Tot 20 44 64  Lower Upper  Value 95% CI 95% CI Se 65.0% 44.1% 85.9% Sp 95.5% 89.3% 100.0% PPV 86.7% 69.5% 100.0% NPV 85.7% 75.9% 95.5% 

5) CEA (serum) > 5 ng/mL for diagnosis of ovarian cancer (vs. benign ovarian tumor or functional ovarian cyst): 

 Dis+ Dis- Tot T+ 8 0 8 T- 12 44 56 Tot 20 44 64  Lower Upper  Value 95% CI 95% CI Se 40.0% 18.5% 61.5% Sp 100.0% 93.2% 100.0% PPV 100.0% 62.5% 100.0% NPV 78.6% 67.8% 89.3% 
A-66

Evidence Table 2 – Question 2 (continued) Study Study Design Patients Clinical Presentation Results Comm

Data also reported for same markers in intracyst fluid. 

Cooper, Ritchie, Broghammer, et al., 2002 #3360 

Geographical location:

Iowa City, IA Study dates: 1995-2000 Size of population: 151 Type of population: Adnexal mass Genomic test(s) used: Serum VEGF Reference standard: Surgical pathology Reference standard applied to all test negatives?: Yes Test reliability established?:

Commercial test Quantikine HVEGF, R&D Systems , no references provided Statistical tests used:

ROC Blinding: No Definition of positive and negative on screening test:No 

Age:Ovarian cancer: Mean (SD): 64 Range: 20-78 Menopausal status (n [%]):NRRace/ethnicity (n [%]):

NR Risk factors (n [%]):NR Diagnoses (n [%]): Ovarian cancer: 81 Borderline: 16 Benign ovarian mass: 34 Other: - 13 peritoneal cancer - 7 fallopian tube cancer Inclusion criteria:Treated on gynecologic oncology service with preoperative serum samples available Exclusion criteria:None specified 

Screening only (n [%]): NR Diagnosis of mass: - Symptomatic (n [%]): NR - Asymptomatic, detected by exam (n [%]): NR - Asymptomatic, detected by imaging (n [%]): NR Additional data used for diagnosis:NR 

1) VEGF > 246 pg/mL for diagnosis of invasive cancer (vs. LMP tumors or benign disease): 

 Dis+ Dis- Tot T+ 75 16 91 

T- 26 34 60 Tot 101 50 151  Lower Upper  Value 95% CI 95% CI Se 74.0% 65.4% 82.6% Sp 68.0% 55.1% 80.9% PPV 82.4% 74.6% 90.2% NPV 56.7% 44.1% 69.2% 

Additional data reported for CA-125 but 2x2 tables could not be determined: Se 93% Sp 71% PPV 93% NPV 68% 

CommeNone Quality ReferenVerificaTest reSample StatisticBlindingDefinitioGrade: 
A-67

Evidence Table 2 – Question 2 (continued) Study Study Design Patients Clinical Presentation Results CommCzekierdowski, 2002 #3020 

Geographical location:

Lublin, Poland Study dates: 1994-99 Size of population: 451 women with persistent adnexal mass from among 4876 women screened with TVUS Type of population: ScreeningGenomic test(s) used: VEGF (also measured CA-19.9; CA-72.4; CA125) Reference standard: Surgical pathology Reference standard applied to all test negatives?: No Test reliability established?:

No Statistical tests used:

ROC, LR Blinding: No Definition of positive and negative on screening test:No, 350 pg/mL was optimal cut-off 

Age:Mean (SD): 38 Median: 39 Range: 13-76 Menopausal status (n [%]):Post (> 55): 88 (19.5%) (47% of cancers) Race/ethnicity (n [%]):

NR Risk factors (n [%]):NR Diagnoses (n [%]): Ovarian cancer: 47 Benign ovarian mass: 404Inclusion criteria:Adnexal mass on screening TVUS Exclusion criteria:NR 

Screening only (n [%]): 100% Diagnosis of mass: - Symptomatic (n [%]): 0 - Asymptomatic, detected by exam (n [%]): 0 - Asymptomatic, detected by imaging (n [%]): 100% Additional data used for diagnosis:NR 

1) VEGF > 350 pg/mL for diagnosis of ovarian cancer: 

 Dis+ Dis- Tot T+ 13 42 55 

T- 9 52 61 Tot 22 94 116  Lower Upper  Value 95% CI 95% CI Se 59.1% 38.5% 79.6% Sp 55.3% 45.3% 65.4% PPV 23.6% 12.4% 34.9% NPV 85.2% 76.3% 94.1% 

2x2 provided for 6 other cut-offs including 100, 150, 200, 300, 450 and 600 pg/mL. AUC = 0.5895 (95% CI, 0.4505 to 0.7285). 2) Logistic regression including Doppler US and tumor markers: 

 Dis+ Dis- Tot T+ 16 8 24 T- 6 86 92 Tot 22 94 116  Lower Upper  Value 95% CI 95% CI Se 72.0% 53.2% 90.8% Sp 91.9% 86.4% 97.4% PPV 66.7% 47.8% 85.5% NPV 93.5% 88.4% 98.5% 

3) Data on other test accuracy measures (correlation coefficient, interclass correlation, etc.) VGEF: Sensitivity of the assay was 9 pg/mL. Inter-assay variability was less than 10%. 

Comme- Menobetweemasses Quality ReferenVerificaTest reSample StatisticBlindingDefinitioGrade: 
A-68

Evidence Table 2 – Question 2 (continued) Study Study Design Patients Clinical Presentation Results CommDarai, Bringuier, WalkerCombrouze, et al., 1998 #6520 

Geographical location:

Paris, France Study dates: Sep 95 - Apr 96 Size of population:77 women Type of population:Adnexal mass Genomic test(s) used: sICAM-1 sCD44std sE-cadherin Reference standard: Surgical pathology Reference standard applied to all test negatives?: Yes Test reliability established?:

Yes Statistical tests used:

Se, Sp Blinding: No Definition of positive and negative on screening test:No 

Age:Range: 18-75 Menopausal status (n [%]):NRRace/ethnicity (n [%]):

NR Risk factors (n [%]):NR Diagnoses (n [%]): Ovarian cancer: 11 Borderline: 5 Benign ovarian mass: Other: 61 - 23 luteal cyst - 9 dermoid cysts - 29 cystadenoma Inclusion criteria:Presenting with cystic ovarian mass Exclusion criteria:NR 

Screening only (n [%]): NR Diagnosis of mass: - Symptomatic (n [%]): NR - Asymptomatic, detected by exam (n [%]): NR - Asymptomatic, detected by imaging (n [%]): NR Additional data used for diagnosis:NR 

1) sE-cadherin >10,000 ng/mL for diagnosis of ovarian cancer (vs. benign cystadenomas and luteal cysts): 

 Dis+ Dis- Tot 

T+ 11 0 11 T- 5 52 57 Tot 16 52 68  Lower Upper  Value 95% CI 95% CI Se 68.8% 46.0% 91.5% Sp 100.0% 94.2% 100.0% PPV 100.0% 72.7% 100.0% NPV 91.2% 83.9% 98.6% 

No 2x2 data for sICAM-1 or sCD44std 2) Data on other test accuracy measures (correlation coefficient, interclass correlation, etc.): Interassay coefficient of variation for samples of cyst fluid assayed in duplicate by two operators was 8% to 14.5% (these figures compared well with those provided by the manufacturers for serum samples). 

CommeNone Quality ReferenVerificaTest reSample StatisticBlindingDefinitioGrade: 
A-69

Evidence Table 2 – Question 2 (continued) Study Study Design Patients Clinical Presentation Results CommDiamandis, Scorilas, Fracchioli, et al., 2003 #2850 

Geographical location:

Turin, Italy; Groningen, The Netherlands; Leuven, Belgium; Helsinki, Finland Study dates: NR 

Size of population:

384 Type of population: Adnexal mass Genomic test(s) used: Human kallikrein 6 (hK6) CA-125 Reference standard: Surgical pathology Reference standard applied to all test negatives?: No, only benign ovarian disease group (not apparently healthy controls) Test reliability established?:

Yes Statistical tests used:

Se, Sp Blinding: No Definition of positive and negative on screening test:hK6 > 4.2 μg/L (90% Sp) hK6 > 4.4 μg/L (95% Sp) 

Age:Ovarian cancer: Mean: 56 Median: 57 Range: 28-78 Benign disease: Mean: 46 Median: 45 Range: 21-76 Healthy controls: Mean: 52 Median: 49 Range: 26-72 Menopausal status (n [%]):NRRace/ethnicity (n [%]):

NR Risk factors (n [%]):NR Diagnoses (n [%]): Ovarian cancer: 146 Benign ovarian mass: 141Healthy controls: 97 Inclusion criteria:Known ovarian mass, undergoing surgery Exclusion criteria:None specified 

Screening only (n [%]): NR Diagnosis of mass: - Symptomatic (n [%]): NR - Asymptomatic, detected by exam (n [%]): NR - Asymptomatic, detected by imaging (n [%]): NR Additional data used for diagnosis:NR 

1) hK6 > 4.2 μg/L for diagnosis of ovarian cancer (vs. benigns and controls): 

 Dis+ Dis- Tot T+ 76 24 100 

T- 70 214 284 Tot 146 238 384  Lower Upper  Value 95% CI 95% CI Se 52.0% 43.9% 60.1% Sp 90.0% 86.2% 93.8% PPV 76.0% 67.6% 84.4% NPV 75.4% 70.3% 80.4% 

2) hK6 > 4.4 μg/L for diagnosis of ovarian cancer (vs. benigns and controls): 

 Dis+ Dis- Tot T+ 69 12 81 T- 77 226 303 Tot 146 238 384  Lower Upper  Value 95% CI 95% CI Se 47.0% 38.9% 55.1% Sp 95.0% 92.2% 97.8% PPV 85.2% 77.4% 92.9% NPV 74.6% 69.7% 79.5% 

3) Data on other test accuracy measures (correlation coefficient, interclass correlation, etc.): hK6 detection limit is 0.1 ug/L; dynamic range up to 50 ug/L; precision less than 10% within the measurement range. 

CommeNone Quality ReferenVerificaTest reSample StatisticBlindingDefinitioGrade: 
A-70

Evidence Table 2 – Question 2 (continued) Study Study Design Patients Clinical Presentation Results CommFayed, Ahmad, Kassim, et al., 1998 #6350 

Geographical location:

Cairo, Egypt Study dates: Mar 94 - Apr 96 Size of population: 30 women ovarian cancer 30 benign pelvic disease 30 healthy controls Type of population: Adnexal mass Genomic test(s) used: CA-125 CA-72-4 Reference standard: Surgical pathology Reference standard applied to all test negatives?: Yes, benign pelvic diseases No, healthy controls Test reliability established?:

No Statistical tests used:

Se, Sp Blinding: No Definition of positive and negative on screening test:No 

Age:Ovarian cancer: Range: 19-65 Benign pelvic disease: Range: 20-60 Healthy controls: Range: 22-63 Menopausal status (n [%]):NRRace/ethnicity (n [%]):

NR Risk factors (n [%]):NR Diagnoses (n [%]): Ovarian cancer: 30 Benign ovarian mass: 30 Healthy controls: 30 Inclusion criteria:NR Exclusion criteria:NR 

Screening only (n [%]): NR Diagnosis of mass: - Symptomatic (n [%]): NR - Asymptomatic, detected by exam (n [%]): NR - Asymptomatic, detected by imaging (n [%]): NR Additional data used for diagnosis:NR 

1) CA-125 ≥ 85 U/mL for diagnosis of 

ovarian cancer (vs. benign disease and healthy controls): 

 Dis+ Dis- Tot T+ 23 3 26 

T- 7 57 64 Tot 30 60 90  Lower Upper  Value 95% CI 95% CI Se 76.7% 61.6% 91.8% Sp 95.0% 89.5% 100.0% PPV 88.5% 76.2% 100.0% NPV 89.1% 81.4% 96.7% 

2) CA 72-4 ≥ 8.5 U/mL for diagnosis of 

ovarian cancer (vs. benign disease and healthy controls): 

 Dis+ Dis- Tot T+ 21 3 24 T- 9 57 66 Tot 30 60 90  Lower Upper  Value 95% CI 95% CI Se 70.0% 53.6% 86.4% Sp 95.0% 89.5% 100.0% PPV 87.5% 74.3% 100.0% NPV 86.4% 78.1% 94.6% 

3) CA-125 ≥ 85 U/mL OR CA 72-4 ≥ 8.5 

U/mL for diagnosis of ovarian cancer (vs. benign disease and healthy controls): 

 Dis+ Dis- Tot T+ 29 3 32 T- 2 57 59 Tot 30 60 91  Lower Upper 

CommeNone Quality ReferenVerificaTest reSample StatisticBlindingDefinitioGrade: 
A-71

Evidence Table 2 – Question 2 (continued) Study Study Design Patients Clinical Presentation Results Comm

 Value 95% CI 95% CI Se 95.0% 87.2% 100.0% Sp 95.0% 89.5% 100.0% PPV 90.6% 80.5% 100.0% NPV 96.6% 92.0% 100.0% 

Gadducci, Baicchi, Marrai, et al., 1996 #7770 

Geographical location:

Pisa, Italy Study dates: NR 

Size of population:

121 Type of population: Adnexal mass Genomic test(s) used: D-dimer CA-125 Reference standard: Surgical pathology Reference standard applied to all test negatives?: NR Test reliability established?:

NR Statistical tests used:

Mann-Whitney U test, Spearman rank correlation, logistic regression, p < 0.05 Blinding: NRDefinition of positive and negative on 

Age:Cancer: Median: 62 Range: 28-81 Benign: Median: 42 Range: 17-73 Menopausal status (n [%]):Pre (< 45): 57 (47%) Post (> 55): 64 (53%) Race/ethnicity (n [%]):

NR Risk factors (n [%]):Clinical diagnosis of ovarian mass Diagnoses (n [%]): Ovarian cancer: 56 (46%)Benign ovarian mass: 65 (54%) Inclusion criteria:Consecutive patients with clinical diagnosis of ovarian mass Exclusion criteria:Patients with cardiovascular disease, diabetes, acute or chronic inflammatory disease, previous malignancy, or 

Screening only (n [%]): NR Diagnosis of mass: - Symptomatic (n [%]): NR - Asymptomatic, detected by exam (n [%]): NR - Asymptomatic, detected by imaging (n [%]): NR Does not indicate how mass was diagnosed – just that they were going to laparotomy Additional data used for diagnosis:NR 

1) CA-125 (> 65 U/mL): 

 Dis+ Dis- Tot T+ 43 4 47 

T- 13 61 74 Tot 56 65 121  Lower Upper  Value 95% CI 95% CI Se 76.8% 65.7% 87.9% Sp 93.8% 87.9% 99.7% PPV 91.5% 83.5% 99.5% NPV 82.4% 73.8% 91.1% 

2) D-Dimer (> 416 ng/mL): 

 Dis+ Dis- Tot T+ 51 11 62 T- 5 54 59 Tot 56 65 121  Lower Upper  Value 95% CI 95% CI Se 91.1% 83.6% 98.6% Sp 83.1% 74.0% 92.2% PPV 82.3% 72.7% 91.8% NPV 91.5% 84.4% 98.6% 

3) Premenopause (CA-125): 

 Dis+ Dis- Tot T+ 8 4 12 T- 4 41 45 Tot 12 45 57 

CommeNone Quality ReferenVerificaTest reSample StatisticBlindingDefinitioGrade: 
A-72

Evidence Table 2 – Question 2 (continued) Study Study Design Patients Clinical Presentation Results Comm

screening test:D-dimer = 416 ng/mL CA-125 = 65 U/mL 

previous episodes of thrombophlebitis or thromboembolia 

 Lower Upper  Value 95% CI 95% CI Se 66.7% 40.0% 93.4% 

Sp 91.1% 82.8% 99.4% PPV 66.7% 40.0% 93.3% NPV 91.1% 82.8% 99.4% 

4) Premenopause D-Dimer: 

 Dis+ Dis- Tot T+ 12 4 16 T- 0 41 41 Tot 12 45 57  Lower Upper  Value 95% CI 95% CI Se 100.0% 75.0% 100.0% Sp 91.1% 82.8% 99.4% PPV 75.0% 53.8% 96.2% NPV 100.0% 92.7% 100.0% 

5) Postmenopause CA-125: 

 Dis+ Dis- Tot T+ 35 0 35 T- 9 20 29 Tot 44 20 64  Lower Upper  Value 95% CI 95% CI Se 79.5% 67.6% 91.4% Sp 100.0% 85.0% 100.0% PPV 100.0% 91.4% 100.0% NPV 69.0% 52.1% 85.8% 

6) Postmenopause D-Dimer: 

 Dis+ Dis- Tot T+ 39 7 46 T- 5 13 18 Tot 44 20 64 
A-73

Evidence Table 2 – Question 2 (continued) Study Study Design Patients Clinical Presentation Results Comm

 Lower Upper  Value 95% CI 95% CI Se 88.6% 79.2% 98.0% 

Sp 65.0% 44.1% 85.9% PPV 84.8% 74.4% 95.2% NPV 72.2% 51.5% 92.9% 

Gorelik, Landsittel, Marrangoni, et al., 2005 #350 

Geographical location:

Cleveland, OH Study dates: NR 

Size of population:

126 Type of population: Screening Genomic test(s) used: Cytokines CA-125 Reference standard: NR Reference standard applied to all test negatives?: NR Test reliability established?:

Yes Statistical tests used:

Wilcoxon rank sum Spearman CART Blinding: NR Definition of positive and negative on 

Age:Control: Median: 46 Range: 36-76 Early cancer: Median: 46 Range: 34-88 Benign: Median: 44.5 Range: 28-87 Menopausal status (n [%]):NRRace/ethnicity (n [%]):NR Risk factors (n [%]):NRDiagnoses (n [%]): Ovarian cancer: 44 (35%)Benign ovarian mass: 37 (29%) Healthy controls: 45 (36%) Inclusion criteria:

NR Exclusion criteria:NR 

Screening only (n [%]): NR Diagnosis of mass: - Symptomatic (n [%]): NR - Asymptomatic, detected by exam (n [%]): NR - Asymptomatic, detected by imaging (n [%]): NR Additional data used for diagnosis:NR 

1) CA-125: 

 Dis+ Dis- Tot T+ 42 21 63 

T- 2 61 63 Tot 44 82 126  Lower Upper  Value 95% CI 95% CI Se 95.5% 89.4% 100.0% Sp 74.4% 65.0% 83.8% PPV 66.7% 55.0% 78.3% NPV 96.8% 92.5% 100.0% 

2) IL-6: 

 Dis+ Dis- Tot T+ 37 11 48 T- 7 71 78 Tot 44 82 126  Lower Upper  Value 95% CI 95% CI Se 84.1% 73.3% 94.9% Sp 86.0% 78.5% 93.5% PPV 77.1% 65.2% 89.0% NPV 91.0% 84.7% 97.4% 

3) EGF: 

 Dis+ Dis- Tot T+ 37 19 56 T- 7 63 70 Tot 44 82 126 

Comme- Cannoused to specificQuality ReferenVerificaTest reSample StatisticBlindingDefinitioGrade: 
A-74

Evidence Table 2 – Question 2 (continued) Study Study Design Patients Clinical Presentation Results Comm

screening test:CA-125 < 28 Il-6 < 0.4 IL-8 < 5.2 EGF < 149.3 VEGF < 126.5 

 Lower Upper  Value 95% CI 95% CI Se 84.1% 73.3% 94.9% 

Sp 76.7% 67.5% 85.9% PPV 66.1% 53.7% 78.5% NPV 90.0% 83.0% 97.0% 

4) IL-8: 

 Dis+ Dis- Tot T+ 39 25 64 T- 5 57 62 Tot 44 82 126  Lower Upper  Value 95% CI 95% CI Se 88.6% 79.2% 98.0% Sp 69.8% 59.9% 79.7% PPV 60.9% 49.0% 72.9% NPV 91.9% 85.2% 98.7% 

5) MCP: 

 Dis+ Dis- Tot T+ 37 23 60 T- 7 59 66 Tot 44 82 126  Lower Upper  Value 95% CI 95% CI Se 84.1% 73.3% 94.9% Sp 72.1% 62.4% 81.8% PPV 61.7% 49.4% 74.0% NPV 89.4% 82.0% 96.8% 

6) VEGF: 

 Dis+ Dis- Tot T+ 35 27 62 T- 9 55 64 Tot 44 82 126 
A-75

Evidence Table 2 – Question 2 (continued) Study Study Design Patients Clinical Presentation Results Comm

 Lower Upper  Value 95% CI 95% CI Se 79.5% 67.6% 91.4% Sp 67.4% 57.3% 77.5% PPV 56.5% 44.1% 68.8% NPV 85.9% 77.4% 94.5% 

7) G-CSF: 

 Dis+ Dis- Tot T+ 32 21 53 T- 12 61 73 Tot 44 82 126  Lower Upper  Value 95% CI 95% CI Se 72.7% 59.5% 85.9% Sp 74.4% 65.0% 83.8% PPV 60.4% 47.2% 73.5% NPV 83.6% 75.1% 92.1% 
A-76

Evidence Table 2 – Question 2 (continued) Study Study Design Patients Clinical Presentation Results CommHasholzner, Baumgartner, Stieber, et al., 1996 #7520 

Geographical location:

Munich, Germany Study dates: NR 

Size of population:

426 Type of population: Screening Genomic test(s) used: CA-125 CA-72-4 Reference standard: Surgical pathology Clinical outcome Reference standard applied to all test negatives?: NR Test reliability established?:

NR Statistical tests used:NR Blinding: NRDefinition of positive and negative on screening test:CA-125: 160 U/mL CA-72-4: 3 U/mL 

Age: NR Menopausal status (n [%]):NRRace/ethnicity (n [%]):

NR Risk factors (n [%]):NR Diagnoses (n [%]): Ovarian cancer: 359 (84%) Benign ovarian mass: 37 (8.6%) Healthy controls: 30 (7%) Inclusion criteria:NR Exclusion criteria:NR 

Screening only (n [%]): NR Diagnosis of mass: - Symptomatic (n [%]): NR - Asymptomatic, detected by exam (n [%]): NR - Asymptomatic, detected by imaging (n [%]): NR Additional data used for diagnosis:NR 

1) CA-125 for healthy vs. cancer: 

 Dis+ Dis- Tot T+ 64 0 64 

T- 59 30 89 Tot 123 30 153  Lower Upper  Value 95% CI 95% CI Se 52.0% 43.2% 60.8% Sp 100.0% 90.0% 100.0% PPV 100.0% 95.3% 100.0% NPV 33.7% 23.9% 43.5% 

2) CA-125 for benign vs. cancer: 

 Dis+ Dis- Tot T+ 64 1 65 T- 59 36 95 Tot 123 37 160  Lower Upper  Value 95% CI 95% CI Se 52.0% 43.2% 60.8% Sp 97.0% 91.5% 100.0% PPV 98.5% 95.5% 100.0% NPV 37.9% 28.1% 47.7% 

3) CA 72-4 for healthy vs. cancer: 

 Dis+ Dis- Tot T+ 66 1 67 T- 57 29 86 Tot 123 30 153  Lower Upper  Value 95% CI 95% CI Se 54.0% 45.2% 62.8% Sp 97.0% 90.9% 100.0% PPV 98.5% 95.6% 100.0% NPV 33.7% 23.7% 43.7% 

CommeNone Quality ReferenVerificaTest reSample StatisticBlindingDefinitioGrade: 
A-77

Evidence Table 2 – Question 2 (continued) Study Study Design Patients Clinical Presentation Results Comm

4) CA 72-4 for benign vs. cancer: 

 Dis+ Dis- Tot T+ 66 1 67 

T- 57 36 93 Tot 123 37 160  Lower Upper  Value 95% CI 95% CI Se 54.0% 45.2% 62.8% Sp 97.0% 91.5% 100.0% PPV 98.5% 95.6% 100.0% NPV 38.7% 28.8% 48.6% 
A-78

Evidence Table 2 – Question 2 (continued) Study Study Design Patients Clinical Presentation Results CommHefler, Mayerhofer, Nardi, et al., 2000 #5050 

Geographical location:

Vienna, Austria Study dates: Dec 1992-Mar 1999 Size of population:117 Type of population: Screening Genomic test(s) used: Serum soluble Fas levels Reference standard: Surgical pathology Clinical outcome Reference standard applied to all test negatives?: NRTest reliability established?:YesStatistical tests used:

Fas levels CA-125 Blinding: NRDefinition of positive and negative on screening test:Fas: 3.69 ng/mL CA-125: 409 U/mL 

Age:Cancer: Median: 57 Range: 29-87 Benign: Median: 50 Range: 24-79 Healthy: Median: 39 Range: 23-58 Menopausal status (n [%]):NR Race/ethnicity (n [%]):NRRisk factors (n [%]):NR Diagnoses (n [%]): Ovarian cancer: 52 (44%)Benign ovarian mass: 30 (26%) Healthy controls: 35 (30%) Inclusion criteria:

Consecutive women with stage I, II, or III ovarian cancer Exclusion criteria:Borderline ovarian cancer 

Screening only (n [%]): NR Diagnosis of mass:NR - Symptomatic (n [%]): - Asymptomatic, detected by exam (n [%]): - Asymptomatic, detected by imaging (n [%]): Additional data used for diagnosis:Follow-up of women with cancer 

1) Fas: 

 Dis+ Dis- Tot T+ 28 3 31 

T- 24 62 86 Tot 52 65 117  Lower Upper  Value 95% CI 95% CI Se 53.0% 39.4% 66.6% Sp 95.0% 89.7% 100.0% PPV 90.3% 79.9% 100.0% NPV 72.1% 62.6% 81.6% 

2) CA-125: 

 Dis+ Dis- Tot T+ 37 3 40 T- 15 62 77 Tot 52 65 117  Lower Upper  Value 95% CI 95% CI Se 71.0% 58.7% 83.3% Sp 95.0% 89.7% 100.0% PPV 92.5% 84.3% 100.0% NPV 80.5% 71.7% 89.4% 

3) CA-125 and Fas combined: 

 Dis+ Dis- Tot T+ 42 3 45 T- 10 62 72 Tot 52 65 117  Lower Upper  Value 95% CI 95% CI Se 81.0% 70.3% 91.7% Sp 95.0% 89.7% 100.0% PPV 93.3% 86.0% 100.0% NPV 86.1% 78.1% 94.1% 

CommeNone Quality ReferenVerificaTest reSample StatisticBlindingDefinitioGrade: 
A-79

Evidence Table 2 – Question 2 (continued) Study Study Design Patients Clinical Presentation Results CommHibbs, Skubitz, Pambuccian, et al., 2004 #9030 

Geographical location:

Minneapolis, MN Study dates: NR 

Size of population:

87 Type of population: Screening Genomic test(s) used: Gene expression B8 integrin BMP-7 Claudin-4 Col ix a2 CRABp-1 FOX J1 S100A1 Reference standard: Surgical pathology Clinical outcome Reference standard applied to all test negatives?: Yes Test reliability established?:YesStatistical tests used:

Youden’s misclassification; pairwise tissue comparisons; Wilcoxon-Mann-Whitney; linear logistic regression

Blinding: Yes Definition of positive and negative on 

Age:Healthy: Mean: 51 Range: 32-79 Serous papillary ovarian cancer: Mean: 57.6 Range: 29-79 Serous papillary ovarian cancer to the omentum: Mean: 59.7 Range: 29-79 Menopausal status (n [%]):NRRace/ethnicity (n [%]):NR Risk factors (n [%]):NRDiagnoses (n [%]): Ovarian cancer: 37 (43%)Healthy controls: 50 (57%) Inclusion criteria:None had been treated with chemotherapy before resection Exclusion criteria:NR 

Screening only (n [%]): NR Diagnosis of mass: - Symptomatic (n [%]): NR - Asymptomatic, detected by exam (n [%]): NR - Asymptomatic, detected by imaging (n [%]): NR Additional data used for diagnosis:

NR 

1) B8 integrin (normal vs. ovarian carcinoma): 

 Dis+ Dis- Tot T+ 16 7 23 

T- 4 43 47 Tot 20 50 70  Lower Upper  Value 95% CI 95% CI Se 80.0% 62.5% 97.5% Sp 86.7% 77.3% 96.1% PPV 69.6% 50.8% 88.4% NPV 91.5% 83.5% 99.5% 

2) B8 integrin (normal vs. metastatic ovarian carcinoma): 

 Dis+ Dis- Tot T+ 14 7 21 T- 3 43 46 Tot 17 50 67  Lower Upper  Value 95% CI 95% CI Se 80.0% 61.0% 99.0% Sp 86.7% 77.3% 96.1% PPV 66.7% 46.5% 86.8% NPV 93.5% 86.3% 100.0% 

3) BMP-7 (normal vs. ovarian carcinoma): 

 Dis+ Dis- Tot T+ 12 17 29 T- 8 33 41 Tot 20 50 70  Lower Upper  Value 95% CI 95% CI Se 60.0% 38.5% 81.5% Sp 66.7% 53.6% 79.8% PPV 41.4% 23.5% 59.3% 

CommeNone Quality ReferenVerificaTest reSample StatisticBlindingDefinitioGrade: 
A-80

Evidence Table 2 – Question 2 (continued) Study Study Design Patients Clinical Presentation Results Comm

screening test: +++ maximum positive ++ moderate + weak +/- faint or questionable - lack of staining 

NPV 80.5% 68.4% 92.6% 

4) BMP-7 (normal vs. metastatic ovarian carcinoma): 

 Dis+ Dis- Tot T+ 10 17 27 

T- 7 33 40 Tot 17 50 67  Lower Upper  Value 95% CI 95% CI Se 60.0% 36.7% 83.3% Sp 66.7% 53.6% 79.8% PPV 37.0% 18.8% 55.3% NPV 82.5% 70.7% 94.3% 

5) Claudin-4 (normal vs. ovarian carcinoma): 

 Dis+ Dis- Tot T+ 20 3 23 T- 0 47 47 Tot 20 50 70  Lower Upper  Value 95% CI 95% CI Se 99.3% 85.0% 100.0% Sp 93.3% 86.4% 100.0% PPV 87.0% 73.2% 100.0% NPV 100.0% 93.6% 100.0% 

6) Claudin-4 (normal vs. metastatic ovarian carcinoma): 

 Dis+ Dis- Tot T+ 17 3 20 T- 0 47 47 Tot 17 50 67  Lower Upper  Value 95% CI 95% CI 
A-81

Evidence Table 2 – Question 2 (continued) Study Study Design Patients Clinical Presentation Results Comm

Se 100.0% 82.4% 100.0% Sp 93.3% 86.4% 100.0% PPV 85.0% 69.4% 100.0% NPV 100.0% 93.6% 100.0% 

7) COL Ix a2 (normal vs. ovarian carcinoma): 

 Dis+ Dis- Tot T+ 4 0 4 T- 16 50 66 Tot 20 50 70  Lower Upper  Value 95% CI 95% CI Se 20.0% 2.5% 37.5% Sp 100.0% 94.0% 100.0% PPV 100.0% 25.0% 100.0% NPV 75.8% 65.4% 86.1% 

8) COL Ix a2 (normal vs. metastatic ovarian carcinoma): 

 Dis+ Dis- Tot T+ 3 0 3 T- 14 50 64 Tot 17 50 67  Lower Upper  Value 95% CI 95% CI Se 20.0% 1.0% 39.0% Sp 100.0% 94.0% 100.0% PPV 100.0% 0.0% 100.0% NPV 78.1% 68.0% 88.3% 

9) CRABP-1 (normal vs. ovarian carcinoma): 

 Dis+ Dis- Tot T+ 11 23 34 T- 9 27 36 Tot 20 50 70 
A-82

Evidence Table 2 – Question 2 (continued) Study Study Design Patients Clinical Presentation Results Comm

 Lower Upper  Value 95% CI 95% CI Se 53.3% 31.4% 75.2% Sp 53.3% 39.5% 67.1% PPV 32.4% 16.6% 48.1% NPV 75.0% 60.9% 89.1% 

10) CRABP-1 (normal vs. metastatic ovarian carcinoma): 

 Dis+ Dis- Tot T+ 2 0 2 T- 15 50 65 Tot 17 50 67  Lower Upper  Value 95% CI 95% CI Se 13.3% 0.0% 29.4% Sp 100.0% 94.0% 100.0% PPV 100.0% 0% 100.0% NPV 76.9% 66.7% 87.2% 

11) FOX J1 (normal vs. ovarian carcinoma):

 Dis+ Dis- Tot T+ 19 33 52 T- 1 17 18 Tot 20 50 70  Lower Upper  Value 95% CI 95% CI Se 93.3% 82.3% 100.0% Sp 33.3% 20.2% 46.4% PPV 36.5% 23.5% 49.6% NPV 94.4% 83.9% 100.0% 

12) FOX J1 (normal vs. metastatic ovarian carcinoma): 

 Dis+ Dis- Tot T+ 17 33 50 
A-83

Evidence Table 2 – Question 2 (continued) Study Study Design Patients Clinical Presentation Results Comm

T- 0 17 17 Tot 17 50 67  Lower Upper  Value 95% CI 95% CI Se 100.0% 82.4% 100.0% Sp 33.3% 20.2% 46.4% PPV 34.0% 20.9% 47.1% NPV 100.0% 82.4% 100.0% 

13) S100A1 (normal vs. ovarian carcinoma): 

 Dis+ Dis- Tot T+ 20 20 40 T- 0 30 30 Tot 20 50 70  Lower Upper  Value 95% CI 95% CI Se 100.0% 85.0% 100.0% Sp 60.0% 46.4% 73.6% PPV 50.0% 34.5% 65.5% NPV 100.0% 90.0% 100.0% 

14) S100A1 (normal vs. metastatic ovarian carcinoma): 

 Dis+ Dis- Tot T+ 16 20 36 T- 1 30 31 Tot 17 50 67  Lower Upper  Value 95% CI 95% CI Se 93.3% 81.4% 100.0% Sp 60.0% 46.4% 73.6% PPV 44.4% 28.2% 60.7% NPV 96.8% 90.6% 100.0% 
A-84

Evidence Table 2 – Question 2 (continued) Study Study Design Patients Clinical Presentation Results CommHofmann and Ruschenburg, 2002 #3610 

Geographical location:

Göttingen Germany Study dates: NR 

Size of population:

44 Type of population: Screening Genomic test(s) used: BAGE MAGE-1 MAGE-3 GAGE-1/2 Reference standard: Surgical pathology Clinical outcome Reference standard applied to all test negatives?: Yes Test reliability established?:Yes Statistical tests used:

Sensitivity, specificity 

Blinding: NR 

Definition of positive and negative on screening test:Presence or absence of gene 

Age:Median: 68 Range: 36-82 Menopausal status (n [%]):NR Race/ethnicity (n [%]):NRRisk factors (n [%]):NRDiagnoses (n [%]): Ovarian cancer: 27 (61%)Healthy controls: 17 (39%) Inclusion criteria:Samples sent to department of cytology for routine diagnosis; clinically suspected ovarian carcinoma Exclusion criteria:NR 

Screening only (n [%]): NR Diagnosis of mass: - Symptomatic (n [%]): NR - Asymptomatic, detected by exam (n [%]): NR - Asymptomatic, detected by imaging (n [%]): NR Additional data used for diagnosis:NR 

1) BAGE: 

 Dis+ Dis- Tot T+ 16 1 17 

T- 11 16 27 Tot 27 17 44  Lower Upper  Value 95% CI 95% CI Se 60.0% 41.5% 78.5% Sp 94.0% 82.7% 100.0% PPV 94.1% 82.9% 100.0% NPV 59.3% 40.7% 77.8% 

2) MAGE-1: 

 Dis+ Dis- Tot T+ 2 0 2 T- 25 17 42 Tot 27 17 44  Lower Upper  Value 95% CI 95% CI Se 8.0% 0.0% 18.2% Sp 100.0% 82.4% 100.0% PPV 100.0% 0% 100.0% NPV 40.5% 25.6% 55.3% 

3) MAGE-3: 

 Dis+ Dis- Tot T+ 9 0 9 T- 18 17 35 Tot 27 17 44  Lower Upper  Value 95% CI 95% CI Se 32.0% 14.4% 49.6% Sp 100.0% 82.4% 100.0% PPV 100.0% 66.7% 100.0% NPV 48.6% 32.0% 65.1% 

CommeNone Quality ReferenVerificaTest reSample StatisticBlindingDefinitioGrade: 
A-85

Evidence Table 2 – Question 2 (continued) Study Study Design Patients Clinical Presentation Results Comm

4) GAGE-1/2: 

 Dis+ Dis- Tot T+ 9 0 9 

T- 18 17 35 Tot 27 17 44  Lower Upper  Value 95% CI 95% CI Se 32.0% 14.4% 49.6% Sp 100.0% 82.4% 100.0% PPV 100.0% 66.7% 100.0% NPV 48.6% 32.0% 65.1% 
A-86

Evidence Table 2 – Question 2 (continued) Study Study Design Patients Clinical Presentation Results CommHurteau, Woolas, Jacobs, et al., 1995 #7890 

Geographical location:

London, England Study dates: NR 

Size of population:

192 Type of population: Screening Genomic test(s) used: sIL-2Rα

CA-125 Reference standard: Surgical pathology Clinical outcome Reference standard applied to all test negatives?: Yes Test reliability established?:

YesStatistical tests used:

Student’s t test, p<0.05 Blinding: NRDefinition of positive and negative on screening test:sIL-2Rα = 650 U/mL 

CA-125 = 35 U/mL 

Age: NRMenopausal status (n [%]):NR Race/ethnicity (n [%]):NR Risk factors (n [%]):NR: Diagnoses (n [%]): Ovarian cancer: 39 (20%)Benign ovarian mass: 61 (32%) Healthy controls: 92 (48%) Inclusion criteria:Gynecologic masses and healthy controls Exclusion criteria:NR 

Screening only (n [%]): NR 

Diagnosis of mass: - Symptomatic (n [%]): NR - Asymptomatic, detected by exam (n [%]): NR - Asymptomatic, detected by imaging (n [%]): NR Additional data used for diagnosis:NR 

1) sIL-2Rα: 

 Dis+ Dis- Tot T+ 37 58 95 

T- 2 3 5 Tot 39 61 100  Lower Upper  Value 95% CI 95% CI Se 94.9% 87.9% 100.0% Sp 4.9% 0.0% 10.3% PPV 38.9% 29.1% 48.8% NPV 60.0% 17.1% 100.0% 

2) sIL-2Rα +CA-125: 

 Dis+ Dis- Tot T+ 34 44 78 T- 5 17 22 Tot 39 61 100  Lower Upper  Value 95% CI 95% CI Se 87.2% 76.7% 97.7% Sp 27.9% 16.6% 39.1% PPV 43.6% 32.6% 54.6% NPV 77.3% 59.8% 94.8% 

Values were reversed in table. Unable to duplicate sensitivity value. 

CommeNone Quality ReferenVerificaTest reSample StatisticBlindingDefinitioGrade: 
A-87

Evidence Table 2 – Question 2 (continued) Study Study Design Patients Clinical Presentation Results CommInaba, Negishi, Fukasawa, et al., 1995 #7960 

Geographical location:

Tokyo, Japan Study dates: NR Size of population:215 Type of population: Screening Genomic test(s) used: Cytokeratin fragment 21 (CYFRA 21-1) CA-125 SCC Reference standard: Surgical pathology Clinical outcome Reference standard applied to all test negatives?: NR Test reliability established?:

Yes Statistical tests used:

X^2 test with Yates correction, t test, p<0.05 Blinding: NR Definition of positive and negative on screening test:CYFRA 21-1 = 1.9 ng/mL CA-125 = 35 U/mL SCC = 1.5 ng/mL 

Age:Healthy: Range: 18-53 Benign: Range: 19-55 Cancer: Range: 23-69 Menopausal status (n [%]):NR Race/ethnicity (n [%]):

NR Risk factors (n [%]):NR Diagnoses (n [%]): Ovarian cancer: 75 (35%)Benign ovarian mass: 38 (18%) Healthy controls: 102 (47%) Inclusion criteria:NR Exclusion criteria:NR 

Screening only (n [%]): NR Diagnosis of mass: - Symptomatic (n [%]): NR - Asymptomatic, detected by exam (n [%]): NR - Asymptomatic, detected by imaging (n [%]): NR Additional data used for diagnosis:NR 

1) CYFRA 21-1: 

 Dis+ Dis- Tot T+ 48 3 51 

T- 27 137 164 Tot 75 140 215  Lower Upper  Value 95% CI 95% CI Se 64.0% 53.1% 74.9% Sp 97.9% 95.5% 100.0% PPV 94.1% 87.7% 100.0% NPV 83.5% 77.9% 89.2% 

2) CA-125: 

 Dis+ Dis- Tot T+ 44 20 64 T- 31 120 151 Tot 75 140 215  Lower Upper  Value 95% CI 95% CI Se 58.7% 47.5% 69.8% Sp 85.7% 79.9% 91.5% PPV 68.8% 57.4% 80.1% NPV 79.5% 73.0% 85.9% 

CommeNone Quality ReferenVerificaTest reSample StatisticBlindingDefinitioGrade: 
A-88

Evidence Table 2 – Question 2 (continued) Study Study Design Patients Clinical Presentation Results CommKim, Herlyn, Wong, et al., 2003 #9230 

Geographical location:

Boston, MA Study dates:

1992-2000 Size of population:

104 Type of population: Screening Genomic test(s) used: Ep-CAM Reference standard: Surgical pathology Reference standard applied to all test negatives?: Yes Test reliability established?:Yes Statistical tests used:

Mean, SD, 95% CI; Mann-Whitney U test Blinding: Yes – to clinical data Definition of positive and negative on screening test:0.140 cutoff

Age:Mean (SD): 58 Range: 45-76 Menopausal status (n [%]):NRRace/ethnicity (n [%]):NRRisk factors (n [%]):NRDiagnoses (n [%]): Ovarian cancer: 52 (50%)Benign ovarian mass: 26 (25%) Healthy controls: 26 (25%) Inclusion criteria:NR Exclusion criteria:NR 

Screening only (n [%]): NR Diagnosis of mass: - Symptomatic (n [%]): NR - Asymptomatic, detected by exam (n [%]): NR - Asymptomatic, detected by imaging (n [%]): NR Additional data used for diagnosis:NR 

1) EP-CAM cutoff 0.140: 

 Dis+ Dis- Tot T+ 22 2 24 

T- 30 50 80 Tot 52 52 104  Lower Upper  Value 95% CI 95% CI Se 42.3% 28.9% 55.7% Sp 96.2% 90.9% 100.0% PPV 91.7% 80.6% 100.0% NPV 62.5% 51.9% 73.1% 

2) EP-CAM cutoff 0.115: 

 Dis+ Dis- Tot T+ 38 11 49 T- 14 41 55 Tot 52 52 104  Lower Upper  Value 95% CI 95% CI Se 73.1% 61.0% 85.1% Sp 78.8% 67.7% 89.9% PPV 77.6% 65.9% 89.2% NPV 74.5% 63.0% 86.1% 

3) CA-125 cutoff 35 U/mL: 

 Dis+ Dis- Tot T+ 45 6 51 T- 7 46 53 Tot 52 52 104  Lower Upper  Value 95% CI 95% CI Se 86.5% 77.2% 95.8% Sp 88.5% 79.8% 97.2% PPV 88.2% 79.4% 97.1% NPV 86.8% 77.7% 95.9% 

CommeNone Quality ReferenVerificaTest reSample StatisticBlindingDefinitioGrade: 
A-89

Evidence Table 2 – Question 2 (continued) Study Study Design Patients Clinical Presentation Results Comm

4) Data on other test accuracy measures (correlation coefficient, interclass correlation, etc.): A patient with positive EP-CAM has 11.4-fold risk of normal women in the diagnosis of ovarian cancer (odds ratio = 11.4, CI = 3.6 to 36.1, relative risk = 3.8) in comparison with CA-125 (OR = 49.3, CI = 11.6 to 208.6, RR = 7.5). 
A-90

Evidence Table 2 – Question 2 (continued) Study Study Design Patients Clinical Presentation Results CommKozak, Amneus, Pusey, et al., 2003 #2220 

Geographical location:

Los Angeles, CA Study dates: NR 

Size of population:

184 Type of population: Screening Genomic test(s) used: Biomarker panels Reference standard: Surgical pathology Reference standard applied to all test negatives?: Yes Test reliability established?:YesStatistical tests used:

Proteinchip data analysis software Blinding: NR Definition of positive and negative on screening test:NR

Age: NR Menopausal status (n [%]):NRRace/ethnicity (n [%]):NRRisk factors (n [%]):NRDiagnoses (n [%]): Ovarian cancer: 109 (59%) Benign ovarian mass: 19 (10%) Healthy controls: 56 (30%) Inclusion criteria:NR Exclusion criteria:NR 

Screening only (n [%]): NR Diagnosis of mass: - Symptomatic (n [%]): NR - Asymptomatic, detected by exam (n [%]): NR - Asymptomatic, detected by imaging (n [%]): NR Additional data used for diagnosis:NR 

1) Screening panel #1 (when Se equals Sp):

 Dis+ Dis- Tot T+ 94 10 104 

T- 15 65 80 Tot 109 75 184  Lower Upper  Value 95% CI 95% CI Se 86.2% 79.7% 92.7% Sp 87.0% 79.4% 94.6% PPV 90.4% 84.7% 96.1% NPV 81.3% 72.7% 89.8% 

2) Screening panel #1 (highest accuracy): 

 Dis+ Dis- Tot T+ 104 13 117 T- 5 62 67 Tot 109 75 184  Lower Upper  Value 95% CI 95% CI Se 95.7% 91.9% 99.5% Sp 82.6% 74.0% 91.2% PPV 88.9% 83.2% 94.6% NPV 92.5% 86.2% 98.8% 

3) Validation panel #1 (when Se equals Sp):

 Dis+ Dis- Tot T+ 93 11 104 T- 16 64 80 Tot 109 75 184  Lower Upper  Value 95% CI 95% CI Se 85.2% 78.5% 91.9% Sp 84.7% 76.6% 92.8% PPV 89.4% 83.5% 95.3% NPV 80.0% 71.2% 88.8% 

CommeNone Quality ReferenVerificaTest reSample StatisticBlindingDefinitioGrade: 
A-91

Evidence Table 2 – Question 2 (continued) Study Study Design Patients Clinical Presentation Results Comm

4) Validation panel #1 (highest accuracy): 

 Dis+ Dis- Tot T+ 89 4 93 

T- 20 71 91 Tot 109 75 184  Lower Upper  Value 95% CI 95% CI Se 81.5% 74.2% 88.8% Sp 94.9% 89.9% 99.9% PPV 95.7% 91.6% 99.8% NPV 78.0% 69.5% 86.5% 

5) Validation panel #2 (when Se equals Sp):

 Dis+ Dis- Tot T+ 89 14 103 T- 20 61 81 Tot 109 75 184  Lower Upper  Value 95% CI 95% CI Se 81.5% 74.2% 88.8% Sp 81.4% 72.6% 90.2% PPV 86.4% 79.8% 93.0% NPV 75.3% 65.9% 84.7% 

6) Validation panel #2 (highest accuracy): 

 Dis+ Dis- Tot T+ 79 4 83 T- 30 71 101 Tot 109 75 184  Lower Upper  Value 95% CI 95% CI Se 72.8% 64.4% 81.2% Sp 94.9% 89.9% 99.9% PPV 95.2% 90.6% 99.8% NPV 70.3% 61.4% 79.2% 
A-92

Evidence Table 2 – Question 2 (continued) Study Study Design Patients Clinical Presentation Results CommLee, Park, Jung, et al., 2005 #270 

Geographical location:

Seoul, South Korea Study dates:

1994-2002 Size of population:

201 Type of population: Screening Genomic test(s) used: P53 P21 Bax Bcl-2 GADD45 Cyclin E CDK2 PCNA MDM2 Reference standard: Surgical pathology Reference standard applied to all test negatives?: NR Test reliability established?:NRStatistical tests used: Cochran-MantelHaenszel chi-square test and Cochran-Armitage trend test Blinding: Yes Definition of positive and negative on 

Age:Median: 51 Range: 16-86 Menopausal status (n [%]):NRRace/ethnicity (n [%]):NR Risk factors (n [%]):NRDiagnoses (n [%]): Ovarian cancer: 104 Borderline: 37 Benign ovarian mass: 60 Inclusion criteria:NR Exclusion criteria:NR 

Screening only (n [%]): NR Diagnosis of mass: - Symptomatic (n [%]): NR - Asymptomatic, detected by exam (n [%]): NR - Asymptomatic, detected by imaging (n [%]): NR Additional data used for diagnosis:NR 

Using > 50% positivity: 1) P53: 

 Dis+ Dis- Tot T+ 30 0 30 T- 74 97 171 Tot 104 97 201  Lower Upper  Value 95% CI 95% CI Se 28.8% 20.1% 37.6% Sp 100.0% 96.9% 100.0% PPV 100.0% 90.0% 100.0% NPV 56.7% 49.3% 64.2% 

2) P21: 

 Dis+ Dis- Tot T+ 7 5 12 T- 97 92 189 Tot 104 97 201  Lower Upper  Value 95% CI 95% CI Se 6.7% 1.9% 11.5% Sp 94.8% 90.4% 99.2% PPV 58.3% 30.4% 86.2% NPV 48.7% 41.6% 55.8% 

3) Bax: 

 Dis+ Dis- Tot T+ 34 10 44 T- 70 87 157 Tot 104 97 201  Lower Upper  Value 95% CI 95% CI Se 32.7% 23.7% 41.7% Sp 89.7% 83.6% 95.7% PPV 77.3% 64.9% 89.7% 

CommeNone Quality ReferenVerificaTest reSample StatisticBlindingDefinitioGrade: 
A-93

Evidence Table 2 – Question 2 (continued) Study Study Design Patients Clinical Presentation Results Comm

screening test:0-5% positivity 6-49% positivity ≥ 50% positivity

NPV 55.4% 47.6% 63.2% 

4) Bcl-2: 

 Dis+ Dis- Tot T+ 13 15 28 

T- 91 82 173 Tot 104 97 201  Lower Upper  Value 95% CI 95% CI Se 12.5% 6.1% 18.9% Sp 84.5% 77.3% 91.7% PPV 46.4% 28.0% 64.9% NPV 47.4% 40.0% 54.8% 

5) GADD45: 

 Dis+ Dis- Tot T+ 9 21 30 T- 95 76 171 Tot 104 97 201  Lower Upper  Value 95% CI 95% CI Se 8.7% 3.3% 14.1% Sp 78.4% 70.2% 86.5% PPV 30.0% 13.6% 46.4% NPV 44.4% 37.0% 51.9% 

6) Cyclin E: 

 Dis+ Dis- Tot T+ 12 0 12 T- 92 97 189 Tot 104 97 201  Lower Upper  Value 95% CI 95% CI Se 11.5% 5.4% 17.7% Sp 100.0% 96.9% 100.0% PPV 100.0% 75.0% 100.0% 
A-94

Evidence Table 2 – Question 2 (continued) Study Study Design Patients Clinical Presentation Results Comm

NPV 51.3% 44.2% 58.4% 

7) CDK2: 

 Dis+ Dis- Tot T+ 9 0 9 T- 95 97 192 Tot 104 97 201  Lower Upper  Value 95% CI 95% CI Se 8.7% 3.3% 14.1% Sp 100.0% 96.9% 100.0% PPV 100.0% 66.7% 100.0% NPV 50.5% 43.4% 57.6% 

8) MDM2: 

 Dis+ Dis- Tot T+ 11 34 45 T- 93 63 156 Tot 104 97 201  Lower Upper  Value 95% CI 95% CI Se 10.6% 4.7% 16.5% Sp 64.9% 55.5% 74.4% PPV 24.4% 11.9% 37.0% NPV 40.4% 32.7% 48.1% 
A-95

Evidence Table 2 – Question 2 (continued) Study Study Design Patients Clinical Presentation Results CommLuo, Bunting, Scorilas, et al., 2001 #4490 

Geographical location:

Toronto, Ontario, Canada Study dates: NR 

Size of population:

122 Type of population: Screening Genomic test(s) used: Hk10 Reference standard: Surgical pathology Reference standard applied to all test negatives?: NR Test reliability established?:YesStatistical tests used:

Pearson correlation coefficient, MannWhitney test, ROC, AUC Blinding: NR Definition of positive and negative on screening test:Hk10 > 1.5 μg/L Hk10 > 0.8 μg/L 

Age: NR Menopausal status (n [%]):NRRace/ethnicity (n [%]):NRRisk factors (n [%]):NRDiagnoses (n [%]): Ovarian cancer: 80 (66%) Healthy controls: 42 (34%) Inclusion criteria:

CA-125 > 372 Ku/l for patients with cancer Exclusion criteria:NR 

Screening only (n [%]): NR Diagnosis of mass: - Symptomatic (n [%]): NR - Asymptomatic, detected by exam (n [%]): NR - Asymptomatic, detected by imaging (n [%]): NR Additional data used for diagnosis:NR 

1) hk10 > 1.5 μg/L: 

 Dis+ Dis- Tot T+ 45 0 45 

T- 35 42 77 Tot 80 42 122  Lower Upper  Value 95% CI 95% CI Se 56.3% 45.4% 67.1% Sp 100.0% 92.9% 100.0% PPV 100.0% 93.3% 100.0% NPV 54.5% 43.4% 65.7% 

2) hk10 > 0.8 μg/L: 

 Dis+ Dis- Tot T+ 62 0 62 T- 18 42 60 Tot 80 42 122  Lower Upper  Value 95% CI 95% CI Se 77.5% 68.3% 86.7% Sp 100.0% 92.9% 100.0% PPV 100.0% 95.2% 100.0% NPV 70.0% 58.4% 81.6% 

3) ROC curve analysis (AUC 0.92,0.88-0.96): Weak correlation between serum hk10 and CA-125 in ovarian cancer patients (r = 0.23, p = 0.04). 

CommeNone Quality ReferenVerificaTest reSample StatisticBlindingDefinitioGrade: 
A-96

Evidence Table 2 – Question 2 (continued) Study Study Design Patients Clinical Presentation Results CommMabrouk and AliLabib, 2003 #2200 

Geographical location: 

Cairo, Egypt Study dates: NR Size of population:60 Type of population: Ad hoc Genomic test(s) used: uPAR c-erbB-2 CA-125 CA-15.3 Reference standard: Surgical pathology Reference standard applied to all test negatives?: No Test reliability established?:YesStatistical tests used:

Pearson correlation Fisher’s exact test Blinding: NR Definition of positive and negative on screening test:Negatives defined as:uPAR: 0.1 ng/mL c-erbB-2: +/1 CA-15.3: < 25 U/mL CA-125: < 35 U/mL 

Age:Ovarian cancer: Mean (SD): 46 (11.97) Benign ovarian mass: Mean (SD): 45.7 (12.95) Healthy controls: Mean (SD): 46 (15.21) Menopausal status (n [%]):NR Race/ethnicity (n [%]):Egyptian 100% Risk factors (n [%]):NR Diagnoses (n [%]): Ovarian cancer: 20 (33.3%) Benign ovarian mass: 20 (33.3%) Healthy controls: 20 (33.3%) Inclusion criteria:NR Exclusion criteria:NR 

Screening only (n [%]): Not applicable Diagnosis of mass: - Symptomatic (n [%]): NR - Asymptomatic, detected by exam (n [%]): NR - Asymptomatic, detected by imaging (n [%]): NR Additional data used for diagnosis:Histopathology for diagnosis 

1) c-erbB-2: 

 Dis+ Dis- Tot T+ 4 4 8 

T- 16 16 32 Tot 20 20 40  Lower Upper  Value 95% CI 95% CI Se 20.0% 2.5% 37.5% Sp 80.0% 62.5% 97.5% PPV 50.0% 15.4% 84.6% NPV 50.0% 32.7% 67.3% 

2) uPAR: 

 Dis+ Dis- Tot T+ 20 9 29 T- 0 11 11 Tot 20 20 40  Lower Upper  Value 95% CI 95% CI Se 100.0% 85.0% 100.0% Sp 55.0% 33.2% 76.8% PPV 69.0% 52.1% 85.8% NPV 100.0% 72.7% 100.0% 

Comme- Ad ho- EstimascreeniQuality ReferenVerificaTest reSample StatisticBlindingDefinitioGrade: 
A-97

Evidence Table 2 – Question 2 (continued) Study Study Design Patients Clinical Presentation Results CommMakhlouf, Fathalla, Zakhary, et al., 2004 #1980 

Geographical location:

Assiut, Egypt Study dates: Mar 1998 - Apr 2000 Size of population:Ad hoc population 46 malignant 16 benign 30 normal/controlsType of population: Ad hoc Genomic test(s) used: Sulfatides Reference standard: Surgical pathology Reference standard applied to all test negatives?: Yes Test reliability established?:

NR Statistical tests used:

Mann Whitney U Kruskall Wallis Chi square Firshers Blinding: NR Definition of positive and negative on screening test:57 μg/mg 

Age:Malignant: Mean: 50 Range: 44.5-55 Benign: Mean: 27.5 Range: 20-44 Control: Mean: 45 Range: 40-49 Menopausal status (n [%]):41/92 (44%)

Race/ethnicity (n [%]):Egyptian 100% Risk factors (n [%]):NR Diagnoses (n [%]): Ovarian cancer: 46 Benign ovarian mass: 16 Healthy controls: 30 Inclusion criteria:NR Exclusion criteria:NR 

Screening only (n [%]): Not applicable Diagnosis of mass: - Symptomatic (n [%]): NR - Asymptomatic, detected by exam (n [%]): NR - Asymptomatic, detected by imaging (n [%]): NR Additional data used for diagnosis:

NR 

1) Sulfatides for predicting malignancy at cutoff of 57 μg/mg: 

 Dis+ Dis- Tot T+ 42 2 44 

T- 4 28 32 Tot 46 30 76  Lower Upper  Value 95% CI 95% CI Se 91.3% 83.2% 99.4% Sp 93.3% 84.4% 100.0% PPV 95.5% 89.3% 100.0% NPV 87.5% 76.0% 99.0% 

2) Sulfatides for predicting malignancy at cutoff of 91.3 μg/mg: 

 Dis+ Dis- Tot T+ 45 0 45 T- 1 30 31 Tot 46 30 76  Lower Upper  Value 95% CI 95% CI Se 97.0% 92.1% 100.0% Sp 100.0% 90.0% 100.0% PPV 100.0% 93.3% 100.0% NPV 96.8% 90.6% 100.0% 

CommeNone Quality ReferenVerificaTest reSample StatisticBlindingDefinitioGrade: 
A-98

Evidence Table 2 – Question 2 (continued) Study Study Design Patients Clinical Presentation Results CommMcIntosh, Drescher, Karlan, et al., 2004 #1410 

Geographical location:

Seattle WA; Los Angeles, CA Study dates: NR Size of population:52 ovarian cancer 43 benign 220 healthy Type of population:Ad hoc Genomic test(s) used: CA-125 Soluble mesothelinrelated marker Reference standard: Surgical pathology Reference standard applied to all test negatives?: Yes Test reliability established?:

No Statistical tests used:

Logistic Regression ROC analysis Mann Witney U Wilcoxin Blinding: NR Definition of positive and negative on screening test:Based on a fixed specificity of 98% 

Age: NRMenopausal status (n [%]):225/315 (71%)Race/ethnicity (n [%]):

3 Native American 8 Asian 1 Black 4 Hispanic 268 White Risk factors (n [%]):NR Diagnoses (n [%]): Ovarian cancer: 52 Benign ovarian mass: 43 Healthy controls: 220 Inclusion criteria:NR Exclusion criteria:NR 

Screening only (n [%]): Not applicable Diagnosis of mass: - Symptomatic (n [%]): NR - Asymptomatic, detected by exam (n [%]): NR - Asymptomatic, detected by imaging (n [%]): NR Additional data used for diagnosis: NR 

1) CA-125 – healthy controls: 

 Dis+ Dis- Tot T+ 41 4 45 

T- 11 216 227 Tot 52 220 272  Lower Upper  Value 95% CI 95% CI Se 78.8% 67.7% 89.9% Sp 98.2% 96.4% 99.9% PPV 91.1% 82.8% 99.4% NPV 95.2% 92.4% 97.9% 

2) CA-125 – benign controls: 

 Dis+ Dis- Tot T+ 20 4 24 T- 32 216 248 Tot 52 220 272  Lower Upper  Value 95% CI 95% CI Se 38.5% 25.2% 51.7% Sp 98.2% 96.4% 99.9% PPV 83.3% 68.4% 98.2% NPV 87.1% 82.9% 91.3% 

3) SMR – healthy controls: 

 Dis+ Dis- Tot T+ 31 4 35 T- 21 216 237 Tot 52 220 272  Lower Upper  Value 95% CI 95% CI Se 59.6% 46.3% 73.0% Sp 98.2% 96.4% 99.9% PPV 88.6% 78.0% 99.1% NPV 91.1% 87.5% 94.8% 

Comme- Ad hospecificQuality ReferenVerificaTest reSample StatisticBlindingDefinitioGrade: 
A-99

Evidence Table 2 – Question 2 (continued) Study Study Design Patients Clinical Presentation Results Comm

4) SMR – benign controls: 

 Dis+ Dis- Tot T+ 15 4 19 

T- 37 216 253 Tot 52 220 272  Lower Upper  Value 95% CI 95% CI Se 28.8% 16.5% 41.2% Sp 98.2% 96.4% 99.9% PPV 78.9% 60.6% 97.3% NPV 85.4% 81.0% 89.7% 

5) Combined SMR + CA-125 – healthy controls: 

 Dis+ Dis- Tot T+ 45 4 49 T- 7 216 223 Tot 52 220 272  Lower Upper  Value 95% CI 95% CI Se 86.5% 77.3% 95.8% Sp 98.2% 96.4% 99.9% PPV 91.8% 84.2% 99.5% NPV 96.9% 94.6% 99.1% 

6) Combined SMR + CA-125 – benign controls: 

 Dis+ Dis- Tot T+ 17 4 21 T- 22 216 238 Tot 39 220 259  Lower Upper  Value 95% CI 95% CI Se 43.6% 28.0% 59.2% Sp 98.2% 96.4% 99.9% PPV 81.0% 64.2% 97.7% 
A-100

Evidence Table 2 – Question 2 (continued) Study Study Design Patients Clinical Presentation Results Comm

NPV 90.8% 87.1% 94.4% 

Medl, Ogris, Peters-Engl, et al., 1995 #8220 

Geographical location:

Austria Study dates: May 1988 - Jun 1993 Size of population: 419 Type of population: Screening Genomic test(s) used: CA-125 Tumor-associated trypsin inhibitor (TATI) Reference standard: Surgical pathology Reference standard applied to all test negatives?: NR Test reliability established?:

Yes Statistical tests used:

Se, Sp, PPV, NPV, ROC curves; Kruskal-Wallis test used for statistical analysis Blinding: Yes Definition of positive and negative on screening test:TATI > 21 ng/mL CA-125 > 35 U/mL 

Age:Mean: 62.4 Range: 23-87 Menopausal status (n [%]):NR Race/ethnicity (n [%]):

Austrian 100% Risk factors (n [%]):NR Diagnoses (n [%]): Ovarian cancer: 152 Benign ovarian mass: 267 Inclusion criteria:NR Exclusion criteria:NR 

Screening only (n [%]): NR Diagnosis of mass:

- Symptomatic (n [%]): NR - Asymptomatic, detected by exam (n [%]): NR - Asymptomatic, detected by imaging (n [%]): NR Additional data used for diagnosis:NR 

1) TATI > 21 ng/mL: 

 Dis+ Dis- Tot T+ 75 67 142 

T- 40 200 240 Tot 115 267 382  Lower Upper  Value 95% CI 95% CI Se 65.2% 56.5% 73.9% Sp 74.9% 69.7% 80.1% PPV 52.8% 44.6% 61.0% NPV 83.3% 78.6% 88.0% 

2) CA-125 > 35 U/mL: 

 Dis+ Dis- Tot T+ 90 59 149 T- 25 208 233 Tot 115 267 382  Lower Upper  Value 95% CI 95% CI Se 78.3% 70.7% 85.8% Sp 77.9% 72.9% 82.9% PPV 60.4% 52.5% 68.3% NPV 89.3% 85.3% 93.2% 

3) CA-125 > 65 U/mL: 

 Dis+ Dis- Tot T+ 79 23 102 T- 36 244 280 Tot 115 267 382  Lower Upper  Value 95% CI 95% CI Se 68.7% 60.2% 77.2% Sp 91.4% 88.0% 94.8% 

Comme- Consenot all htumors; populatQuality ReferenVerificaTest reSample StatisticBlindingDefinitioGrade: 
A-101

Evidence Table 2 – Question 2 (continued) Study Study Design Patients Clinical Presentation Results Comm

PPV 77.5% 69.3% 85.6% NPV 87.1% 83.2% 91.1% 

4) TATI > 21 ng/mL or CA-125 > 35 U/mL: 

 Dis+ Dis- Tot T+ 105 105 210 T- 10 162 172 Tot 115 267 382  Lower Upper  Value 95% CI 95% CI Se 91.3% 86.2% 96.5% Sp 60.7% 54.8% 66.5% PPV 50.0% 43.2% 56.8% NPV 94.2% 90.7% 97.7% 

5) TATI > 21 ng/mL or CA-125 > 65 U/mL: 

 Dis+ Dis- Tot T+ 98 85 183 

T- 17 182 199 Tot 115 267 382  Lower Upper  Value 95% CI 95% CI Se 85.2% 78.7% 91.7% Sp 68.2% 62.6% 73.8% PPV 53.6% 46.3% 60.8% NPV 91.5% 87.6% 95.3% 
A-102

Evidence Table 2 – Question 2 (continued) Study Study Design Patients Clinical Presentation Results CommObermair, Tempfer, Hefler, et al., 1998 #6690 

Geographical location:

Austria Study dates: NR Size of population:256 Type of population: Ad hoc Genomic test(s) used: Vascular endothelial growth factor CA-125 Reference standard: Surgical pathology Reference standard applied to all test negatives?: Unable to determine Test reliability established?:

Yes Statistical tests used:

Logistic Regression ROC curves Blinding: NR Definition of positive and negative on screening test:VEGF – 363.7 pg/ml CA-125 – 74.9 U/ml 

Age:Median: 47 Range: 21-79 Menopausal status (n [%]):NR Race/ethnicity (n [%]):

NR Risk factors (n [%]):NR Diagnoses (n [%]): Ovarian cancer: 44 Benign ovarian mass: 81 Healthy controls: 131 Inclusion criteria:NR Exclusion criteria:NR 

Screening only (n [%]): NR Diagnosis of mass: - Symptomatic (n [%]): NR - Asymptomatic, detected by exam (n [%]): NR - Asymptomatic, detected by imaging (n [%]): NR Additional data used for diagnosis:NR 

1) VEGF – healthy/cancer: 

 Dis+ Dis- Tot T+ 24 19 43 

T- 20 62 82 Tot 44 81 125  Lower Upper  Value 95% CI 95% CI Se 54.5% 39.8% 69.3% Sp 76.5% 67.3% 85.8% PPV 55.8% 41.0% 70.7% NPV 75.6% 66.3% 84.9% 

2) CA-125: 

 Dis+ Dis- Tot T+ 37 6 43 T- 7 75 82 Tot 44 81 125  Lower Upper  Value 95% CI 95% CI Se 84.1% 73.3% 94.9% Sp 92.6% 86.9% 98.3% PPV 86.0% 75.7% 96.4% NPV 91.5% 85.4% 97.5% 

CommeNone Quality ReferenVerificaTest reSample StatisticBlindingDefinitioGrade: 
A-103

Evidence Table 2 – Question 2 (continued) Study Study Design Patients Clinical Presentation Results CommOehler and Caffier, 1999 #5560 

Geographical location:

Wurzburg, Germany; Houston, TX Study dates: NR 

Size of population:

41 cancers 20 benign tumors 20 normal controls Type of population: Ad hoc Genomic test(s) used: VEGF Reference standard: Surgical pathology Reference standard applied to all test negatives?: No Test reliability established?:

Yes Statistical tests used:

Kruskal-Wallis Mann-Whitney U ROC curve Blinding: NR Definition of positive and negative on screening test:300 pg/mL 

Ovarian CA patients Age:Mean: 60 Range: 32-83 Menopausal status (n [%]):NR Race/ethnicity (n [%]):

NR Risk factors (n [%]):NR Diagnoses (n [%]): Ovarian cancer: 41 Benign ovarian mass: 20 Healthy controls: 20 Inclusion criteria:NR Exclusion criteria:NR 

Screening only (n [%]): NR Diagnosis of mass: - Symptomatic (n [%]): NR - Asymptomatic, detected by exam (n [%]): NR - Asymptomatic, detected by imaging (n [%]): NR Additional data used for diagnosis:NR 

1) VEGF – ovarian cancer and normal controls: 

 Dis+ Dis- Tot T+ 30 6 36 

T- 11 14 25 Tot 41 20 61  Lower Upper  Value 95% CI 95% CI Se 73.2% 59.6% 86.7% Sp 70.0% 49.9% 90.1% PPV 83.3% 71.2% 95.5% NPV 56.0% 36.5% 75.5% 

2) VEGF – ovarian cancer and benign tumors: 

 Dis+ Dis- Tot T+ 29 7 36 T- 12 13 25 Tot 41 20 61  Lower Upper  Value 95% CI 95% CI Se 70.7% 56.8% 84.7% Sp 65.0% 44.1% 85.9% PPV 80.6% 67.6% 93.5% NPV 52.0% 32.4% 71.6% 

Comme- Ad hoQuality ReferenVerificaTest reSample StatisticBlindingDefinitioGrade: 
A-104

Evidence Table 2 – Question 2 (continued) Study Study Design Patients Clinical Presentation Results CommOnsrud, Shabana, and Austgulen, 1996 #7710 

Geographical location:

Trondhiem, Norway Study dates: NR 

Size of population:

45 - cancer 27 - benign pelvic masses 26 - controls Type of population: Ad hoc Genomic test(s) used: p55 p75 CA-125 Reference standard: Surgical pathology Reference standard applied to all test negatives?: Yes Test reliability established?:

No Statistical tests used:

Wilcoxin rank sum Blinding: NR Definition of positive and negative on screening test:Mean + 2 SD of the control women for all three tests: p55 = 2.0 ng/mL p75 = 4.3 ng/mL CA-125 = 20 U/mL 

Age:Ovarian cancer: Median: 58 Range: 21-80 Benign tumors: Median: 45 Range: 19-67 Normal controls: Median: 39 Range: 27-76 Menopausal status (n [%]):NR Race/ethnicity (n [%]):

NR Risk factors (n [%]):NR Diagnoses (n [%]): Ovarian cancer: 45 Benign ovarian mass: 27 Healthy controls: 26 Inclusion criteria:Patients undergoing laparotomy surgery Exclusion criteria:NR 

Screening only (n [%]): NR Diagnosis of mass:- Symptomatic (n [%]): NR - Asymptomatic, detected by 

exam (n [%]): NR - Asymptomatic, detected by imaging (n [%]): NR Additional data used for diagnosis:NR 

Note: All tables are for ovarian cancer versus benign tumors. 1) p55: 

 Dis+ Dis- Tot T+ 26 3 29 

T- 19 24 43 Tot 45 27 72  Lower Upper  Value 95% CI 95% CI Se 57.8% 43.3% 72.2% Sp 88.9% 77.0% 100.0% PPV 89.7% 78.6% 100.0% NPV 55.8% 41.0% 70.7% 

2) p75: 

 Dis+ Dis- Tot T+ 7 1 8 T- 38 26 64 Tot 45 27 72  Lower Upper  Value 95% CI 95% CI Se 15.6% 5.0% 26.1% Sp 96.3% 89.2% 100.0% PPV 87.5% 64.6% 100.0% NPV 40.6% 28.6% 52.7% 

3) CA-125: 

 Dis+ Dis- Tot T+ 37 4 41 T- 8 23 31 Tot 45 27 72  Lower Upper  Value 95% CI 95% CI Se 82.2% 71.1% 93.4% Sp 85.2% 71.8% 98.6% 

CommeNone Quality ReferenVerificaTest reSample StatisticBlindingDefinitioGrade: 
A-105

Evidence Table 2 – Question 2 (continued) Study Study Design Patients Clinical Presentation Results Comm

PPV 90.2% 81.2% 99.3% NPV 74.2% 58.8% 89.6% 

Note: NPV calculated here does not match the 72% reported in the paper. 4) p55 + CA-125: 

 Dis+ Dis- Tot T+ 38 6 44 T- 7 21 28 Tot 45 27 72  Lower Upper  Value 95% CI 95% CI Se 84.4% 73.9% 95.0% Sp 77.8% 62.1% 93.5% PPV 86.4% 76.2% 96.5% NPV 74.2% 58.8% 89.6% 
A-106

Evidence Table 2 – Question 2 (continued) Study Study Design Patients Clinical Presentation Results CommOpala, Drews, Rzymski, et al., 2003 #2650 

Geographical location:

Poznan, Poland Study dates: 

Jun 2000 – Nov 2002 Size of population:

101 with suspicious tumors 16 healthy women Type of population: Ad hoc Genomic test(s) used: Soluble intracellular adhesion molecule-1 Reference standard: Surgical pathology Reference standard applied to all test negatives?: NR Test reliability established?:

Yes Statistical tests used:

Mann Witney U Spearman Pearson Blinding: NR Definition of positive and negative on screening test:CA-125 = 35 U/mL sICAM-1 = 250 ng/mL 

Age:Cancer: Mean (SD): 51.5 (11.5) Range: 23-80 Borderline: Mean (SD): 46.0 (15.8) Range: 25-62 Benign: Mean (SD): 39.4 (14.3) Range: 13-71 Normal: Mean (SD): 27.8 (12.6) Range: 20-63 Menopausal status (n [%]):NR Race/ethnicity (n [%]):

NR Risk factors (n [%]):NR Diagnoses (n [%]): Ovarian cancer: 51 Borderline: 5 Benign ovarian mass: 45 Healthy controls: 16 Inclusion criteria:Women treated surgically b/c of suspicious pelvic tumors Exclusion criteria:NR 

Screening only (n [%]): NR Diagnosis of mass:- Symptomatic (n [%]): NR - Asymptomatic, detected by exam (n [%]): NR - Asymptomatic, detected by imaging (n [%]): NR Additional data used for diagnosis:NR 

Note: All tables are for ovarian cancer vs. healthy controls. 1) sICAM-1: 

 Dis+ Dis- Tot T+ 42 7 49 

T- 9 9 18 Tot 51 16 67  Lower Upper  Value 95% CI 95% CI Se 82.4% 71.9% 92.8% Sp 56.3% 31.9% 80.6% PPV 85.7% 75.9% 95.5% NPV 50.0% 26.9% 73.1% 

Note: Numbers do not match those reported in the article. 2) CA-125: 

 Dis+ Dis- Tot T+ 48 4 52 T- 3 12 15 Tot 51 16 67  Lower Upper  Value 95% CI 95% CI Se 93.8% 87.2% 100.0% Sp 74.5% 53.1% 95.9% PPV 92.3% 85.1% 99.6% NPV 80.0% 59.8% 100.0% 

Note: Numbers do not match those reported in the article. (3) sICAM-I + CA-125: 

 Dis+ Dis- Tot T+ 48 2 50 T- 3 14 17 

Comme- Majorwomen - UnablCA-125 - Same et al., 2Quality ReferenVerificaTest reSample StatisticBlindingDefinitioGrade: 
A-107

Evidence Table 2 – Question 2 (continued) Study Study Design Patients Clinical Presentation Results Comm

Tot 51 16 67  Lower Upper  Value 95% CI 95% CI Se 93.8% 87.2% 100.0% Sp 86.3% 69.5% 100.0% PPV 96.0% 90.6% 100.0% NPV 82.4% 64.2% 100.0% 

Note: Numbers do not match those reported in the article. 
A-108

Evidence Table 2 – Question 2 (continued) Study Study Design Patients Clinical Presentation Results CommOpala, Rzymski, Wildzak, et al., 2005 #480 

Geographical location:

Poznan, Poland Study dates: 

Jun 2000 – Nov 2002 Size of population:

101 with suspicious tumors 16 healthy women Type of population: Ad hoc Genomic test(s) used: p55 p75 CA-125 Reference standard applied to all test negatives?: No Test reliability established?:

Yes Statistical tests used:

Mann Withney U ROC curves Correlation coefficients Blinding: NR Definition of positive and negative on screening test:p55: 1663 pg/mL p75: 2837 pg/mL CA-125: 35 U/mL 

Age:Cancer: Mean (SD): 51.5 (11.5) Range: 23-80 Borderline: Mean (SD): 46.0 (15.8) Range: 25-62 Benign: Mean (SD): 39.4 (14.3) Range: 13-71 Normal: Mean (SD): 27.8 (12.6) Range: 20-63 Menopausal status (n [%]):NR Race/ethnicity (n [%]):

NR Risk factors (n [%]):NR Diagnoses (n [%]): Ovarian cancer: 51 Borderline: 5 Benign ovarian mass: 45 Healthy controls: 16 Inclusion criteria:Women treated surgically b/c of suspicious pelvic tumors Exclusion criteria:NR 

Screening only (n [%]): NR Diagnosis of mass: - Symptomatic (n [%]): NR - Asymptomatic, detected by exam (n [%]): NR - Asymptomatic, detected by imaging (n [%]): NR Additional data used for diagnosis:

NR 

1) p55: 

 Dis+ Dis- Tot T+ 28 1 29 

T- 23 15 38 Tot 51 16 67  Lower Upper  Value 95% CI 95% CI Se 54.9% 41.2% 68.6% Sp 93.8% 81.9% 100.0% PPV 96.6% 89.9% 100.0% NPV 39.5% 23.9% 55.0% 

2) p75: 

 Dis+ Dis- Tot T+ 22 3 25 T- 29 13 42 Tot 51 16 67  Lower Upper  Value 95% CI 95% CI Se 43.1% 29.5% 56.7% Sp 81.3% 62.1% 100.0% PPV 88.0% 75.3% 100.0% NPV 31.0% 17.0% 44.9% 

3) CA-125: 

 Dis+ Dis- Tot T+ 38 1 39 T- 13 15 28 Tot 51 16 67  Lower Upper  Value 95% CI 95% CI Se 74.5% 62.5% 86.5% Sp 93.8% 81.9% 100.0% PPV 97.4% 92.5% 100.0% NPV 53.6% 35.1% 72.0% 

Comme- Cutpoversus - Ad ho- Same et al., 2Quality ReferenVerificaTest reSample StatisticBlindingDefinitioGrade: 
A-109

Evidence Table 2 – Question 2 (continued) Study Study Design Patients Clinical Presentation Results CommPeters-Engl, Medl, Ogris, et al., 1995 #7840 

Geographical location:

Vienna, Austria Study dates: NR 

Size of population:

180 ovarian cancer 214 with benign pelvic disease Type of population: Ad hoc Genomic test(s) used: TATI CA-125 Reference standard: Surgical pathology Reference standard applied to all test negatives?: Yes Test reliability established?:

Yes Statistical tests used:

“Non parametric test” Blinding: Yes Definition of positive and negative on screening test:TATI: 21 ng/ml CA-125: 35 U/ml 

Age:Mean: 62.4 Range: 23-87 Menopausal status (n [%]):NR Race/ethnicity (n [%]):

Austrian 100% Risk factors (n [%]):NR Diagnoses (n [%]): Ovarian cancer: 180 Benign ovarian mass: 214 Healthy controls: 149 – used for determination of cutpoint only Inclusion criteria:NR Exclusion criteria:NR 

Screening only (n [%]): NR Diagnosis of mass:- Symptomatic (n [%]): NR - Asymptomatic, detected by exam (n [%]): NR - Asymptomatic, detected by imaging (n [%]): NR Additional data used for diagnosis:NR 

1) TATI: 

 Dis+ Dis- Tot T+ 114 60 174 

T- 66 154 220 Tot 180 214 394  Lower Upper  Value 95% CI 95% CI Se 63.3% 56.3% 70.4% Sp 72.0% 65.9% 78.0% PPV 65.5% 58.5% 72.6% NPV 70.0% 63.9% 76.1% 

2) CA-125: 

 Dis+ Dis- Tot T+ 144 39 183 T- 36 175 211 Tot 180 214 394  Lower Upper  Value 95% CI 95% CI Se 80.0% 74.2% 85.8% Sp 81.8% 76.6% 86.9% PPV 78.7% 72.8% 84.6% NPV 82.9% 77.9% 88.0% 

3) CA-125 + TATI: 

 Dis+ Dis- Tot T+ 164 75 239 T- 16 139 155 Tot 180 214 394  Lower Upper  Value 95% CI 95% CI Se 91.1% 87.0% 95.3% Sp 65.0% 58.6% 71.3% PPV 68.6% 62.7% 74.5% NPV 89.7% 84.9% 94.5% 

CommeNone Quality ReferenVerificaTest reSample StatisticBlindingDefinitioGrade: 
A-110

Evidence Table 2 – Question 2 (continued) Study Study Design Patients Clinical Presentation Results CommSchutter, Mijatovic, Kok, et al., 1999 #5950 

Geographical location:

Amsterdam, The Netherlands Study dates: NR 

Size of population:

31 Type of population: Malignant and nonmalignant disease Genomic test(s) used: UGP (urinary gonadotropin peptide) UGP/Cr CA-125 Reference standard: Surgical pathology Reference standard applied to all test negatives?: Yes Test reliability established?:No (except CA-125)Statistical tests used:

Se, Se, ROCBlinding: NR Definition of positive and negative on screening test:UGP > 1 fmol/L is positive UGP/creat > 1.33 fmol/mg positive CA-125 > 100 U/mL positive 

Age: NR Menopausal status (n [%]):NRRace/ethnicity (n [%]):NR Risk factors (n [%]):NRDiagnoses (n [%]): Ovarian cancer: 9 Benign ovarian or uterine mass: 10 Other benign controls: 12 - Sterilization: 3 - Refertilization (?): 2 - Prolapse: 2 - DUB: 2 - Endometriosis: 2 - Sterility: 1 Inclusion criteria:

Scheduled for surgery due to a gynecologic condition Exclusion criteria:NR 

Screening only (n [%]): NR Diagnosis of mass: - Symptomatic (n [%]): NR - Asymptomatic, detected by exam (n [%]): NR - Asymptomatic, detected by imaging (n [%]): NR Additional data used for diagnosis:

NR 

1) Ability of UGP >1 fmol/L to discriminate ovarian carcinoma from all benign gynecologic conditions (both benign masses and controls): 

 Dis+ Dis- Tot T+ 7 7 14 

T- 2 14 16 Tot 9 21 30  Lower Upper  Value 95% CI 95% CI Se 78.0% 50.9% 100.0% Sp 65.0% 44.6% 85.4% PPV 50.0% 23.8% 76.2% NPV 87.5% 71.3% 100.0% 

2) Ability of UGP/creat > 1.33 fmol/mg to discriminate ovarian arcinoma from all benign gynecologic conditions (both benign masses and controls). Note: same numbers as first table: 

 Dis+ Dis- Tot T+ 7 7 14 T- 2 14 16 Tot 9 21 30  Lower Upper  Value 95% CI 95% CI Se 78.0% 50.9% 100.0% Sp 65.0% 44.6% 85.4% PPV 50.0% 23.8% 76.2% NPV 87.5% 71.3% 100.0% 

Comme- Only 9 - For puthis matest or a preferafor discmasses controlstogetheto differmasses Quality ReferenVerificaTest reSample StatisticBlindingDefinitioGrade: 
A-111

Evidence Table 2 – Question 2 (continued) Study Study Design Patients Clinical Presentation Results CommSchutter, Sohn, Kristen, et al., 1998 #6850 

Geographical location:

Amsterdam, The Netherlands Study dates: NR 

Size of population:

155 Type of population: Adnexal mass Genomic test(s) used: CA 72-4 CA-125 Reference standard: Surgical pathology Reference standard applied to all test negatives?: Yes Test reliability established?:YesStatistical tests used:

Se, Sp, PPV, NPV, ROC, logistic regression (incorporating ultrasound score)Blinding: NR Definition of positive and negative on screening test:CA-125 < 35 u/mL normal CA-72-4 < 3 normal

Age:Mean: 63 Median: 61 Range: 45-88 Menopausal status (n [%]):Post (> 55): 155 (100%) Race/ethnicity (n [%]):NR Risk factors (n [%]):NRDiagnoses (n [%]): Ovarian cancer: 43 (27%)Borderline: 4 (3%) Benign ovarian mass: 92 (59%) Other (non-ovarian malignancies): 16 (10%) Inclusion criteria:

Plan to undergo surgery for a pelvic mass, postmenopausal Exclusion criteria: Exclusion criteria not defined in this paper, reader is referred to Reference #1.

Screening only (n [%]): NR Diagnosis of mass: - Symptomatic (n [%]): NR - Asymptomatic, detected by exam (n [%]): NR - Asymptomatic, detected by imaging (n [%]): NR 

Additional data used for diagnosis:NR 

1) CA-72-4, ovarian cancer versus benign masses, cutoff 2 U/mL: 

 Dis+ Dis- Tot T+ 36 42 78 

T- 7 50 57 Tot 43 92 135  Lower Upper  Value 95% CI 95% CI Se 84.0% 73.0% 95.0% Sp 54.0% 43.8% 64.2% PPV 46.2% 35.1% 57.2% NPV 87.7% 79.2% 96.2% 

2) CA-72-4, ovarian cancer versus benign masses, cutoff 3 U/mL: 

 Dis+ Dis- Tot T+ 28 6 34 T- 15 86 101 Tot 43 92 135  Lower Upper  Value 95% CI 95% CI Se 65.0% 50.7% 79.3% Sp 93.0% 87.8% 98.2% PPV 82.4% 69.5% 95.2% NPV 85.1% 78.2% 92.1% 

For CA-72-4 with cutoff 3.2 U/mL, Se = 65, Sp = 95, PPV = 85, NPV = 85. 

Comme- No hehad eithmalignaQuality ReferenVerificaTest reSample StatisticBlindingDefinitioGrade: 
A-112

Evidence Table 2 – Question 2 (continued) Study Study Design Patients Clinical Presentation Results CommSedlaczek, Frydecka, Gabrys, et al., 2002 #3310 

Geographical location:

Wroclaw, Poland Study dates: NR 

Size of population:

99 Type of population: Adnexal mass Genomic test(s) used: TPS, sIL-2R, CA-125 Reference standard Surgical pathology Reference standard applied to all test negatives?: Yes Test reliability established?:YesStatistical tests used:

Mann-Whitney U, Wilcoxon Blinding: NRDefinition of positive and negative on screening test:CA-125 < 35 U/mL normal TPS < 80 U/mL normal sIL-2R < 2140 pg/nl normal 

Age: NR Menopausal status (n [%]):NRRace/ethnicity (n [%]):

NR Risk factors (n [%]):NRDiagnoses (n [%]): Ovarian cancer: 67 (67%)Benign ovarian mass: 32 (32%) Inclusion criteria:Planned surgery for ovarian mass Exclusion criteria:NR 

Screening only (n [%]): NR Diagnosis of mass: - Symptomatic (n [%]): NR - Asymptomatic, detected by exam (n [%]): NR - Asymptomatic, detected by imaging (n [%]): NR Additional data used for diagnosis:NR 

1) TPS serum – ovarian cancer vs. benign ovarian mass: 

 Dis+ Dis- Tot 

T+ 53 6 59 T- 14 26 40 Tot 67 32 99  Lower Upper  Value 95% CI 95% CI Se 79.1% 69.4% 88.8% Sp 81.3% 67.7% 94.8% PPV 89.8% 82.1% 97.5% NPV 65.0% 50.2% 79.8% 

2) sIL-2R serum – ovarian cancer vs. benign ovarian mass: 

 Dis+ Dis- Tot T+ 54 1 55 T- 13 31 44 Tot 67 32 99  Lower Upper  Value 95% CI 95% CI Se 80.6% 71.1% 90.1% Sp 96.9% 90.8% 100.0% PPV 98.2% 94.7% 100.0% NPV 70.5% 57.0% 83.9% 

3) TPS cyst fluid – ovarian cancer vs. benign ovarian mass: 

 Dis+ Dis- Tot T+ 26 32 58 T- 0 0 0 Tot 26 32 58  Lower Upper  Value 95% CI 95% CI Se 100.0% 88.5% 100.0% Sp 0.0% 0.0% 0.0% 

Comme- TPS leof all pa(Sp = 0- No dapatients calculatQuality ReferenVerificaTest reSample StatisticBlindingDefinitioGrade: 
A-113

Evidence Table 2 – Question 2 (continued) Study Study Design Patients Clinical Presentation Results Comm

PPV 44.8% 32.0% 57.6% NPV #DIV/0! #DIV/0! #DIV/0! 

4) sIL-2R cyst fluid – ovarian cancer vs. benign ovarian mass: 

 Dis+ Dis- Tot T+ 21 4 25 T- 5 28 33 Tot 26 32 58  Lower Upper  Value 95% CI 95% CI Se 80.8% 65.6% 95.9% Sp 87.5% 76.0% 99.0% PPV 84.0% 69.6% 98.4% NPV 84.8% 72.6% 97.1% 

5) TPS ascites – ovarian cancer vs. benign ovarian mass: 

 Dis+ Dis- Tot T+ 45 0 45 T- 0 0 0 Tot 45 0 45  Lower Upper  Value 95% CI 95% CI Se 100.0% 93.3% 100.0% Sp #DIV/0! #DIV/0! #DIV/0! PPV 100.0% 93.3% 100.0% NPV #DIV/0! #DIV/0! #DIV/0! 

6) sIL-2R ascites – ovarian cancer vs. benign ovarian mass: 

 Dis+ Dis- Tot T+ 44 0 44 T- 1 0 1 Tot 45 0 45  Lower Upper 
A-114

Evidence Table 2 – Question 2 (continued) Study Study Design Patients Clinical Presentation Results Comm

 Value 95% CI 95% CI Se 97.8% 93.5% 100.0% Sp #DIV/0! #DIV/0! #DIV/0! PPV 100.0% 93.2% 100.0% NPV 0.0% 0.0% 0.0% 

Skates, Horick, Yu, et al., 2004 #1380 

Geographical location:

Boston, MA; Houston, TX; Durham, NC; Baltimore, MD; Groningen, The Netherlands; London, UK Study dates: NR 

Size of population:

189 (training set); 158 (validation set) Type of population Known cases of cancer, healthy controls Genomic test(s) used: CA-125 II CA-15-3 CA-72-4 Macrophage colonystimulating factor (MCSF) Reference standard Surgical pathology for cancer; not specified for controls Reference standard applied to all test negatives?: Unclear Test reliability established?:

Age: NRMenopausal status (n [%]):NR Race/ethnicity (n [%]):

NR Risk factors (n [%]):NR Diagnoses (n [%]): Ovarian cancer: 60 (38.0%) Healthy controls: 98 (62.0%) Inclusion criteria:NR Exclusion criteria:NR 

Screening only (n [%]): NR Diagnosis of mass: - Symptomatic (n [%]): NR - Asymptomatic, detected by exam (n [%]): NR - Asymptomatic, detected by imaging (n [%]): NR Additional data used for diagnosis:NR 

Models including all 4 markers. 1) Logistic regression, cancer vs. control. Note: 95% CIs may not reflect 95% CI estimates based on statistical model; CIs not reported: 

 Dis+ Dis- Tot T+ 41 2 43 

T- 19 96 115 Tot 60 98 158  Lower Upper  Value 95% CI 95% CI Se 68.0% 56.2% 79.8% Sp 98.0% 95.2% 100.0% PPV 95.3% 89.1% 100.0% NPV 83.5% 76.7% 90.3% 

2) CART, cancer vs. control. Note: 95% CIs may not reflect 95% CI estimates based on statistical model; CIs not reported: 

 Dis+ Dis- Tot T+ 21 2 23 T- 39 96 135 Tot 60 98 158  Lower Upper  Value 95% CI 95% CI Se 35.0% 22.9% 47.1% Sp 98.0% 95.2% 100.0% PPV 91.3% 79.8% 100.0% NPV 71.1% 63.5% 78.8% 

Comme- Very hboth tes- 95% CsensitivstatisticuniformQuality ReferenVerificaTest reSample StatisticBlindingDefinitioGrade: 
A-115

Evidence Table 2 – Question 2 (continued) Study Study Design Patients Clinical Presentation Results Comm

Referenced Statistical tests used:

Logistic regression using results from multiple markers; CART trees; mixture discriminant analysis Blinding: NRDefinition of positive and negative on screening test:

3) MDA, cancer vs. control. Note: 95% CIs may not reflect 95% CI estimates based on statistical model; CIs not reported: 

 Dis+ Dis- Tot T+ 41 2 43 

T- 19 96 115 Tot 60 98 158  Lower Upper  Value 95% CI 95% CI Se 68.0% 56.2% 79.8% Sp 98.0% 95.2% 100.0% PPV 95.3% 89.1% 100.0% NPV 83.5% 76.7% 90.3% 

4) Possible additional 2x2 tables: Combinations of markers at 98% specificity: CA-125 II + CA-72.4: Se 67% CA-125 II + CA-72-4 +M-CSF: Se 70% CA-125 II + CA-72-4 +M-CSF + CA-15-3: Se 68% Additional 2x2 tables possible for some models, but at specificity fixed at 95%. 5) Data on other test accuracy measures (correlation coefficient, interclass correlation, etc.): Single marker sensitivities all lower at fixed specificity. 
A-116

Evidence Table 2 – Question 2 (continued) Study Study Design Patients Clinical Presentation Results CommTakano, Okamoto, Fukushima, et al., 2000 #4960 

Geographical location:

Tokyo, Japan Study dates: NR 

Size of population:

59 Type of population: Ovarian cancer, benign ovarian tumor, or healthy control Genomic test(s) used: CK19 expression in peripheral blood mononuclear cells (PBMCs) Reference standard: Surgical pathology (not specified but assumed) Reference standard applied to all test negatives?: No Test reliability established?:NoStatistical tests used:

Not applicable Blinding: NR Definition of positive and negative on screening test:Not given (any present versus none present is assumed based on reading paper)

Age:NR Menopausal status (n [%]):NRRace/ethnicity (n [%]):NR Risk factors (n [%]):NRDiagnoses (n [%]): Ovarian cancer: 25 (42%)Benign ovarian mass: 14 (24%) Healthy controls: 20 (34%) Inclusion criteria:NR Exclusion criteria:NR 

Screening only (n [%]): NR Diagnosis of mass: - Symptomatic (n [%]): NR - Asymptomatic, detected by exam (n [%]): NR - Asymptomatic, detected by imaging (n [%]): NR Additional data used for diagnosis:NR 

1) CK19 expression in PBMCs – ovarian cancer vs. benign ovarian tumors: 

 Dis+ Dis- Tot 

T+ 21 10 31 T- 4 4 8 Tot 25 14 39  Lower Upper  Value 95% CI 95% CI Se 84.0% 69.6% 98.4% Sp 28.6% 4.9% 52.2% PPV 67.7% 51.3% 84.2% NPV 50.0% 15.4% 84.6% 

2) CK19 expression in PBMCs – ovarian cancer vs. healthy controls: 

 Dis+ Dis- Tot T+ 21 12 33 T- 4 8 12 Tot 25 20 45  Lower Upper  Value 95% CI 95% CI Se 84.0% 69.6% 98.4% Sp 40.0% 18.5% 61.5% PPV 63.6% 47.2% 80.0% NPV 66.7% 40.0% 93.3% 

Comme- No deexpressthe abilwith PC- Authothis tescontrols Quality ReferenVerificaTest reSample StatisticBlindingDefinitioGrade: 
A-117

Evidence Table 2 – Question 2 (continued) Study Study Design Patients Clinical Presentation Results CommTanir, Ozalp, Yalcin, et al., 2003 #2630 

Geographical location:

Eskisehir, Turkey Study dates: Aug 2001-Sep 2002 Size of population:63 Type of population: Adnexal mass Genomic test(s) used: Serum VEGF Reference standard: Surgical pathology Reference standard applied to all test negatives?: Yes Test reliability established?:NoStatistical tests used:

Se, Sp, ROC, positive and negative likelihood ratiosBlinding: NR Definition of positive and negative on screening test:68.7 pg/mL was proposed, as it had best Se, Sp (different cutoffs were examined)

Age:Non-neoplastic: Mean (SD): 39 (± 2) Neoplastic: Mean (SD): 56.9 (± 4.2) Menopausal status (n [%]):Pre (< 45): 40 (63%) Post (> 55): 23 (37%) Race/ethnicity (n [%]):NRRisk factors (n [%]):NRDiagnoses (n [%]): Ovarian cancer (group III): 12 (19.3%) Non-neoplastic benign (group I): 40 (64.5%) Neoplastic benign (group II): 10 (16.1%) Inclusion criteria:

Pelvic mass, planned surgery Exclusion criteria:NR 

Screening only (n [%]): NR Diagnosis of mass: - Symptomatic (n [%]): NR - Asymptomatic, detected by exam (n [%]): NR - Asymptomatic, detected by imaging (n [%]): NR Additional data used for diagnosis: Among postmenopausal women, used 

tumor volume, solid/cystic appearance, presence and thickness of septa, uni-or bilaterality, intratumoral papillary excrescences to determine malignancy of mass 

1) Serum VEGF cutoff 68.7 pg/mL – benign and non-neoplastic versus malignant ovarian masses. Se 92, Sp 88, LR(+) 3.3, LR(-) 0.11 

 Dis+ Dis- Tot T+ 11 6 17 

T- 1 44 45 Tot 12 50 62  Lower Upper  Value 95% CI 95% CI Se 91.7% 76.0% 100.0% Sp 88.0% 79.0% 97.0% PPV 64.7% 42.0% 87.4% NPV 97.8% 93.5% 100.0% 

2) Data on other test accuracy measures (correlation coefficient, interclass correlation, etc.): AUC for VEGF = 0.938 (95% CI 0.81 to 0.96). AUC for VEGF (postmenopausal only) = 0.902 (0.70 to 0.98). 

Comme- Small of resulQuality ReferenVerificaTest reSample StatisticBlindingDefinitioGrade: 
A-118

Evidence Table 2 – Question 2 (continued) Study Study Design Patients Clinical Presentation Results CommTempfer, Hefler, Heinzl, et al., 1998 #6560 

Geographical location:

Vienna, Austria Study dates: NR 

Size of population:

175 Type of population: Adnexal mass Other benign conditions Genomic test(s) used: CYFRA 21-1 Reference standard: Surgical pathology Clinical outcome (some patients had known benign conditions and were not operative candidates) Reference standard applied to all test negatives?: N/A Test reliability established?:NoStatistical tests used:

Se, Sp, ROC curvesBlinding: NR Definition of positive and negative on screening test:CYFRA 21-1 level of 4.7 mcg/L was used to report Se, Sp

Age:Median: 57.9 (cancer patients), 31.3 (healthy controls) Menopausal status (n [%]):NRRace/ethnicity (n [%]):NR Risk factors (n [%]):NR Diagnoses (n [%]): Ovarian cancer: 37 (21%)Other: - Benign cyst: 90 (51%) - Endometriosis: 10 (6%) - PID: 38 (22%) - Healthy controls: 40 (23%) - Inflammatory bowel disease: 10 (6%) - Cirrhosis: 20 (11%) Inclusion criteria:Surgery for a pelvic mass, healthy controls, and controls with nonmalignant diseases Exclusion criteria:NR 

Screening only (n [%]): NR Diagnosis of mass: - Symptomatic (n [%]): NR - Asymptomatic, detected by exam (n [%]): NR - Asymptomatic, detected by imaging (n [%]): NR Additional data used for diagnosis:

NR 

1) CYFRA 21-1 cutoff 4.7 mcg/L – ovarian cancer vs. healthy controls: Se 41%, Sp 95%. 

 Dis+ Dis- Tot T+ 15 2 17 

T- 22 38 60 Tot 37 40 77  Lower Upper  Value 95% CI 95% CI Se 40.5% 24.7% 56.4% Sp 95.0% 88.2% 100.0% PPV 88.2% 72.9% 100.0% NPV 63.3% 51.1% 75.5% 

2) CYFRA 21-1 – ovarian cancer vs. benign ovarian cyst: AUC = 0.86. Individual cutoffs not given. 3) Data on other test accuracy measures (correlation coefficient, interclass correlation,etc.): Intra-assay correlation coefficient is 6.5% at a concentration of 3 mcg/L CYFRA 21-1. 

Comme- Canceage-ma- No cuCYFRA given to Quality ReferenVerificahealthy to checfollow uTest reSample StatisticBlindingDefinitioGrade: 
A-119

Evidence Table 2 – Question 2 (continued) Study Study Design Patients Clinical Presentation Results CommTsukishiro, Suzumori, Nishikawa, et al., 2005 #870 

Geographical location:

Nagoya, Japan Study dates: 1997-2004 Size of population:118 Type of population: Adnexal mass Healthy controls Genomic test(s) used: Secretory leukocyte protease inhibitor (SLPI) Reference standard: Surgical pathology Reference standard applied to all test negatives?: All except healthy controls Test reliability established?:NoStatistical tests used:

Se,Sp, ROC Blinding: No Definition of positive and negative on screening test:SLPI > 50 ng/mL CA-125 > 30 U/mL

Age:Malignant: Mean (SD): 54 (1.8) Benign: Mean (SD): 50 (4.3) Control: Mean (SD): 49 (4.8) Menopausal status (n [%]):Pre (< 50): 14 Post (> 50): 41 Race/ethnicity (n [%]):NR Risk factors (n [%]):NRDiagnoses (n [%]): Ovarian cancer: 55 (47%)Benign ovarian mass: 25 (21%) Healthy controls: 38 (32%) Inclusion criteria: Pre-operative for assessment of ovarian mass, healthy controls Exclusion criteria:“Inflammatory states”, positive blood cultures, CRP > 5 mg/dl, peripheral leukocyte counts >10,000, borderline tumors 

Screening only (n [%]): NR Diagnosis of mass: - Symptomatic (n [%]): NR - Asymptomatic, detected by exam (n [%]): NR - Asymptomatic, detected by imaging (n [%]): NR Additional data used for diagnosis:

NR 

1) SLPI cutoff 50 ng/mL – ovarian cancer vs. benign cysts: 

 Dis+ Dis- Tot T+ 42 5 47 

T- 13 20 33 Tot 55 25 80  Lower Upper  Value 95% CI 95% CI Se 76.0% 64.7% 87.3% Sp 80.0% 64.3% 95.7% PPV 89.4% 80.5% 98.2% NPV 60.6% 43.9% 77.3% 

2) SLPI > 50 ng/mL and CA-125 > 30 U/mL – ovarian cancer vs. benign ovarian cysts: 

 Dis+ Dis- Tot T+ 52 0 52 T- 3 25 28 Tot 55 25 80  Lower Upper  Value 95% CI 95% CI Se 95.0% 89.2% 100.0% Sp 100.0% 88.0% 100.0% PPV 100.0% 94.2% 100.0% NPV 89.3% 77.8% 100.0% 

3) CA-125 > 30U/mL – ovarian cancer vs. benign ovarian cysts: 

 Dis+ Dis- Tot T+ 48 4 52 T- 7 21 28 Tot 55 25 80  Lower Upper  Value 95% CI 95% CI Se 87.0% 78.1% 95.9% Sp 84.0% 69.6% 98.4% 

CommeQuality Referenhealthy VerificacontrolsTest reSample StatisticBlindingDefinitioGrade: 
A-120

Evidence Table 2 – Question 2 (continued) Study Study Design Patients Clinical Presentation Results Comm

PPV 92.3% 85.1% 99.6% NPV 75.0% 59.0% 91.0% 

Udagawa, Aoki, Ito, et al., 1998 #6640 

Geographical location:

Tokyo, Japan Study dates: NR 

Size of population:

860 Type of population: Multiple clinical groups, some with cancer (see Diagnoses) Genomic test(s) used: Serum GAT (galactosyltransferase associated with tumor) Reference standard: NR Reference standard applied to all test negatives?: No Test reliability established?:NoStatistical tests used:

Se, Sp, ROC Blinding: NR Definition of positive and negative on screening test:GAT > 16 U/mL (2 SDs over the mean) CA-602 > 63 U/mL 

Age:Range: 10-89 Menopausal status (n [%]):NRRace/ethnicity (n [%]):NR Risk factors (n [%]):NR Diagnoses (n [%]): Ovarian cancer: 134 (16%) Benign ovarian mass: 193 (22%) Healthy controls: 294 (34%) Pregnant: 32 (4%) Endometriosis: 110 (13%)Cervical cancer: 40 (5%) Endometrial cancer: 48 (6%) Cancer metastatic to ovary: 9 (1%) Inclusion criteria:

NR Exclusion criteria:NR 

Screening only (n [%]): 0 Diagnosis of mass: NR - Symptomatic (n [%]): NR - Asymptomatic, detected by exam (n [%]): NR - Asymptomatic, detected by imaging (n [%]): NR Additional data used for diagnosis:NR 

1) GAT cutoff 16 U/mL – ovarian cancer vs. benign ovarian tumors: 

 Dis+ Dis- Tot T+ 68 13 81 

T- 68 285 353 Tot 136 298 434  Lower Upper  Value 95% CI 95% CI Se 50.0% 41.6% 58.4% Sp 95.6% 93.3% 97.9% PPV 84.0% 76.0% 91.9% NPV 80.7% 76.6% 84.9% 

AUC of GAT = 0.791 2) GAT/CA602/CA546 – ovarian malignancy vs. benign ovarian mass: The second and third markers each have one reference to their discovery as putative ovarian cancer markers. 

 Dis+ Dis- Tot T+ 115 112 227 T- 21 186 207 Tot 136 298 434  Lower Upper  Value 95% CI 95% CI Se 84.6% 78.5% 90.7% Sp 62.4% 56.9% 67.9% PPV 50.7% 44.2% 57.2% NPV 89.9% 85.7% 94.0% 

3) Data on other test accuracy measures (correlation coefficient, interclass correlation, 

Comme- Did nofrom maovary in - Meno- No aggroups Quality ReferenVerificaTest reSample StatisticBlindingDefinitioGrade: 
A-121

Evidence Table 2 – Question 2 (continued) Study Study Design Patients Clinical Presentation Results Comm

CA-546 > 12U/mL etc.): 

Coefficient of variation < 5%. 

van HaaftenDay, Shen, Xu, et al., 2001 #4010 

Geographical location:

Sydney, Australia; Houston, TX; Durham, NC; Groningen, The Netherlands Study dates:

May 1989-Oct 1993 for some subjects (not reported for all) Size of population:

398 Type of population: Adnexal mass; 87 healthy subjects Genomic test(s) used: CA-125-II M-CSF OVX1 Reference standard: Surgical pathology Reference standard applied to all test negatives?: No Test reliability established?:

Referenced Statistical tests used:

Logistic regression Blinding: NR Definition of positive 

Age: NRMenopausal status (n [%]):NR Race/ethnicity (n [%]):

NR Risk factors (n [%]):NR Diagnoses (n [%]): Ovarian cancer: 175 (44.1%) Borderline: 28 (7.1%) Benign ovarian mass: 77 (19.4%) Healthy controls: 117 (29.5%) Inclusion criteria:NR Exclusion criteria:NR 

Screening only (n [%]): NR Diagnosis of mass: - Symptomatic (n [%]): NR - Asymptomatic, detected by exam (n [%]): NR - Asymptomatic, detected by imaging (n [%]): NR Additional data used for diagnosis:NR 

1) CA-125 > 35 U/mL, including borderline as Dis+: 

 Dis+ Dis- Tot T+ 153 27 180 

T- 50 167 217 Tot 203 194 397  Lower Upper  Value 95% CI 95% CI Se 75.4% 69.4% 81.3% Sp 86.1% 81.2% 91.0% PPV 85.0% 79.8% 90.2% NPV 77.0% 71.4% 82.6% 

2) OVX1 > 7.2 U/mL, cancer + borderline vs. benign and control: 

 Dis+ Dis- Tot T+ 38 16 54 T- 165 178 343 Tot 203 194 397  Lower Upper  Value 95% CI 95% CI Se 18.7% 13.4% 24.1% Sp 91.8% 87.9% 95.6% PPV 70.4% 58.2% 82.5% NPV 51.9% 46.6% 57.2% 

3) M-CSF > 3.5 ng/mL, cancer + borderline vs. benign and control: 

 Dis+ Dis- Tot T+ 69 28 97 T- 134 166 300 Tot 203 194 397 

Comme- Benigreporte- 95% C- Sensitinvasive Quality ReferenVerificaTest reSample StatisticBlindingDefinitioGrade: 
A-122

Evidence Table 2 – Question 2 (continued) Study Study Design Patients Clinical Presentation Results Comm

and negative on screening test: Positive if: CA-125 II > 35 U/mL OVX1 > 7.2 M-CSF > 3.5

 Lower Upper  Value 95% CI 95% CI Se 34.0% 27.5% 40.5% 

Sp 85.6% 80.6% 90.5% PPV 71.1% 62.1% 80.2% NPV 55.3% 49.7% 61.0% 

4) All 3 markers, any test above threshold = positive, cancer + borderline vs. benign + healthy control: 

 Dis+ Dis- Tot T+ 155 52 207 T- 48 142 190 Tot 203 194 397  Lower Upper  Value 95% CI 95% CI Se 76.4% 70.5% 82.2% Sp 73.2% 67.0% 79.4% PPV 74.9% 69.0% 80.8% NPV 74.7% 68.6% 80.9% 
A-123

Evidence Table 2 – Question 2 (continued) Study Study Design Patients Clinical Presentation Results CommWakahara, Kikkawa, Nawa, et al., 2001 #4120 

Geographical location:

Toyohashi, Japan Study dates: 1994-1999 Size of population:292 Type of population: Adnexal mass Genomic test(s) used: CA 19-9, CA 72-4, CA125 Reference standard: Surgical pathology Reference standard applied to all test negatives?: Yes Test reliability established?:YesStatistical tests used: Se, Sp, ROC 

Blinding: NR Definition of positive and negative on screening test:Abnormal defined as: CA-125 > 35U/mL CA-19-9 > 37U/mL CA-72-4 > 4 U/mL 

Age:Mean (SD): 40.3 - Benign: 37.5 (13.1) - LMP: 41 (19.9)  - Cancer: 48.8 (14.7) Range: 11-79 Menopausal status (n [%]):NRRace/ethnicity (n [%]):NR Risk factors (n [%]):NRDiagnoses (n [%]): Ovarian cancer: 66 (23%)Borderline: 18 (6.1%) Benign ovarian mass: 208 (71%) Inclusion criteria:

Surgery for an adnexal mass Exclusion criteria:NR 

Screening only (n [%]): 292 (assumed)Diagnosis of mass: - Symptomatic (n [%]): NR - Asymptomatic, detected by exam (n [%]): NR - Asymptomatic, detected by imaging (n [%]): NR Additional data used for diagnosis:Transvaginal sonography; 4 patterns used to determine/describe malignancy 

1) CA-19-9 cutoff 37 U/mL, ovarian cancer versus benign ovarian mass: AUC for CA-19-9 = 0.475 

 Dis+ Dis- Tot T+ 24 78 102 

T- 42 127 169 Tot 66 205 271  Lower Upper  Value 95% CI 95% CI Se 36.4% 24.8% 48.0% Sp 62.0% 55.3% 68.6% PPV 23.5% 15.3% 31.8% NPV 75.1% 68.6% 81.7% 

2) CA-72-4 cutoff 4 U/mL, ovarian cancer versus benign ovarian mass. AUC for CA-72-4 = 0.645 

 Dis+ Dis- Tot T+ 20 18 38 T- 20 108 128 Tot 40 126 166  Lower Upper  Value 95% CI 95% CI Se 50.0% 34.5% 65.5% Sp 85.7% 79.6% 91.8% PPV 52.6% 36.8% 68.5% NPV 84.4% 78.1% 90.7% 

Comme- The sttumor mbetter tbenign masses Quality ReferenVerificaTest reSample StatisticBlindingDefinitioGrade: 
A-124

Evidence Table 2 – Question 2 (continued) Study Study Design Patients Clinical Presentation Results CommWarwas, Haczynska, Gerber, 1997 #7160 

Geographical location:

Wroclaw, Poland Study dates:

1989-1994 Size of population:

127 Type of population: Ovarian cancers, benign ovarian cysts, 2 control groups Genomic test(s) used: Cathepsin B-like activity Antipapain activity Reference standard: Surgical pathology None for healthy controls Reference standard applied to all test negatives?: No Test reliability established?:NoStatistical tests used: Se 

Blinding: NRDefinition of positive and negative on screening test:The mean value for benign ovarian cysts plus 2 SDs = 102.3 mIU/L (cathepsin b); 117.9 nU/L (anti-papain activity) 

Age:Cancer: Median: 51 Range: 14-82 Benign cyst: Median: 48 Range: 35-60 Healthy controls: Median: 39 Range: 28-46 Menopausal status (n [%]):NRRace/ethnicity (n [%]):

NR Risk factors (n [%]):NR Diagnoses (n [%]): Ovarian cancer: 75 (59%)Benign ovarian mass: 17 (13%) Healthy controls: 15 (12%) Uterine fibroids: 20 (16%) Inclusion criteria:Surgery for ovarian mass, uterine fibroids, or control group (no surgery) Exclusion criteria:NR 

Screening only (n [%]): NR Diagnosis of mass: - Symptomatic (n [%]): NR - Asymptomatic, detected by exam (n [%]): NR - Asymptomatic, detected by imaging (n [%]): NR 

Additional data used for diagnosis:NR 

1) Cathepsin B, cutoff 102.3 – ovarian cancer vs. benign ovarian cysts: Se: All stages 78.6 %; stage I 60% 

 Dis+ Dis- Tot T+ 59 0 59 

T- 16 17 33 Tot 75 17 92  Lower Upper  Value 95% CI 95% CI Se 78.6% 69.3% 87.9% Sp 100.0% 82.4% 100.0% PPV 100.0% 94.9% 100.0% NPV 51.5% 34.5% 68.6% 

2) Antipapain activity, cutoff 117.9 mU/L – ovarian cancers vs. benign cysts: Se: All stages 22.6%, stage I 50% 

 Dis+ Dis- Tot T+ 17 0 17 T- 58 17 75 Tot 75 17 92  Lower Upper  Value 95% CI 95% CI Se 22.6% 13.1% 32.1% Sp 100.0% 82.4% 100.0% PPV 100.0% 82.4% 100.0% NPV 22.7% 13.2% 32.1% 

Comme- ControQuality ReferenVerificahave suTest reSample cysts) StatisticBlindingDefinitioGrade: 
A-125

Evidence Table 2 – Question 2 (continued) Study Study Design Patients Clinical Presentation Results CommWeitzel, Ding, Larson, et al., 2000 #11800 

Geographical location:

Duarte, CA Study dates: NR 

Size of population:

244 Type of population: Ovarian cancer or control Genomic test(s) used: Rare HRAS1 allele polymorphisms Reference standard: Surgical pathology or clinical status (controls) Reference standard applied to all test negatives?: No (controls did not have follow up or surgery) Test reliability established?:NoStatistical tests used:

Odds ratios Blinding: NR Definition of positive and negative on screening test:Presence or absence of HRAS1 polymorphism as determined by PCR; a number of techniques are described depending on the type of sample obtained (blood, tissue, or both) 

Age:Ovarian cancer: Mean (SD): 53.2 (13.3) Controls: Mean (SD): 52.5 (15.4) Menopausal status (n [%]):NR Race/ethnicity (n [%]):Caucasian 244 (100%)

Risk factors (n [%]):NRDiagnoses (n [%]): Ovarian cancer: 136 (56%) Healthy controls: 108 (44%) Inclusion criteria:- Cases: epithelial ovarian cancer - Controls: age and race matched, no history of cancer and ovaries intact Exclusion criteria:Prior oophorectomy, history of cancer (for control group) 

Screening only (n [%]): NR Diagnosis of mass: - Symptomatic (n [%]): NR - Asymptomatic, detected by exam (n [%]): NR - Asymptomatic, detected by imaging (n [%]): NR Additional data used for diagnosis:NR 

1) Existence of a rare HRAS1 allele; ovarian cancer vs. healthy controls; OR for having cancer with a rare allele is 1.76 (95%CI 1.08 to 2.87). Risk appears to increase with the presence of 2 rare alleles (OR 2.86, 0.75 to 10.94). 

 Dis+ Dis- Tot T+ 29 14 43 

T- 107 94 201 Tot 136 108 244  Lower Upper  Value 95% CI 95% CI Se 21.5% 14.6% 28.4% Sp 86.6% 80.2% 93.0% PPV 67.4% 53.4% 81.4% NPV 46.8% 39.9% 53.7% 

Comme- Some blood; owere alerror if deletion The auton 24 sand tissconcludconcord- Authostatus mrisk. Quality ReferenVerificahave suTest reSample StatisticBlindingDefinitioGrade: 
A-126

Evidence Table 2 – Question 2 (continued) Study Study Design Patients Clinical Presentation Results CommYuce, Baykal, Genc, et al., 2001 #4220 

Geographical location:

Ankara, Turkey Study dates: Dec 1998-Jun 1999 Size of population: 65 Type of population: Adnexal mass Genomic test(s) used: LDH – serum and peritoneal fluid Reference standard: Surgical pathology Reference standard applied to all test negatives?: Yes Test reliability established?:YesStatistical tests used:

Se, Sp, PPV, NPVBlinding: NR Definition of positive and negative on screening test:Serum LDH: 512.28 U/mL Peritoneal LDH: 650 U/mL Serum CA-125: 129 U/mL

Age:Benign masses: Mean: 45.3 Range: 22-77 Ovarian cancer: Mean: 58.6 Range: 20-78 Menopausal status (n [%]):NRRace/ethnicity (n [%]):NR Risk factors (n [%]): NR Diagnoses (n [%]): Ovarian cancer: 27 (41.5%) Benign ovarian mass: 38 (58.5%) Inclusion criteria:Ovarian mass for surgery Exclusion criteria:NR 

Screening only (n [%]): NoneDiagnosis of mass: - Symptomatic (n [%]): NR - Asymptomatic, detected by exam (n [%]): NR - Asymptomatic, detected by imaging (n [%]): NR Additional data used for diagnosis:NR 

1) Serum LDH cutoff 512.28 U/mL – ovarian 

cancer vs. benign ovarian mass: 

 Dis+ Dis- Tot T+ 5 2 7 

T- 22 36 58 Tot 27 38 65  Lower Upper  Value 95% CI 95% CI Se 18.5% 3.9% 33.1% Sp 94.7% 87.6% 100.0% PPV 71.4% 38.0% 100.0% NPV 62.1% 49.6% 74.6% 

2) Peritoneal LDH cutoff 650 U/mL – ovarian cancer vs. benign ovarian mass: 

 Dis+ Dis- Tot T+ 13 3 16 T- 14 35 49 Tot 27 38 65  Lower Upper  Value 95% CI 95% CI Se 48.1% 29.3% 66.9% Sp 92.1% 83.5% 100.0% PPV 81.3% 62.1% 100.0% NPV 71.4% 58.8% 84.1% 

3) Peritoneal LDH cutoff 650 U/mL and serum cA125 cutoff 129 U/mL – ovarian cancer vs. benign ovarian mass: 

 Dis+ Dis- Tot T+ 20 4 24 T- 7 34 41 Tot 27 38 65  Lower Upper  Value 95% CI 95% CI Se 74.1% 57.6% 90.6% 

Comme- Cutoff“statistideterm- N is fa- Peritoavailabwashingcases. variable samplelower Lcontrolsenhanccancers- Authoof CA-1significaQuality ReferenVerificaTest reSample StatisticBlindingDefinitioGrade: 
A-127

Evidence Table 2 – Question 2 (continued) Study Study Design Patients Clinical Presentation Results Comm

Sp 89.9% 80.3% 99.5% 

PPV 83.3% 68.4% 98.2% NPV 82.9% 71.4% 94.4% 

Zakrzewska, Borawska, Poznanski, et al., 1999 #5960 

Geographical location:

Bialystok, Poland Study dates: NR 

Size of population:

96 Type of population: Patients with ovarian neoplasms Genomic test(s) used: CEA, CA-72-4, CA-125 Reference standard: Surgical pathology Reference standard applied to all test negatives?: Yes Test reliability established?:YesStatistical tests used:

Wilcoxin test for significance Blinding: NR Definition of positive and negative on screening test:CA-125: < 35 U/mL CA72-4: < 9.8 U/mL CEA: < 5 ng/mL 

Age:Cancer: Mean: 47 Range: 38-72 Menopausal status (n [%]):NRRace/ethnicity (n [%]):NRRisk factors (n [%]):NRDiagnoses (n [%]): Ovarian cancer: 70 (73%)Benign ovarian neoplasm: 26 (27%) Inclusion criteria:

Ovarian cancer or benign ovarian neoplasm Exclusion criteria:NR 

Screening only (n [%]): NR Diagnosis of mass: - Symptomatic (n [%]): NR - Asymptomatic, detected by exam (n [%]): NR - Asymptomatic, detected by imaging (n [%]): NR Additional data used for diagnosis:NR 

1) CA72-4 cutoff 9.8 U/mL – ovarian cancer vs. benign ovarian neoplasm: 

 Dis+ Dis- Tot T+ 39 0 39 T- 31 26 57 Tot 70 26 96  Lower Upper  Value 95% CI 95% CI Se 55.7% 44.1% 67.4% Sp 100.0% 88.5% 100.0% PPV 100.0% 92.3% 100.0% NPV 45.6% 32.7% 58.5% 

2) CEA cutoff 5 ng/mL – ovarian cancer vs. benign ovarian neoplasm: 

 Dis+ Dis- Tot T+ 7 0 7 T- 63 26 89 Tot 70 26 96  Lower Upper  Value 95% CI 95% CI Se 10.0% 3.0% 17.0% Sp 100.0% 88.5% 100.0% PPV 100.0% 57.1% 100.0% NPV 29.2% 19.8% 38.7% 

Comme- Authosensitivdetectinof ovarihave remanageQuality ReferenVerificaTest reSample StatisticBlindingDefinitioGrade: 
A-128

Evidence Table 2 – Question 2 (continued) Study Study Design Patients Clinical Presentation Results CommZhang, Bast, Yu, et al., 2004 #790 

Geographical location:

Baltimore, MD Study dates: NR 

Size of population:

503 for proteomic profiling (discovery set) + 142 for testing identified markers (validation set) = 645 Type of population: Ovarian cancers versus various clinical groups Genomic test(s) used: Proteomics: SELDI mass spectrometry (Ciphergen ProteinChip Biomarker System) Reference standard: NR Reference standard applied to all test negatives?: NR Test reliability established?:NoStatistical tests used:

After identification of 3 biomarkers using proteomics platform, a multivariate model was constructed using the 3 biomarkers to predict malignancy, with or without incorporation of CA-125 information. 

Age:Ovarian cancer, test set: Mean (SD): 52 (16) Median: 54 Menopausal: 57.9% Healthy control, test set: Mean (SD): 39 (11) Median: 38 Menopausal: 20.5% Ovarian cancer, validation set: Mean (SD): 57 (13) Median: 57 Menopausal: 74.6% Healthy control, validation set: Mean (SD): 44 (10) Median: 45 Menopausal: 34.9% Benign mass, validation set: Mean (SD): 53 (16) Median: 51 Menopausal: 55.5% Menopausal status (n [%]):See aboveRace/ethnicity (n [%]):NR Risk factors (n [%]):NRDiagnoses (n [%]): Ovarian cancer: 194 (30%) Borderline: 28 (4%) Recurrent ovarian cancer: 14 (2%) 

Screening only (n [%]): NR Diagnosis of mass: - Symptomatic (n [%]): NR - Asymptomatic, detected by exam (n [%]): NR - Asymptomatic, detected by imaging (n [%]): NR Additional data used for diagnosis: NR 

1) Three-biomarker multivariate model: ovarian cancer vs. healthy controls (validation set): 

 Dis+ Dis- Tot T+ 107 2 109 T- 31 61 92 Tot 138 63 201  Lower Upper  Value 95% CI 95% CI Se 77.5% 70.6% 84.5% Sp 96.8% 92.5% 100.0% PPV 98.2% 95.6% 100.0% NPV 66.3% 56.6% 76.0% 

Comme- Age dhealthy - The 3 “acute pbe secrQuality ReferenVerificaTest revalidatiSample StatisticBlindingDefinitio(a predcutoffs Grade: 
A-129

Evidence Table 2 – Question 2 (continued) Study Study Design Patients Clinical Presentation Results Comm

Models were evaluated using Se, Sp, ROC Blinding: NR Definition of positive and negative on screening test:Used a combination of variables identified from multiple logistic regression based on mass spectroscopy. Peaks occurring at m/z 12,828; 28043 and 3,272 were used. 

Benign pelvic mass: 166 (26%) Healthy controls: 183 (28%) Other: - Breast cancer: 20 (3%) - Colon cancer: 20 (3%) - Prostate cancer: 20 (3%)Inclusion criteria:Clinical groups listed above Exclusion criteria:NR 
A-130

Evidence Table 3 – Question 4: What is the evidence that genomic testing in women with clinical suspicion of ovarian ca

diagnosed ovarian cancer changes clinical management and leads to improved health outcomes?

Study Study Design Patients Outcome Measures Results ComAyhan, Tuncer, and Ayhan, 1998 #6460 

Geographical location: 

Ankara, Turkey Study dates:

1988-1994 Study type:

Cohort Size of population:48 Genomic test(s) used: p53 - immunohistochemistry Reference standard: Surgical pathology Test reliability established?:Yes Statistical tests used:

Chi square Definition of positive and negative on screening test:NR 

Age:Mean: 52.8 Menopausal status (n [%]):NRRace/ethnicity (n [%]):

NR Risk factors (n [%]):NRDiagnoses (n [%]): Ovarian cancer: 48 (100%)Treatment (n [%]): Surgery: 48 (100%) Chemotherapy (platinum): 48 (100%) Inclusion criteria:Ovarian carcinoma subjected to primary surgery and cisplatinumbased chemotherapy followed by second-look laparotomy Exclusion criteria:NR 

Use of test results: No change in management (prediction only) Outcomes measured: Cancer progression on second-look laparotomy (SLL) 

1) p53 expression to predict residual disease at SLL – all patients: Out+ = positive SLL Out- = negative SLL T+ = p53 overexpressed T- = p53 not overexpressed 

 Out+ Out- Tot T+ 12 8 20 

T- 10 18 28 Tot 22 26 48  Lower Upper  Value 95% CI 95% CI Se 54.5% 33.7% 75.4% Sp 69.2% 51.5% 87.0% PPV 60.0% 38.5% 81.5% NPV 64.3% 46.5% 82.0% 

2) p53 expression to predict residual disease at SLL – serous stage III only (T/Out as described above): 

 Out+ Out- Tot T+ 9 5 14 T- 6 10 16 Tot 15 15 30  Lower Upper  Value 95% CI 95% CI Se 60.0% 35.2% 84.8% Sp 66.7% 42.8% 90.5% PPV 64.3% 39.2% 89.4% NPV 62.5% 38.8% 86.2% 

CommCohorstage pts StQualitFor coUnbia(prospsubjecLarge AdequcohortUse otest: + Use oascertAdequCompAnalysand reGrade
A-131

Evidence Table 3 – Question 4 (continued) Study Study Design Patients Outcome Measures Results ComBaekelandt, Holm, Trope, et al., 1999 #6000 

Geographical location: 

Oslo, Norway Study dates:

1988-1993 Study type:

Cohort Size of population:185 Genomic test(s) used: Cathepsin-D nm23 Immunohistochemistry (IHC) Reference standard: Surgical pathology Test reliability established?:YesStatistical tests used:

Differences in proportions were evaluated by the chisquare or Fisher’s exact test. Disease-free and corrected survival rates using Kaplan-Meier, logrank test, and Cox proportional hazards regression model. Definition of positive and negative on screening test:Positive > 10% moderate or strong staining 

Age:Median: 54 Range: 21-70 Menopausal status (n [%]):NRRace/ethnicity (n [%]):NRRisk factors (n [%]):NRDiagnoses (n [%]): Ovarian cancer: 185 (100%) Treatment (n [%]): Surgery: First surgery, 185 (100%) Chemotherapy (platinum): 185 (100%) Other (epirubicin): 185 (100%) Inclusion criteria:

Consecutive patients in a multicenter trial on consolidation treatment after second-look laparotomy in stage III ovarian cancer patients. Patient age < 71 years, Karnofsky index ≥ 60, histologically verified and previously untreated stage III epithelial ovarian cancer and s-creatinin < 115 μmol/L. Exclusion criteria: NR 

Use of test results: No change in management (prediction only) Outcomes measured: Cancer mortality Cancer progression or regression with primary chemotherapy as assessed at time of second-look laparotomy (SLL) 

1) Cathespin-D levels to predict residual disease after to primary chemotherapy: Out+ = macroscopic residual or no response to chemo Out- = complete pathologic response or microscopic residual disease only T+ = cathepsin D positive IHC T- = cathepsin D negative IHC 

 Dis+ Dis- Tot T+ 78 22 100 T- 61 14 75 Tot 139 36 175  Lower Upper  Value 95% CI 95% CI Se 56.1% 47.9% 64.4% Sp 38.9% 23.0% 54.8% PPV 78.0% 69.9% 86.1% NPV 18.7% 9.8% 27.5% 

2) nm23 levels to predict residual disease after primary chemotherapy (T/Out as described above): 

 Dis+ Dis- Tot T+ 100 24 124 T- 39 12 51 Tot 139 36 175  Lower Upper  Value 95% CI 95% CI Se 71.9% 64.5% 79.4% Sp 33.3% 17.9% 48.7% PPV 80.6% 73.7% 87.6% NPV 23.5% 11.9% 35.2% 

Comm- Samal., 19- Lumpathol“microsomewappropatholSLL) vdiseasQualitFor coUnbia(prospsubjecLarge AdequcohortUse otest: + Use oascertAdequCompAnalysand reGrade
A-132

Evidence Table 3 – Question 4 (continued) Study Study Design Patients Outcome Measures Results ComBaekelandt, Kristensen, Nesland, et al., 1999 #830 

Geographical location: 

Oslo, Norway Study dates:

1988-1993 Study type:

Cohort Size of population:185 Genomic test(s) used: P53, mdm2, bcl-2 by Immunohistochemistry (IHC) Reference standard: Surgical pathology Test reliability established?:YesStatistical tests used:

Differences in proportions were evaluated by the chisquare or Fisher’s exact test. Disease-free and corrected survival rates using Kaplan-Meier, logrank test, and Cox proportional hazards regression model. Definition of positive and negative on screening test:For p53 and bcl-2: immunostaining of at least 5% of tumor cells For mdm2: not specified 

Age:Median: 54 Range: 21-70 Menopausal status (n [%]):NRRace/ethnicity (n [%]):NRRisk factors (n [%]):NRDiagnoses (n [%]): Ovarian cancer: 185 (100%) Treatment (n [%]): Surgery: First surgery 185 (100%) Chemotherapy (platinum): 185 (100%) Other (epirubicin): 185 (100%) Inclusion criteria:

Consecutive patients in a multicenter trial on consolidation treatment after second-look laparotomy (SLL) in stage III ovarian cancer patients. Patient age < 71 years, Karnofsky index ≥ 60, histologically verified and previously untreated stage III epithelial ovarian cancer and s-creatinin < 115 μmol/L. Exclusion criteria:NR 

Use of test results: No change in management (prediction only) Outcomes measured: Cancer mortality Cancer progression or regression 

Findings at SLL (complete pathologic response, microscopic or macroscopic disease) 

1) p53 expression to predict residual disease at SLL: Out+ = macroscopic disease at SLL Out- = complete pathologic response or microscopic residual only T+ = p53 expression ≥ 5% tumor cells T- = p53 negative or < 5% staining 

 Dis+ Dis- Tot T+ 64 20 84 

T- 75 16 91 Tot 139 36 175  Lower Upper  Value 95% CI 95% CI Se 46.0% 37.8% 54.3% Sp 44.4% 28.2% 60.7% PPV 76.2% 67.1% 85.3% NPV 17.6% 9.8% 25.4% 

2) mdm2 expression to predict residual disease at SLL: Out+/Out- = see above T+ = mdm2 staining (percentage of cells not specified) T- = mdm2 negative (percentage not specified) 

 Dis+ Dis- Tot T+ 25 4 29 T- 114 32 146 Tot 139 36 175  Lower Upper  Value 95% CI 95% CI Se 18.0% 11.6% 24.4% Sp 88.9% 78.6% 99.2% PPV 86.2% 73.7% 98.8% NPV 21.9% 15.2% 28.6% 

Comm- Samal., 19- Cutonegatiarbitramdm2- Lumpathol“microsomewappropatholSLL) vdiseasQualitFor coUnbia(prospsubjecLarge AdequcohortUse otest: +Use oascertAdequCompAnalysand reGrade
A-133

Evidence Table 3 – Question 4 (continued) Study Study Design Patients Outcome Measures Results Com

3) bcl-2 expression to predict residual disease at SLL: Out+/Out- = see above T+ = ≥ 5% of tumor cells stain positive for bcl-2 T- = < 5% tumor cells stain positive for bcl-2 

 Dis+ Dis- Tot T+ 49 19 68 

T- 90 17 107 Tot 139 36 175  Lower Upper  Value 95% CI 95% CI Se 35.3% 27.3% 43.2% Sp 47.2% 30.9% 63.5% PPV 72.1% 61.4% 82.7% NPV 15.9% 9.0% 22.8% 

4) Hazard Ratio or other relevant information: Overall survival was lower in patients with p53 expression and loss of bcl-2 expression using Kaplan Meier analysis. 
A-134

Evidence Table 3 – Question 4 (continued) Study Study Design Patients Outcome Measures Results ComBalbi, Cardone, Passaro, et al., 2005 #90 

Geographical location: 

Naples, Italy Study dates: 1992-2002 Study type: Cohort Size of population:150 Genomic test(s) used: CA-125 Reference standard: Radiographic imaging: size of measurable lesions on CT, MRI, ultrasound, or physical examination. Response, stable disease, or disease progression as determined by WHO criteria. Test reliability established?:Yes Statistical tests used:

Chi-square test Definition of positive and negative on screening test:CA-125 < 30 U/mL considered normal Response to therapy by CA-125 criteria: “50% response” = 50% decrease in CA-125 from pre-treatment level after 

Age: NR Menopausal status (n [%]):NRRace/ethnicity (n [%]):NRRisk factors (n [%]):NR Diagnoses (n [%]): Ovarian cancer: 150 (100%) Treatment (n [%]): Surgery: 150 (100%) Chemotherapy:  - Single agent Platinum: 96 (64%)  - Single agent Taxol: 54 (46%) Inclusion criteria:

Treated with adjuvant chemo after optimal surgery for epithelial carcinoma of the ovary. Had to have CA125 measurements performed on at least three serum samples, with at least one sample having a level more than 40 U/mL at the start of therapy. Maximum period during which a response may occur is the first 6 months after the start of treatment. The final sample had to be at least 28 days after the previous sample. Also needed to have at least one bidimensionally measurable 

Use of test results: No change in management (prediction only) Outcomes measured: Response to treatment where response was defined as two samples where there was a 50% decrease, confirmed by a 4th sample or 

a serial decrease over three sample of greater than 75% 

1) Ability of CA-125 criteria to measure response to platinum when compared to conventional criteria: Out+ = response to chemo by standard criteria Dis -= no response by standard criteria T+ = response by CA-125 criteria T- = no response by CA-125 criteria 

 Out+ Out- Tot T+ 32 2 34 

T- 8 24 32 Tot 40 26 66  Lower Upper  Value 95% CI 95% CI Se 80.0% 67.6% 92.4% Sp 92.3% 82.1% 100.0% PPV 94.1% 86.2% 100.0% NPV 75.0% 60.0% 90.0% 

2) Ability of CA-125 criteria to measure response to paclitaxel when compared to conventional criteria (T/Out defined as above): 

 Out+ Out- Tot T+ 22 2 24 T- 4 19 23 Tot 26 21 47  Lower Upper  Value 95% CI 95% CI Se 84.6% 70.7% 98.5% Sp 90.5% 77.9% 100.0% PPV 91.7% 80.6% 100.0% NPV 82.6% 67.1% 98.1% 

Comm- This 125 to Many have nare nopaper supporesponalone. QualitFor coUnbia(prospsubjecLarge AdequcohortUse otest: + Use oascertAdequCompAnalysand reGrade
A-135

Evidence Table 3 – Question 4 (continued) Study Study Design Patients Outcome Measures Results Com

2 samples “75% response” = 75% drop in CA-125 from pretreatment level after 3 samples 

lesion as evidenced by CT, MRI, US or physical exam. Exclusion criteria:NR 

Berchuck, Iversen, Lancaster, et al., 2004 #1740 

Geographical location: 

Durham, NC Study dates:

1988-2001 Study type:

Cohort Size of population:49 Genomic test(s) used: Microarray analysis using Affymetrix U133A GeneChip Reference standard: Degree of surgical cytoreduction (optimal = all residual tumor nodules are < 1 cm in diameter; suboptimal = at least one tumor nodule > 2 cm) Test reliability established?:

NR Statistical tests used:

Multivariable predictive modelling; within-sample validation Definition of positive and negative on 

Age:Median: 63 (optimal debulked), 57 (suboptimally debulked) Menopausal status (n [%]):NRRace/ethnicity (n [%]):White: 17 (89%) of optimal and 18 (72%) of suboptimalBlack: 2 (11%) of optimal, and 3 (12%) or suboptimalRisk factors (n [%]):NRDiagnoses (n [%]): Ovarian cancer: 49 (100%)Treatment (n [%]): Surgery: 49 (100%) Chemotherapy (platinum): 49 (100%) Inclusion criteria:Treated for serous ovarian cancer, all patients were < 75 years, and none had died of causes other than ovarian cancer Exclusion criteria:NR 

Use of test results: No change in management (prediction only) Outcomes measured: Optimal or suboptimal debulking 

1) Microarray for prediction of ability to debulk stage III/IV cancer (n = 44): Out+ = optimal debulking performed Out- = suboptimal debulking performed T+ = chip predicts optimal debulking T- = chip predicts suboptimal debulking 

 Out+ Out- Tot T+ 12 5 17 

T- 7 20 27 Tot 19 25 44  Lower Upper  Value 95% CI 95% CI Se 63.2% 41.5% 84.8% Sp 80.0% 64.3% 95.7% PPV 70.6% 48.9% 92.2% NPV 74.1% 57.5% 90.6% 

Comm- No sone-oQualitFor coUnbia(prospsubjecLarge AdequcohortUse otest: - Use oascertAdequCompAnalysand reGrade
A-136

Evidence Table 3 – Question 4 (continued) Study Study Design Patients Outcome Measures Results Com

screening test:Optimal debulking prediction 

Berek, Chung, Kaldi, et al., 1991 #12230 

Geographical location: Los Angeles, CA Study dates:NR Study type: Cohort Size of population:

48 Genomic test(s) used: IL-6 Reference standard: Surgical pathology Test reliability established?:YesStatistical tests used:

Student’s t-test Definition of positive and negative on screening test:IL-6 > 0.20 U/mL elevated 

Age: NR Menopausal status (n [%]):NRRace/ethnicity (n [%]):

NR Risk factors (n [%]):NRDiagnoses (n [%]): Ovarian cancer: 36 (66%) Healthy controls: 12 (34%) Treatment (n [%]): NR Inclusion criteria:Histologically documented epithelial ovarian cancer; plan to undergo surgery Exclusion criteria:NR 

Use of test results: No change in management (prediction only) Outcomes measured: Cancer mortality Quality of life Quantity of measurable disease at laparotomy 

1) IL-6 to predict macroscopic versus microscopic disease: Out+ = macroscopic disease present Out- = microscopic disease present T+ = IL6 positive T- = IL6 negative 

 Out+ Out- Tot T+ 16 2 18 

T- 5 13 18 Tot 21 15 36  Lower Upper  Value 95% CI 95% CI Se 76.2% 58.0% 94.4% Sp 86.7% 69.5% 100.0% PPV 88.9% 74.4% 100.0% NPV 72.2% 51.5% 92.9% 

2) IL-6 to predict degree of macroscopic disease: Out+ = > 2 cm disease Out- = < 2 cm T+ = IL6 positive T- = IL6 negative 

 Out+ Out- Tot T+ 8 8 16 T- 1 4 5 Tot 9 12 21  Lower Upper  Value 95% CI 95% CI Se 88.9% 68.4% 100.0% Sp 33.3% 6.7% 60.0% PPV 50.0% 25.5% 74.5% NPV 80.0% 44.9% 100.0% 

Comm- Unclprimarlaparosituatiuncleachang- Reascutoff - Used subjecdiseasmicro, QualitFor coUnbia(prospsubjecLarge AdequcohortUse otest: - establUse oascertAdequCompAnalysand reGrade
A-137

Evidence Table 3 – Question 4 (continued) Study Study Design Patients Outcome Measures Results ComDiamandis, Scorilas, Fracchioli, et al., 2003 #2850 

Geographical location: Toronto, Canada Study dates:NR Study type: Case-control Size of population:97 healthy controls 141 benign disease 146 ovarian cancer Genomic test(s) used: Human kallikrien 6 (hK6) CA-125 Reference standard: Surgical pathology Test reliability established?:Yes Statistical tests used:

ROC curve, paired ttests, Kaplan Meier, Cox proportional hazards Definition of positive and negative on screening test:hK6 - 4.2 μg/L 

hK6- 4.4 μg/L CA-125 > 23KU/L low CA-125 23-60 slightly elevated CA-125 > 60KU/L elevated 

Age:Healthy: Mean (SD): 52 Median: 49 Range: 26-72 Benign disease: Mean (SD): 46 Median: 45 Range: 21-76 Ovarian Cancer: Mean (SD): 56 Median: 57 Range: 28-78 Menopausal status Ovarian cancer only (n [%]):103/146 (71%) Race/ethnicity (n [%]):

NR (pooled cases from Italy, Netherlands, Belgium, Finland) Risk factors (n [%]):NR Diagnoses (n [%]): Ovarian cancer: 146 Benign ovarian mass: 141 Healthy controls: 97 Treatment (n [%]): Chemotherapy:  Platinum: 146 Inclusion criteria:None stated Exclusion criteria:None stated 

Use of test results: No change in management (prediction only) Outcomes measured: Residual tumor Recurrence Response to chemotherapy Response to chemotherapy was assessed as follows: complete response was defined as a resolution of all evidence of disease for at least 1 month; a decrease (lasting at least 1 month) of at least 50% in the diameters of all measurable lesions 

without the development of new lesions was termed partial response. Stable disease was defined as a decrease of <25% in the product of the diameters of all measurable lesions, an increase of ≥25% was 

termed as a progressive disease. 

1) hK6 > 4.4 μg/L for predicting presence of 

residual tumor 

 Dis + Dis - Tot T + 43 24 67 

T - 9 52 61 Tot 52 76 128  Lower Upper  Value 95% CI 95% CI Se 82.7% 72.4% 93.0% Sp 68.4% 58.0% 78.9% PPV 64.2% 52.7% 75.7% NPV 85.2% 76.3% 94.1% 

2) hK6 > 4.4 μg/L for predicting debulking success – optimal debulking defined residual disease < 1cm diameter 

 Dis+ Dis- Tot T+ 40 28 68 T- 9 53 62 Tot 49 81 130  Lower Upper  Value 95% CI 95% CI Se 81.6% 70.8% 92.5% Sp 65.4% 55.1% 75.8% PPV 58.8% 47.1% 70.5% NPV 85.5% 76.7% 94.3% 

3) hK6 > 4.4 μg/L for predicting response to chemotherapy – complete/partial remission defined as no disease 

 Dis+ Dis- Tot T+ 17 46 63 T- 4 61 65 Tot 21 107 128  Lower Upper  Value 95% CI 95% CI 

CommIncludof ovaSt III 7QualitFor caValid aUnbiaAppropopulaVerificcanceCompwith reconfouApproanalysStudy normacontroGrade
A-138

Evidence Table 3 – Question 4 (continued) Study Study Design Patients Outcome Measures Results Com

Se 81.0% 64.2% 97.7% 

Sp 57.0% 47.6% 66.4% PPV 27.0% 16.0% 37.9% NPV 93.8% 88.0% 99.7% 

4) Hazard Ratio or other relevant information: hK6 positive – 4.10 (2.28 to 7.36) 

Folk, Botsford, and Musa, 1995 #8260 

Geographical location: 

Syracuse, NY Study dates:

1989-93 Study type:

Not specified – ad hoc Size of population:60 Genomic test(s) used: CA-125 Reference standard: Surgical pathology Clinical outcome Test reliability established?:

Not stated Statistical tests used:

t-test Definition of positive and negative on screening test:CA-125 < 35IU/ml CA-125 < 20IU/ml for response to therapy 

Age:Mean (SD): 55.2 Range: 25-85 Menopausal status (n [%]):NR Race/ethnicity (n [%]):

NR Risk factors (n [%]):NR Diagnoses (n [%]): Ovarian cancer: 60 (100%) Treatment (n [%]): Surgery: 60 Chemotherapy: 60  Variable regimens Inclusion criteria:None specified Exclusion criteria:None specified 

Use of test results: No change in management (prediction only) Outcomes measured: Cancer progression on second-look laparotomy (SLL) 

1) CA-125 (after 3rd course of Chemo) ≥ 35 

to predict residual disease at SLL 

 Dis+ Dis- Tot T+ 3 0 3 

T- 24 18 42 Tot 27 18 45  Lower Upper  Value 95% CI 95% CI Se 11.1% 0.0% 23.0% Sp 100.0% 83.3% 100.0% PPV 100.0% 0.0% 100.0% NPV 42.9% 27.9% 57.8% 

2) CA-125 (after 3rd course of Chemo) ≥ 20 

U/ml 

 Dis+ Dis- Tot T+ 6 5 11 

T- 21 13 34 Tot 27 18 45  Lower Upper  Value 95% CI 95% CI Se 22.2% 6.5% 37.9% Sp 72.2% 51.5% 92.9% PPV 54.5% 25.1% 84.0% NPV 38.2% 21.9% 54.6% 

3) CA-125 (immediately prior to SLL) ≥ 35 U/ml; SLL positive or negative 

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Evidence Table 3 – Question 4 (continued) Study Study Design Patients Outcome Measures Results Com

 Dis+ Dis- Tot T+ 9 0 9 

T- 18 18 36 Tot 27 18 45  Lower Upper  Value 95% CI 95% CI Se 33.3% 15.6% 51.1% Sp 100.0% 83.3% 100.0% PPV 100.0% 66.7% 100.0% NPV 50.0% 33.7% 66.3% 

4) CA-125 (immediately prior to SLL) ≥ 20 U/ml 

 Dis+ Dis- Tot T+ 7 2 9 T- 18 18 36 Tot 25 20 45  Lower Upper  Value 95% CI 95% CI Se 28.0% 10.4% 45.6% Sp 90.0% 76.9% 100.0% PPV 77.8% 50.6% 100.0% NPV 50.0% 33.7% 66.3% 
A-140

Evidence Table 3 – Question 4 (continued) Study Study Design Patients Outcome Measures Results ComGadducci, Cosio, Fanucchi, et al., 2004 #1810 

Geographical location: 

Pisa, Italy 

Study dates:

1996-2000 Study type:

Other (retrospective series) Size of population:71 Genomic test(s) used: CA-125 Reference standard: Surgical pathology Clinical outcome (based on exam, sonography, and radiology) Test reliability established?:

Not stated Statistical tests used:

Chi square, logistic regression Definition of positive and negative on screening test:CA-125 half life ≤ 14 days CA-125 % reduction after 1st cycle of chemo 

≤ 71% 

Age:Mean (SD): 58 Range: 27-73 Menopausal status (n [%]):NR Race/ethnicity (n [%]):Italian Risk factors (n [%]):NR Diagnoses (n [%]): Ovarian cancer: 71 (100%) Treatment (n [%]): Surgery: 71 (100%) Chemotherapy: 71  Platinum: 71  Paclitaxel: 71 Inclusion criteria:Patients with Stage IIc-IV cancer Exclusion criteria:

None stated 

Use of test results: Prediction of outcome of chemotherapy Outcomes measured: Response to treatment Progression-free survival Overall survival 

1) CA-125 half-life ≤ 14 days to predict 

complete response to treatment 

 Dis+ Dis- Tot T+ 26 10 36 

T- 16 19 35 Tot 42 29 71  Lower Upper  Value 95% CI 95% CI Se 61.9% 47.2% 76.6% Sp 65.5% 48.2% 82.8% PPV 72.2% 57.6% 86.9% NPV 54.3% 37.8% 70.8% 

2) CA-125 percentage reduction after first cycle of chemotherapy > 71% to predict complete response to treatment 

 Dis+ Dis- Tot T+ 25 10 35 T- 17 19 36 Tot 42 29 71  Lower Upper  Value 95% CI 95% CI Se 59.5% 44.7% 74.4% Sp 65.5% 48.2% 82.8% PPV 71.4% 56.5% 86.4% NPV 52.8% 36.5% 69.1% 

3) Hazard Ratio or other relevant information: Serum 125 half life Complete response to treatment OR 3.362 (1.178-9.594) Progression free survival HR 2.739 (1.425-6.262) Overall survival HR 3.113 (1.214-7.980) 

CommCase Study differeStudy responwere nwomeevaluaQualitFor coUnbia(prospsubjecLarge AdequcohortUse otest: + Use oascertAdequCompAnalysand reGrade
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Evidence Table 3 – Question 4 (continued) Study Study Design Patients Outcome Measures Results ComGadducci, Ferdeghini, Cosio, et al., 2001 #4180 

Geographical location: 

Pisa Italy Study dates:

NR Study type:

Other (ad hoc) Size of population:60 with cancer 59 with benign disease used to determine cutpoints Genomic test(s) used: CYFRA 21-1 CA-125 Reference standard: Surgical pathology Clinical outcome Test reliability established?:

Not stated Statistical tests used:

Fisher’s exact tests, Mann Whitney U, Spearman rank, logistic regression Definition of positive and negative on screening test:Used the 25,50 and 75 quantiles of pre-operative CYFRA 21-1 – 1.9, 4.8 and 14.4 ng/ml 

Age:Cases: Median: 58.5 Range: 23-80 Benign disease: Median: 39 Range: 20-82 Menopausal status (n [%]):NR Race/ethnicity (n [%]):

NR Risk factors (n [%]):NR Diagnoses (n [%]): Ovarian cancer: 60 Borderline: 59 Info. limited to 39 patients with advanced ovarian cancer Treatment (n [%]): Surgery: 0? Chemotherapy: 39  Platinum: 34 Inclusion criteria:60 consecutive patients with untreated ovarian cancer Exclusion criteria:None stated 

Use of test results: Not used Outcomes measured: Complete responders 

1) No disease – defined as complete responders. Of 39 with advanced (III-IV) disease. 4 did not have SLL so data not presented in paper). CYFRA 21-1 - < 1.9 is negative 

 Dis+ Dis- Tot T+ 20 5 25 

T- 4 6 10 Tot 24 11 35  Lower Upper  Value 95% CI 95% CI Se 83.3% 68.4% 98.2% Sp 54.5% 25.1% 84.0% PPV 80.0% 64.3% 95.7% NPV 60.0% 29.6% 90.4% 

2) CYFRA 21-1 < 4.8 is negative 

 Dis+ Dis- Tot T+ 16 2 18 T- 8 9 17 Tot 24 11 35  Lower Upper  Value 95% CI 95% CI Se 66.7% 47.8% 85.5% Sp 81.8% 59.0% 100.0% PPV 88.9% 74.4% 100.0% NPV 52.9% 29.2% 76.7% 

3) CYFRA 21-1<14.4 is negative 

 Dis+ Dis- Tot T+ 8 0 8 T- 16 11 27 Tot 24 11 35  Lower Upper  Value 95% CI 95% CI 

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Evidence Table 3 – Question 4 (continued) Study Study Design Patients Outcome Measures Results Com

Se 33.3% 14.5% 52.2% Sp 100.0% 72.7% 100.0% PPV 100.0% 62.5% 100.0% NPV 40.7% 22.2% 59.3% 

Gemer, Lurian, Gdalevich, et al., 2005 #13320 

Geographical location: 

Ashkelon, Beer Sheva, Tzrifin, Haifa, Kfar Saba, Rehovot, Petah Tikva, and Jerusalem, IsraelStudy dates:

NR Study type:

Cohort Size of population:424 Genomic test(s) used: CA-125 Reference standard: Clinical outcome (suboptimal debulking) Test reliability established?:

Yes Statistical tests used:

Sensitivity, specificity, ROC curve Definition of positive and negative on screening test:CA-125 at different cutpoints 

Age: Median: 62 Menopausal status (n [%]):NR Race/ethnicity (n [%]):

NR Risk factors (n [%]):NR Diagnoses (n [%]): Ovarian cancer: - Stage IIIA 25 (5.9%) - Stage IIIB 75 (17.7%) - Stage IIIC 296 (69.8%) - Stage IV 28 (6.6%) Treatment (n [%]): Cytoreductive surgery 100% Inclusion criteria:Stage III and IV ovarian cancer undergoing primary cytoreductive surgery (diameter of largest residual nodule ≤ 1 cm) Exclusion criteria:None specified 

Use of test results: Prediction of debulking (theoretical - retrospective study, no change in management) Outcomes measured: Suboptimal debulking 

1) Test characteristics of CA-125 > 400 to predict suboptimal debulking: 

 Dis+ Dis- Tot T+ 126 104 230 

T- 56 138 194 Tot 182 242 424  Lower Upper  Value 95% CI 95% CI Se 69.2% 62.5% 75.9% Sp 57.0% 50.8% 63.2% PPV 54.8% 48.4% 61.2% NPV 71.1% 64.8% 77.5% 

Area under ROC: 0.65 

CommNone QualitFor coUnbia(prospsubjecLarge AdequcohortUse otest: + Use oascertAdequCompAnalysand reGradein man
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Evidence Table 3 – Question 4 (continued) Study Study Design Patients Outcome Measures Results ComGemer, Segal, and Kopmar, 2001 #4430 

Geographical location: Ashkelon, Israel Study dates:NR Study type:

Other (retrospective case series) Size of population:40 stage III patients Genomic test(s) used: CA-125 Reference standard: Pathology Test reliability established?:

Not stated Statistical tests used:

ANOVA, t-test, chisquare Definition of positive and negative on screening test:CA-125 > 500 U/mL CA-125 > 1500 U/mL 

Age:Median: 65 Range: 42-78 Menopausal status (n [%]):NR Race/ethnicity (n [%]):

Israeli Risk factors (n [%]):NR Diagnoses (n [%]): Ovarian cancer: 40 (100%)Treatment (n [%]): Surgery: 40 Inclusion criteria:Stage III Exclusion criteria:None stated 

Use of test results: Not used for management Outcomes measured: Optimal cytoreduction, defined by the Gynecology Oncology Group as the diameter of the largest residual nodule measure < 1cm. 

1) CA-125 < 500 U/mL to predict optimal cytoreduction: 

 Dis+ Dis- Tot T+ 20 6 26 

T- 4 10 14 Tot 24 16 40  Lower Upper  Value 95% CI 95% CI Se 83.3% 68.4% 98.2% Sp 62.5% 38.8% 86.2% PPV 76.9% 60.7% 93.1% NPV 71.4% 47.8% 95.1% 

2) CA-125 < 1500 U/mL to predict optimal cytoreduction: 

 Dis+ Dis- Tot T+ 24 8 32 T- 0 8 8 Tot 24 16 40  Lower Upper  Value 95% CI 95% CI Se 100.0% 87.5% 100.0% Sp 50.0% 25.5% 74.5% PPV 75.0% 60.0% 90.0% NPV 100.0% 62.5% 100.0% 

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Evidence Table 3 – Question 4 (continued) Study Study Design Patients Outcome Measures Results ComGronlund, Hansen, Hogdall, et al., 2004 #11660 

Geographical location: Copenhagen, Denmark Study dates:Aug 1994-Jan 2001 Study type:

Case series Size of population:124 Genomic test(s) used: CA-125 change, using 2 criteria: Reference standard: Clinical outcome using US or CT for ascertainment of tumor volume Test reliability established?:

Yes Statistical tests used:

Sensitivity, specificity, Fisher’s exact Definition of positive and negative on screening test:1) GCIG: 2 pretreatment samples at least 70 u/mL, with at least 2 additional samples after start of treatment; Response=≥ 50% decrease by 4th sample 

2) CA-125 ratio: Pretreatment level at least 70 u/mL; ratio of 

Age:Median: 59.4 Range: 34.8-77.2 Menopausal status (n [%]):NR Race/ethnicity (n [%]):NR Risk factors (n [%]):NR Diagnoses (n [%]): Ovarian cancer: 124 (100%, all recurrent) Treatment (n [%]): Surgery: 124 Chemotherapy: 124  Platinum: 60  Taxol: 60  Topotecan: 64 Inclusion criteria:Recurrent disease after primary surgery/chemotherapy Exclusion criteria:NR 

Use of test results: 

Prediction of response to second-line chemotherapy with topotecan (n = 64) or platinum/paclitaxel (n = 60) Outcomes measured: Tumor volume 

1) GCIG criteria (at least 50% decrease = response), complete and partial response vs. no change or progression (sensitivity for detecting response); only 72 subjects evaluable: 

 Out+ Out- Tot T+ 27 14 41 T- 1 30 31 Tot 28 44 72  Lower Upper  Value 95% CI 95% CI Se 96.4% 89.6% 100.0% Sp 68.2% 54.4% 81.9% PPV 65.9% 51.3% 80.4% NPV 96.8% 90.6% 100.0% 

2) Hazard Ratio or other relevant information: Unable to do 2x2 table for ratio; performance was best when measured after 3rd cycle of chemotherapy (n = 73); reported 

sensitivity 91% (95% CI, specificity 61% (95% CI 43 to 76%). 

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Evidence Table 3 – Question 4 (continued) Study Study Design Patients Outcome Measures Results Com

posttreatment to pretreatment value ≤ 0.5 

Heinrich, BottcherLuiz, Andrade, et al., 2004 #780 

Geographical location: Campinas, Sao Paulo, Brazil, and Denver, CO Study dates: NR Study type: Cohort (retrospective) Size of population:

74 women Genomic test(s) used: HER-2 CA-125 Reference standard: Surgical pathology Clinical outcome (tumor progression) Test reliability established?:

NR Statistical tests used:

None Definition of positive and negative on screening test:Yes 

Age: NR Menopausal status (n [%]): NR

Race/ethnicity (n [%]):NR Risk factors (n [%]):NR Diagnoses (n [%]): Borderline: 45 (100%) Treatment (n [%]): Surgery: 45 (100%) Inclusion criteria:Patients with borderline ovarian tumors and samples available in tissue bank Exclusion criteria:“Inadequate follow-up information” 

Use of test results: Potential for herceptin therapy Staging Outcomes measured: Stage Tumor progression 

1) CA-125 expression (mod-strong homogeneous cytoplasmic staining) for staging of borderline Ov tumors (FIGO stage II or III vs stage I): 

 Dis+ Dis- Tot T+ 10 12 22 

T- 6 17 23 Tot 16 29 45  Lower Upper  Value 95% CI 95% CI Se 62.5% 38.8% 86.2% Sp 58.6% 40.7% 76.5% PPV 45.5% 24.6% 66.3% NPV 73.9% 56.0% 91.9% 

2) None of the 45 tumors showed HER-2 overexpression. 

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Evidence Table 3 – Question 4 (continued) Study Study Design Patients Outcome Measures Results ComHogdall, Hogdall, Hording, et al., 1996 #7730 

Geographical location: 

Copenhagen, Denmark Study dates: Sep 94 – Jun 87 Study type: Cohort Size of population:63 second-look; 5 thirdlook in 65 patients Genomic test(s) used: Tetranectin (TN) CA-125 CASA Reference standard: Surgical pathology Test reliability established?:Tetranectin - yesStatistical tests used:

Sensitivity, specificity Definition of positive and negative on screening test:TN ≤ 9.3 mg/l CASA ≥ 10 U/ml 

CA-125 ≥ 35 U/ml 

Age: NR Menopausal status (n [%]): NR

Race/ethnicity (n [%]):NR Risk factors (n [%]):NR Diagnoses (n [%]): Ovarian cancer: 100% Treatment (n [%]): Chemotherapy:  Platinum: 50%  Other (Cyclo-  phosphamide,  Adriamycin and 5-FU  [CAF]): 50% Inclusion criteria:Participants in a RCT comparing chemo regimens after primary surgery for ovarian cancer Exclusion criteria:NR 

Use of test results: Prediction of residual tumor Outcomes measured: Presence of residual tumor after adjuvant chemotherapy at second-look laparotomy (SLL) 

1) TN ≤ 9.3 mg/l to predict presence of residual tumor at SLL after primary resection and chemotherapy for ovarian cancer: 

 Dis+ Dis- Tot T+ 9 0 9 

T- 29 30 59 Tot 38 30 68  Lower Upper  Value 95% CI 95% CI Se 23.7% 10.2% 37.2% Sp 100.0% 90.0% 100.0% PPV 100.0% 66.7% 100.0% NPV 50.8% 38.1% 63.6% 

2) CASA ≥ 10 U/ml to predict presence of 

residual tumor at SLL after primary resection and chemotherapy for ovarian cancer: 

 Dis+ Dis- Tot T+ 12 0 12 T- 26 29 55 Tot 38 29 67  Lower Upper  Value 95% CI 95% CI Se 31.6% 16.8% 46.4% Sp 100.0% 89.7% 100.0% PPV 100.0% 75.0% 100.0% NPV 52.7% 39.5% 65.9% 

Additional tables reported for CA-125 ≥ 10 U/ml 

CA-125 ≥ 35 U/ml 

CASA ≥ 10 or CA-125 ≥ 35 CASA ≥ 10 or CA-125 ≥ 10 

CASA ≥ 10 or CA-125 ≥ 35 or TN ≤ 9.3 

CASA ≥ 10 or CA-125 ≥ 10 or TN ≤ 9.3 

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Evidence Table 3 – Question 4 (continued) Study Study Design Patients Outcome Measures Results ComIzquierdo, van der Zee, Vermorken, et al., 1995 #8010 

Geographical location: Amsterdam and Groningen, The Netherlands Study dates: 1984-1993 Study type: Cohort/retrospective case series Size of population:57 women/tumors Genomic test(s) used: Markers Lrp, Mrp, and Pgp Reference standard: Surgical pathology Test reliability established?:Yes Statistical tests used:

Sensitivity, specificity, survival analysis Definition of positive and negative on screening test:Yes, more than 10% of cells stained; kappa test of reliability 0.553 -0.854 (blind interpretation)

Age:Mean (SD): 66 Range: 29-84 Menopausal status (n [%]): NR

Race/ethnicity (n [%]):NR Risk factors (n [%]):NR Diagnoses (n [%]): Ovarian cancer: 57 (100%)Treatment (n [%]): Surgery: 57 (100%) Chemotherapy:  Platinum: 50 (88%) Inclusion criteria:Banked frozen specimens from women who underwent initial surgery for stage II or IV Ov Ca Exclusion criteria:None specified 

Use of test results: Prediction of response to chemotherapy Outcomes measured: Cancer mortality Response by second-look surgery (SLL) or clinical and/or radiographic evaluation (WHO criteria) 

1) Pgp expression negative to predict response (partial or none vs complete) to induction chemotherapy: 

 Dis+ Dis- Tot T+ 32 8 40 

T- 9 0 9 Tot 41 8 49  Lower Upper  Value 95% CI 95% CI Se 78.0% 65.4% 90.7% Sp 0.0% 0.0% 0.0% PPV 80.0% 67.6% 92.4% NPV 0.0% 0.0% 0.0% 

2) Mrp expression negative to predict response (partial or none vs complete) to induction chemotherapy: 

 Dis+ Dis- Tot T+ 13 3 16 T- 28 5 33 Tot 41 8 49  Lower Upper  Value 95% CI 95% CI Se 31.7% 17.5% 46.0% Sp 62.5% 29.0% 96.0% PPV 81.3% 62.1% 100.0% NPV 15.2% 2.9% 27.4% 

3) Lrp expression negative to predict response (partial or none vs complete) to induction chemotherapy 

 Dis+ Dis- Tot T+ 5 5 10 T- 36 3 39 Tot 41 8 49  Lower Upper 

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Evidence Table 3 – Question 4 (continued) Study Study Design Patients Outcome Measures Results Com

 Value 95% CI 95% CI Se 12.2% 2.2% 22.2% Sp 37.5% 4.0% 71.0% PPV 50.0% 19.0% 81.0% NPV 7.7% 0.0% 16.1% 

4) Hazard Ratio or other relevant information: No association between Pgp or Mrp and either progression-free survival or overall survival in univariate survival analysis. Lrp-positive tumors had shorter progressionfree (9 mo vs 28 mo; p = 0.003) and overall survival (median 15 mo vs 42 mo; p = 0.007) than Lrp-negative tumors. Lrp remained a significant predictor (p = 0.009) of survival in a multivariable survival analysis controlling for FIGO stage, residual tumor after initial surgery, tumor grade, and presence of ascites. 
A-149

Evidence Table 3 – Question 4 (continued) Study Study Design Patients Outcome Measures Results ComKamazawa, Kigawa, Kanamori, et al., 2002 #3550 

Geographical location: Yonago, Japan Study dates: 2000-2001 Study type: Cohort/case series Size of population:

27 women Genomic test(s) used: MDR-1 MRP-1 MRP-2 by RT-PCR Reference standard: Surgical pathology Clinical outcome (response, CR, PR, NC) Test reliability established?:

No Statistical tests used:

t-test, sensitivity, specificity Definition of positive and negative on screening test:MDR-1 gene expression of 100 

Age:Mean (SD): 55.2 Range: 21-72 Menopausal status (n [%]):NRRace/ethnicity (n [%]):

NR Risk factors (n [%]):NR Diagnoses (n [%]): Ovarian cancer: 27 (100%)Treatment (n [%]): Surgery: 27 (100%) Chemotherapy:  Platinum: 27 (100%) Inclusion criteria:Residual disease after primary surgery Exclusion criteria:Borderline malignancy 

Use of test results: Predictor of response to paclitaxel-based chemotherapy Outcomes measured: Complete or partial response to chemotherapy as measured by CT/MR/US 

1) MDR-1 expression < 100 to predict response to chemotherapy: 

 Dis+ Dis- Tot T+ 21 1 22 

T- 0 5 5 Tot 21 6 27  Lower Upper  Value 95% CI 95% CI Se 100.0% 85.7% 100.0% Sp 80.0% 48.0% 100.0% PPV 95.5% 86.8% 100.0% NPV 100.0% 40.0% 100.0% 

Tests of MRP-1 and MRP-2 did not differ between responders and non-responders (Figure; 2x2 not provided) 

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Evidence Table 3 – Question 4 (continued) Study Study Design Patients Outcome Measures Results ComKierkegaard, Mogensen, Mogensen, et al., 1995 #7940 

Geographical location: Aarhus, Denmark Study dates: Sep 1987 – Dec 1992 Study type:

Cohort Size of population:93 women from 265 consecutive patients Genomic test(s) used: CASA CA-125 Reference standard: Surgical pathology Test reliability established?:No Statistical tests used:

Sensitivity, specificity, Cox Definition of positive and negative on screening test:Yes 

Age: Median: 54.6 Range: 27-70 Menopausal status (n [%]):NRRace/ethnicity (n [%]):

NR Risk factors (n [%]):NR Diagnoses (n [%]): Ovarian cancer: 93 (100%)Treatment (n [%]): Surgery: 93 (100%) Chemotherapy:  Platinum: 93 (100%) Inclusion criteria:Epithelial ovarian cancer FIGO stage II, III or IV, with no residual tumor at primary surgery Exclusion criteria:NR 

Use of test results: Prediction of positive second-look laparotomy Outcomes measured: Presence of residual disease at second-look 

1) CASA > 8 U/ml for diagnosis of tumor at SLL: 

 Dis+ Dis- Tot T+ 13 0 13 

T- 45 35 80 Tot 58 35 93  Lower Upper  Value 95% CI 95% CI Se 22.4% 11.7% 33.1% Sp 100.0% 91.4% 100.0% PPV 100.0% 76.9% 100.0% NPV 43.8% 32.9% 54.6% 

2) CA-125 > 15 U/ml for diagnosis of tumor at SLL: 

 Dis+ Dis- Tot T+ 23 0 23 T- 35 35 70 Tot 58 35 93  Lower Upper  Value 95% CI 95% CI Se 39.7% 27.1% 52.2% Sp 100.0% 91.4% 100.0% PPV 100.0% 87.0% 100.0% NPV 50.0% 38.3% 61.7% 

3) CASA > 8 U/ml OR CA-125 > 15 U/ml for diagnosis of tumor at SLL: 

 Dis+ Dis- Tot T+ 27 0 27 T- 31 35 66 Tot 58 35 93  Lower Upper  Value 95% CI 95% CI Se 46.6% 33.7% 59.4% Sp 100.0% 91.4% 100.0% 

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Evidence Table 3 – Question 4 (continued) Study Study Design Patients Outcome Measures Results Com

PPV 100.0% 88.9% 100.0% NPV 53.0% 41.0% 65.1% 

4) Hazard Ratio or other relevant information: In multivariable model for survival (controlling for age, histopathology, FIGO stage and grade): RR CA-125 > 35 U/ml = 2.9 (2.1 to 3.7; p = 0.007) RR for CASA > 8 U.ml = 2.2 (1.5 to 3.0; p = 0.043) 
A-152

Evidence Table 3 – Question 4 (continued) Study Study Design Patients Outcome Measures Results ComKupryjanczyk, Szymanska, Madry, et al., 2003 #2830 

Geographical location: Krakow, Poland Study dates: NR Study type: Cohort/retrospective series Size of population:229 patients from 548 cases submitted Genomic test(s) used: TP53 Reference standard: Clinical outcome (tumor response by WHO clinical criteria) Test reliability established?:No Statistical tests used:

2x2, CPH model Definition of positive and negative on screening test:Yes 

Age: Median: 53.2 Range: 24-77 Menopausal status (n [%]):NRRace/ethnicity (n [%]):

NR Risk factors (n [%]):NR Diagnoses (n [%]): Ovarian cancer: 229 (100%) Treatment (n [%]): Surgery: 229 (100%) Chemotherapy (platinum): 229 (100%) Inclusion criteria:Ov Ca FIGO IIB-IV, platinum chemotherapy and available tumor tissue in bank Exclusion criteria:Chemo before staging laparotomy 

Use of test results: Prediction of tumor response to platinum-based chemotherapy Outcomes measured: Cancer mortality Tumor response (WHO criteria) 

1) TP53 expression positive to predict response to chemotherapy (CR or PR vs. NC or PD): 

 Dis+ Dis- Tot T+ 98 37 135 

T- 57 37 94 Tot 155 74 229  Lower Upper  Value 95% CI 95% CI Se 63.2% 55.6% 70.8% Sp 50.0% 38.6% 61.4% PPV 72.6% 65.1% 80.1% NPV 39.4% 29.5% 49.2% 

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Evidence Table 3 – Question 4 (continued) Study Study Design Patients Outcome Measures Results ComLassus, Leminen, Vayrynen, et 

al., 2004 #1360 

Geographical location: Helsinki, Finland Study dates: 1980-2000 Study type: Cohort Size of population:401 women Genomic test(s) used: ERBB2 Tissue protein microarray Reference standard: Surgical pathology CT/MR Death Test reliability established?:No Statistical tests used:

Survival analysis Definition of positive and negative on screening test:No 

Age:< 57 years: 38% ≥ 57 years: 62% Menopausal status (n [%]):NRRace/ethnicity (n [%]):98% white/Finnish

Risk factors (n [%]):NR Diagnoses (n [%]): Ovarian cancer: 401 (100%) Treatment (n [%]): Surgery: 401 (100%) Chemotherapy:  Platinum: 345 (96%)  Taxol:  Other (not specified):  13 (3%) No chemotherapy: 42 (10%) Inclusion criteria:Ov Ca treated at Helsinki Univ, with data for both primary treatment and survival status. Exclusion criteria:Non-serous tumors 

Use of test results: Prediction of response Outcomes measured: Cancer mortality Response to chemotherapy 

1) ERBB2 gene copy number = 2 (vs. 3-5 or > 5) to predict response to chemotherapy (CR or PR vs. NR or PD): 

 Dis+ Dis- Tot T+ 234 30 264 

T- 66 51 117 Tot 300 81 381  Lower Upper  Value 95% CI 95% CI Se 78.0% 73.3% 82.7% Sp 63.0% 52.5% 73.5% PPV 88.6% 84.8% 92.5% NPV 43.6% 34.6% 52.6% 

2) Hazard Ratio or other relevant information: In multivariable survival analysis ERBB2 copy number status was an independent prognostic factor (HR 2.14 [1.34 to 3.42] for > 5 gene copies, and HR 1.70 [1.17 to 2.46] for 3-5 gene copies compared to 2 copies). Other independent prognostic factors included: Grade (1, 2 or 3) Residual tumor > 1cm Age ≥ 57 years FIGO stage (I, II, II IV) 

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Evidence Table 3 – Question 4 (continued) Study Study Design Patients Outcome Measures Results ComLuo, Katsaros, Scorilas, et al., 2003 #2930 

Geographical location: 

Turin, Italy; Groningen, The Netherlands; Leuven, Belgium; Helsinki, Finland Study dates: NR Study type: Cohort/case series Size of population:146 Ov Ca Genomic test(s) used: hK10 Reference standard: Surgical pathology Test reliability established?:

Referenced Statistical tests used:

Survival analysis Definition of positive and negative on screening test:No 

Age:Ovarian Ca Mean (SD): 52 Median: 49 Range: 26-72 Menopausal status (n [%]):NRRace/ethnicity (n [%]):

NR Risk factors (n [%]):NR Diagnoses (n [%]): Ovarian cancer: 146 (100%) Benign ovarian mass: 141 Healthy controls: 97 Treatment (n [%]): Surgery: 146 (100%) Chemotherapy: NR Inclusion criteria:Not described Exclusion criteria:NR 

Use of test results: Prediction of response to chemotherapy Outcomes measured: Cancer mortality Tumor response to chemotherapy 

1) hK10 < 843 ng/L to predict response to chemotherapy (CR/PR vs NC/PD): 

 Dis+ Dis- Tot T+ 74 7 81 

T- 44 14 58 Tot 118 21 139  Lower Upper  Value 95% CI 95% CI Se 62.7% 54.0% 71.4% Sp 66.7% 46.5% 86.8% PPV 91.4% 85.2% 97.5% NPV 24.1% 13.1% 35.2% 

2) Hazard Ratio or other relevant information: hK10 positive (> 843 ng/L) was NOT associated with PFS, HR = 1.31 (0.65 to 2.62; p = 0.45). hK10 positive was associated with OS, HR = 3.43 (1.23 to 5.54; p = 0.018). 

CommNone QualitFor coUnbia(prospsubjecLarge AdequcohortUse otest: + Use oascertAdequCompAnalysand reGrade
A-155

Evidence Table 3 – Question 4 (continued) Study Study Design Patients Outcome Measures Results ComMemarzadeh, Lee, Berek, et al., 2003 #2790 

Geographical location: Los Angeles, CA Study dates: 1989-2001 Study type: Case-control Cases = optimally cytoreduced Controls = suboptimally cytoreduced Size of population:

99 Genomic test(s) used: CA-125 Reference standard: Clinical outcome: optimal versus suboptimal cytoreduction. Optimal is defined as all residual tumor nodules less than 1 cm. Test reliability established?:YesStatistical tests used:

Sensitivity, specificity, ROC Definition of positive and negative on screening test:Cutoff CA-125 912 U/ml determined using ROC

Age:Median: 59 Range: 23-83 Menopausal status (n [%]):NRRace/ethnicity (n [%]):NRRisk factors (n [%]):NRDiagnoses (n [%]): Ovarian cancer: 99 (100%)Treatment (n [%]): Surgery: 99 (100%) Inclusion criteria:Stage IIIC-IV ovarian cancer who had primary cytoreductive surgery Exclusion criteria:Borderline tumors 

Use of test results: Predicting optimal cytoreduction Outcomes measured: Ability to perform optimal cytoreductive surgery 

1) CA-125 > 912 U/mL to predict suboptimal cytoreduction Out+ = suboptimal Out- = Optimal T+ = above cutoff T- = below cutoff 

 Dis+ Dis- Tot T+ 14 31 45 

T- 12 42 54 Tot 26 73 99  Lower Upper  Value 95% CI 95% CI Se 53.8% 34.7% 73.0% Sp 57.5% 46.2% 68.9% PPV 31.1% 17.6% 44.6% NPV 77.8% 66.7% 88.9% 

Comm- Retroany pagiven)- No vcutoff - No dpredicthan CvariabQualitFor caValid aUnbiaAppropopulaVerificcanceCompwith reconfouValidamethoApproanalysGrade
A-156

Evidence Table 3 – Question 4 (continued) Study Study Design Patients Outcome Measures Results ComObeidat, Latimer, and Crawford, 2004 #1800 

Geographical location: 

Irbid, Jordan Study dates: 1/00-12/01 Study type: Case-control Size of population:40 Genomic test(s) used: CA-125 Reference standard: Clinical outcome: optimal surgical cytoreduction, GOG criteria, largest remaining tumor nodule ≤1 cm Test reliability established?:YesStatistical tests used:

Sensitivity, specificity, ROC curves Definition of positive and negative on screening test:CA-125 > 500 U/mL 

Age:Median:  Optimal debulking: 57  Suboptimal: 63.5 Range:  Optimal: 30-79  Suboptimal: 49-78 Menopausal status (n [%]):NRRace/ethnicity (n [%]):NR Risk factors (n [%]):NRDiagnoses (n [%]): Ovarian cancer: 40 (100%)Treatment (n [%]): Surgery: 40 (100%) Inclusion criteria:

Stage III ovarian cancer Exclusion criteria:Borderline malignancy 

Use of test results: Predicting optimal cytoreduction Outcomes measured: Optimal surgical cytoreduction 

1) CA-125 ≥ 500U/ml to predict sub-optimal 

cytoreduction: Out+ = suboptimal cytoreduction Out- = optimal cytoreduction T+ = CA-125 > 500 T- = CA-125 < 500 

 Out+ Out- Tot T+ 13 6 19 

T- 5 16 21 Tot 18 22 40  Lower Upper  Value 

95% 

CI 

95% 

CI 

Se 72.0% 51.3% 92.7% 

Sp 73.0% 54.4% 91.6% 

PPV 68.4% 47.5% 89.3% NPV 76.2% 58.0% 94.4% 

Comm- Not areportQualitFor caValid aUnbiaAppropopulaVerificcanceCompwith reconfouValidamethoApproanalysGrade
A-157

Evidence Table 3 – Question 4 (continued) Study Study Design Patients Outcome Measures Results ComRustin, Marples, Nelstrop, et al., 2001 #4110 

Geographical location: Northwood, UK Study dates: 1981-1999 Study type: Case-control Size of population:

300 Æ 88 who had persistent elevation of CA-125 following primary chemotherapy were the basis for this paper’s analysis Genomic test(s) used: CA-125 Reference standard: Clinical outcome: radiographic evidence of disease progression or clinical evidence of progression Test reliability established?:YesStatistical tests used:

Sensitivity Definition of positive and negative on screening test:Elevation of CA-125 to twice the nadir level 

Age: NR Menopausal status (n [%]):NRRace/ethnicity (n [%]):NRRisk factors (n [%]):NRDiagnoses (n [%]): Ovarian cancer: 300 (100%) Treatment (n [%]): Chemotherapy (type not specified): 300 (100%) Inclusion criteria:

Treatment with first line chemotherapy for ovarian cancer; at least one CA-125 available; CA-125 elevated above normal range persistently post-treatment Exclusion criteria:Normal CA-125 at conclusion of primary chemotherapy 

Use of test results: Change/delay in treatment Outcomes measured: Cancer progression based on clinical criteria 

1) Ability of CA-125 doubling to diagnose ovarian cancer progression as measured by clinical signs or radiography: Out+ = patients whose disease progressed Out- = patients whose disease did not progress T+ = CA-125 doubled T- = CA-125 did not double 

 Out+ Out- Tot T+ 80 1 81 

T- 5 2 7 Tot 85 3 88  Lower Upper  Value 95% CI 95% CI Se 94.1% 89.1% 99.1% Sp 66.7% 13.3% 100.0%PPV 98.8% 96.4% 100.0%NPV 28.6% 0.0% 62.0% 

Comm- No swere uRECISsize) - Few who dcalculQualitFor caValid aUnbiaAppropopulaVerificcancespecifprogreCompwith reconfouValidamethoApproanalysGrade
A-158

Evidence Table 3 – Question 4 (continued) Study Study Design Patients Outcome Measures Results ComRustin, Nelstrop, Tuxen, et al., 1996 #7510 

Geographical location: Northwood, UK Study dates:12/89-4/94 Study type:

Case-control Size of population:255 Genomic test(s) used: CA-125 Reference standard: Clinical outcome: clinical evidence of ovarian cancer progression with follow up at least 12 months Test reliability established?:YesStatistical tests used:

Sensitivity, specificity, PPV, NPV Definition of positive and negative on screening test:Upper limit of normal CA125 is 30 U/ml. Definition of progression is a doubling of the CA15 from a baseline level ≤ 30. 

Age: NR Menopausal status (n [%]):NRRace/ethnicity (n [%]): NR Risk factors (n [%]):NRDiagnoses (n [%]): Ovarian cancer: 255 (100%) Treatment (n [%]): Chemotherapy (carboplatin or cisplatin): 100% Inclusion criteria:

Patients with ovarian cancer enrolled on a large trial of 5 versus 8 cycles of carboplatin or cisplatin. Exclusion criteria:- Very few (< 4) CA-125 samples available; - Patients who received treatment with a monoclonal antibody; - Patients with secondary malignancy; follow up less than 12 months, persistent CA-125 elevation following primary treatment 

Use of test results: Treat relapse disease early Outcomes measured: Cancer progression 

1) Ability of CA-125 to detect progression Out+ = progression of cancer Out- = no progression T+ = CA-125 double the normal cutoff (30u/ml) T- = CA-125 never doubled the normal cutoff 

 Out+ Out- Tot T+ 73 4 77 

T- 12 42 54 Tot 85 46 131  Lower Upper  Value 95% CI 95% CI Se 85.9% 78.5% 93.3% Sp 91.3% 83.2% 99.4% PPV 94.8% 89.8% 99.8% NPV 77.8% 66.7% 88.9% 

2) Ability of CA-125 to predict early progression: Out+ = progression of cancer Out- = no progression T+ = 2 consecutive CA-125 more than double the normal cutoff (30u/ml) T- = did not meet T+ criteria above 

 Out+ Out- Tot T+ 73 1 74 T- 14 42 56 Tot 87 43 130  Lower Upper  Value 95% CI 95% CI Se 83.9% 76.2% 91.6% Sp 97.7% 93.2% 100.0%PPV 98.6% 96.0% 100.0%NPV 75.0% 63.7% 86.3% 

Comm- Seve125 crwere echose validavalue. QualitFor caValid aUnbiaAppropopulaVerificcancemonthCompwith reconfouValidamethoApproanalysGrade
A-159

Evidence Table 3 – Question 4 (continued) Study Study Design Patients Outcome Measures Results ComSantillan, Garg, Zahurak, et al., 2005 #13220 

Geographical location: 

Baltimore, MD Study dates: Sep 1997-Mar 2003 Study type: Cohort Size of population:39 Genomic test(s) used: CA-125 Reference standard: Clinical outcome: Recurrent disease (pathology or radiologic evidence of recurrence) Test reliability established?:YesStatistical tests used:

Fisher’s exact test, t-test Definition of positive and negative on screening test:Absolute change in CA125 post-treatment ≥ 5 

Age:Mean: 55 Range: 43-71 Menopausal status (n [%]):NRRace/ethnicity (n [%]):NRRisk factors (n [%]):NRDiagnoses (n [%]): Ovarian cancer: 39 (100%) Treatment (n [%]): NR Inclusion criteria:- CA-125 > 35 at diagnosis - Complete response to initial therapy with CA-125 < 35 - At least 3 serial serum CA-125 measurements - Clinical or radiologic determination of disease status at last visit Exclusion criteria:None specified 

Use of test results: Prediction of recurrence Outcomes measured: Cancer recurrence 

1) Recurrence; positive test = absolute change in CA-125 ≥ 5 

 Dis+ Dis- Tot T+ 22 1 23 

T- 0 16 16 Tot 22 17 39  Lower Upper  Value 95% CI 95% CI Se 100.0% 86.4% 100.0%Sp 94.1% 82.9% 100.0%PPV 95.7% 87.3% 100.0%NPV 100.0% 81.3% 100.0%

CommNone QualitFor coUnbia(prospsubjecLarge AdequcohortUse otest: + Use oascertAdequCompAnalysand reGrade
A-160

Evidence Table 3 – Question 4 (continued) Study Study Design Patients Outcome Measures Results ComSaygili, Guclu, Uslu, et al., 2002 #3680 

Geographical location: 

Izmir, Turkey Study dates: 1994-2001 Study type: Case-control Size of population:92 Genomic test(s) used: CA-125 Reference standard: Surgical pathology Clinical outcome: Surgical cytoreduction of all tumor nodules to less than 1 cm (optimal cytoreduction) Test reliability established?:YesStatistical tests used:

Sensitivity, specificity, ROC Definition of positive and negative on screening test:CA-125 cutoff 500 U/ml established using ROC curve 

Age:Mean (SD): 56 Range: 46-64 Menopausal status (n [%]):NRRace/ethnicity (n [%]):NRRisk factors (n [%]):NRDiagnoses (n [%]): Ovarian cancer: 92 (100%) Treatment (n [%]): Surgery: 92 (100%) Chemotherapy: NR Inclusion criteria:

Stage IIIC ovarian cancer, patient undergoing primary surgery Exclusion criteria:Pre-operative chemotherapy 

Use of test results: Determine whether patients will be resectable Outcomes measured: Ability to optimally cytoreduce ovarian cancer 

1) CA-125 > 500 U/mL to predict suboptimal surgical cytoreduction: Out+ = suboptimal Out- = optimal T+ = CA-125>500 T- = CA-125<500 

 Dis+ Dis- Tot T+ 33 12 45 

T- 11 36 47 Tot 44 48 92  Lower Upper  Value 95% CI 95% CI Se 75.0% 62.2% 87.8% Sp 75.0% 62.8% 87.3% PPV 73.3% 60.4% 86.3% NPV 76.6% 64.5% 88.7% 

Comm- No cvariabdebulkQualitFor caValid aUnbia(uncleAppropopulaVerificcanceCompwith reconfouValidamethoApproanalysGrade
A-161

Evidence Table 3 – Question 4 (continued) Study Study Design Patients Outcome Measures Results ComSenapad, Neungton, Thirapakawong, et al., 2000 #5170 

Geographical location: Bangkok, Thailand Study dates: 5/95-12/98 Study type: Cohort Size of population:

33 Genomic test(s) used: CA-125, TPS Reference standard: ]Surgical pathology at second-look laparotomy Test reliability established?:CA-125 – yes TPS – no

Statistical tests used:

Sensitivity, specificity, PPV, NPV Definition of positive and negative on screening test:For prediction of negative second-look laparotomy: CA-125 < 10 u/ml TPS < 50 U/ml 

Age:Median: 45 Range: 27-72 Menopausal status (n [%]):NRRace/ethnicity (n [%]):NRRisk factors (n [%]):NRDiagnoses (n [%]): Ovarian cancer: 33 (100%)Treatment (n [%]): Chemotherapy (cisplatinum, paraplatin, or the paclitaxel combination regime): 33 (100%)Inclusion criteria:

- Non-mucinous epithelial ovarian cancer, stage III-IV - All achieved a complete response with primary chemotherapy (no physical exam or radiographic evidence of disease) - All underwent second-look laparotomy following completion of 6 cycles of chemotherapyExclusion criteria:- Non complete responders - Patients who did not receive second-look laparotomy 

Use of test results:Potential use of authors’ criteria to predict negative second-look laparotomy (and therefore avoid surgery)Outcomes measured: Pathology results at secondlook laparotomy 

1) CA-125 > 10 U/mL to predict positive second-look laparotomy 

Out+ = positive second-look Out- = negative second-look T+ = CA-125 > 10 T- = CA-125 < 10 

 Dis+ Dis- Tot T+ 11 11 22 

T- 8 3 11 Tot 19 14 33  Lower Upper  Value 95% CI 95% CI Se 57.9% 35.7% 80.1% Sp 21.4% 0.0% 42.9% PPV 50.0% 29.1% 70.9% NPV 27.3% 1.0% 53.6% 

2) Combination of CA-125 > 10 and TPS>50 to predict positive SLL Out+ = positive SLL Out- = negative SLL T+ = CA-125 > 10 and TPS > 50 T- = doesn’t meet T+ criteria above 

 Dis+ Dis- Tot T+ 11 13 24 T- 8 1 9 Tot 19 14 33  Lower Upper  Value 95% CI 95% CI Se 57.9% 35.7% 80.1% Sp 7.1% 0.0% 20.6% PPV 45.8% 25.9% 65.8% NPV 11.1% 0.0% 31.6% 

Comm- Authpatienand Tdue to with a - Secolonger StatesQualitFor caValid aUnbiaspecifAppropopulaVerificcanceCompwith reconfouValidamethoApproanalysGrade
A-162

Evidence Table 3 – Question 4 (continued) Study Study Design Patients Outcome Measures Results ComWong, Dai, Lele, et al., 2000 #5400 

Geographical location: Buffalo, NY Study dates: 1/90-12/96 Study type:

Case-control Size of population:72 Genomic test(s) used: CA-125 Reference standard: Surgical pathology Test reliability established?:YesStatistical tests used:

Sensitivity, specificity, NPV Definition of positive and negative on screening test:CA-125 < 35U/ml normal 

Age:Mean: 58.9 Menopausal status (n [%]):NRRace/ethnicity (n [%]):NR Risk factors (n [%]):NR Diagnoses (n [%]): Ovarian cancer: 72 (100%)Treatment (n [%]): Primary surgery: 72 (100%) Second-look surgery: 46 (64%) Chemotherapy (platinumbased): 70 (97%) - Cisplatin/cytoxan: 43 (60%) - Cisplatin/taxol: 15 (21%) - Cisplatin/Adria/Cytoxan: 12 (17%) - Methotrexate/Cytoxan: 2 (3%) Inclusion criteria:

Patients with epithelial ovarian cancer who underwent initial optimal surgical cytoreduction followed by 6 cycles of platinum-based chemotherapy Exclusion criteria:Non-epithelial ovarian cancer 

Use of test results: No use in the current study Outcomes measured: Pathology results at secondlook laparotomy/laparoscopy, if performed Overall survival 

1) Ability of CA-125 performed after 3 cycles chemotherapy to predict positive secondlook laparotomy performed after 6 cycles chemotherapy: Out+ = positive pathology at second-look Out- = negative pathology at second-look T+ = CA-125 > 35U/ml T- = CA-125 < 35U/ml 

 Out+ Out- Tot T+ 5 1 6 

T- 23 17 40 Tot 28 18 46  Lower Upper  Value 95% CI 95% CI Se 17.9% 3.7% 32.1% Sp 94.7% 84.4% 100.0%PPV 83.3% 53.5% 100.0%NPV 42.5% 27.2% 57.8% 

2) Other relevant information: CA-125 in normal range after 3 cycles chemotherapy predicted better median survival (30 months vs. 17 months, P < 0.0001 log rank test). 

Comm- Studnormacycles predicseconpredicQualitFor caValid aUnbiaAppropopulaVerificcanceCompwith reconfouValidamethoApproanalysGrade
A-163

Evidence Table 4 – Question 5: What are the harms of using genomic tests for ovarian cancer prevention and managemStudy Study Design Patients Outcome Measures Results CommBish, Sutton, Jacobs, et al., 2002 #10530 

Geographical location: London, UK Study dates: May 1997-May 1999 Study type:

Cohort Size of population:203 Genomic test(s) used: BRCA1/BRCA2 Reference standard: Clinical outcome (psychological distress) Test reliability established?:

Yes – validated scale for measuring outcome Statistical tests used: ANOVA, t-tests Definition of positive and negative on screening test:Positive BRCA1/BRCA2 

Age:Mean (SD): 42.3 (12.6) Range: 18-73 Menopausal status (n [%]):NR Race/ethnicity (n [%]):

97% White 3% Other Definition of “high risk”:Estimated lifetime cancer risk of 1 in 3 or higher Diagnoses (n [%]): 46 (24%) with previous history of breast and/or ovarian cancer (1 with ovarian cancer, 4 with both, rest with breast cancer) 26 (13.4%) low risk 76 (39.2%) moderate 46 (23.7%) high risk Inclusion criteria:Referral criteria (any of the following): - Breast cancer < 40 years- More than 1 primary - Breast and ovary as 2 primary tumors - 3 close relatives with breast or ovarian cancer - 2 close relatives with breast or ovarian cancer if 1 < 50 - 1 1st degree relative with 

cancer < 40 - 1 1st degree relative with bilateral breast cancer or 2 primaries 

Use of test results: Counseling regarding BRCA1/BRCA2 testing, options for screening and surveillance Outcomes measured: Quality of life - Hospital Anxiety and Depression Scale - General Health Questionnaire - Cancer Worry Scale - Perceived risk of cancer - Perceived risk of mutation Timing of outcome measurement: - Pre-consultation - 2 weeks post-consultation (83% response) - 6 months (85% response) - 12 months (91%) Only those with data at all 4 time points (n = 203, 35% of initial cohort scheduled for genetic consultation during time period) were included in the analysis 

1) At baseline, significantly less worry about ovarian cancer compared to breast cancer (7% vs. 34% reported worry often or almost all the time). 2) Worry about ovarian cancer increased significantly among those with history of breast or ovarian cancer compared to unaffected women, no matter what risk group. 3) Overall no change in worry in response to counseling. 

CommeNone Quality For cohUnbiase(prospesubjectLarge sAdequacohort: Use of test: + Use of ascertaAdequaCompledropout Analysiand repGrade: 
A-164

Evidence Table 4 – Question 5 (continued) Study Study Design Patients Outcome Measures Results Comm

- “Very worried about family history” Exclusion criteria:None specified 

Claes, EversKiebooms, Denayer, et al., 2005 #13010 

Geographical location Leuven, BelgiumStudy dates: 1999-July 2003 Study type:Cohort Size of population:71 (68 [96%] completed follow-up) Genomic test(s) used: BRCA1/BRCA2 Reference standard: 

Clinical outcome (quality of life measured using validated instruments) Test reliability established?:

Yes Statistical tests used: Fisher’s exact test, t-tests Definition of positive and negative on screening test:Positive BRCA1 or BRCA2 

Age:Carriers (n = 34) Mean (SD): 38.4 (11.4) Range: 19-61 Non-carriers (n = 34) Mean (SD): 35.24 (10.6) Range: 19-64 Menopausal status (n [%]):NR Race/ethnicity (n [%]):

NR Risk factors (n [%]):NR Diagnoses (n [%]): NR Inclusion criteria:Family members of patients with identified BRCA1/BRCA2 mutations Exclusion criteria:None specified 

Use of test results: Counseled about risk, prophylactic surgery Outcomes measured: Quality of life - Coping strategies (Utrecht Coping List) - Perceived impact of test result - Risk perception - Sense of control - Cancer-specific distress (Impact of Event Scale) - General distress (Spielberger STAI) 

1) Intrusive thoughts, depressive symptoms, and breast and ovarian cancer worries improved for those with negative result. 2) No change from baseline in those with positive test results or those who refused testing. 3) Problem-solving training results in greater improvement than client-based counseling. 4) Sex not significant factor in multivariate analysis. 

CommeNone Quality For cohUnbiase(prospesubjectLarge sAdequacohort: Use of test: + Use of ascertaAdequaCompleAnalysiand repGrade: 
A-165

Evidence Table 4 – Question 5 (continued) Study Study Design Patients Outcome Measures Results CommMcInerneyLeo, Biesecker, Hadley, et al., 2004 #10100 

Geographical location Bethesda, Rockville, and Baltimore, MD; Philadelphia, PA

Study dates: NR Study type:Cohort Size of population:212 (559 invited, 262 agreed and completed baseline, 212 completed baseline and follow-up) Genomic test(s) used: BRCA1/BRCA2 Reference standard: Clinical outcome (measures of family relationships [conflict, cohesiveness, expressiveness], using Family Relationship Index) Test reliability established?:

Yes Statistical tests used: Paired t-tests, ANOVAs; linear regression Definition of positive and negative on screening test:NA 

Age:Range: 95 (45%) under 40 (not reported by sex) Menopausal status (n [%]):NR; 35% male Race/ethnicity (n [%]):

“Primarily Caucasian” Risk factors (n [%]):Family history: 212 (100%) Diagnoses (n [%]): Ovarian cancer: 0 Borderline: 0 Benign ovarian mass: 0 Other: 0 Healthy controls: 100% Inclusion criteria:Age > 18, family members with at least one BRCA1 or 2 mutation identified Exclusion criteria:None specified 

Use of test results: Not specified; subjects randomized to problemsolving training or clientcentered counseling Outcomes measured: - Depression (Center for Epidemiologic Studies Depression Scale) - Self-esteem (Rosenberg Self-Esteem Scale) - Cancer-related distress (Impact of Events Scale, Breast Cancer Worries Scale, Ovarian Cancer Worries Scale) 

1) Intrusive thoughts, depressive symptoms, and breast and ovarian cancer worries improved for those with negative result. 2) No change from baseline in those with 

positive test results or those who refused testing. 3) Problem-solving training results in greater improvement than client-based counseling. 4) Sex not significant factor in multivariate analysis. 

Comme- Same McInern- Familiin previcommu- Baselthan avaverage - 212 sufamilies study apossibly Quality For cohUnbiase(prospesubjectLarge sAdequacohort: Use of test: + Use of ascertaAdequaCompleAnalysiand repGrade: 
A-166

Evidence Table 4 – Question 5 (continued) Study Study Design Patients Outcome Measures Results CommMcInerneyLeo, Biesecker, Hadley, et al., 2005 #520 

Geographical location: Bethesda, Rockville, and Baltimore, MD; Philadelphia, PA Study dates: NR Study type:

Cohort of adult members of families with identified BRCA1/2 mutations Size of population:212 (559 invited, 262 agreed and completed baseline, 212 completed baseline and follow-up) Genomic test(s) used: BRCA1/BRCA2 Reference standard: Clinical outcome (measures of family relationships [conflict, cohesiveness, expressiveness] using Family Relationship Index) Test reliability established?:

Yes Statistical tests used: Paired t-tests, ANOVAs; linear regression Definition of positive and negative on screening test:NA 

Age:Range: “over half over age of 40” Menopausal status (n [%]):NR; 35% male Race/ethnicity (n [%]):

“Primarily Caucasian” Risk factors (n [%]):Family history: 212 (100%) Diagnoses (n [%]): Ovarian cancer: 0 Borderline: 0 Benign ovarian mass: 0 Other: 0 Healthy controls: 100% Inclusion criteria:Age > 18, family members with at least one BRCA1 or 2 mutation identified Exclusion criteria:None specified 

Use of test results: Offered genetic testing Outcomes measured: Measures of family relationships 

1) Subjects who declined genetic testing had positive changes in family relationships; expressiveness and cohesiveness increased compared to those who chose testing. 2) Abnormal test result was associated with decreased expressiveness compared to negative test result (trend, but not significant at p < 0.05). 3) Sex not significant factor in multivariate analysis. 

Comme- Same McInern- Familiin previcommu- Baselthan avaverage - 212 sufamilies study apossibly Quality For cohUnbiase(prospesubjectLarge sAdequacohort: Use of test: + Use of ascertaAdequaCompleAnalysiand repGrade: 
A-167

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Appendix E: Peer Reviewers 

The Duke Evidence-based Practice Center is grateful to the following peer reviewers who 

read and commented on a draft version of this report: 

Ralph J. Coates, PhD, Associate Director for Science, Division of Cancer Prevention and 

Control, Centers for Disease Control and Prevention, Atlanta, Georgia 

Tracy G. Lively, PhD, Associate Chief, Diagnostics Evaluation Branch, Cancer 

Diagnosis Program, National Cancer Institute, Rockville, Maryland 

Edward E. Partridge, MD, Professor, Obstetrics and Gynecology, and Interim Director, 

UAB Comprehensive Cancer Center, Birmingham, Alabama 

Gurvaneet Randhawa, MD, MPH, Center for Outcomes and Evidence, Agency for 

Healthcare Research and Quality, Rockville, Maryland 

Edward (Ted) Trimble, MD, MPH, Head of Gynecologic Cancer Therapeutics and 

Quality of Cancer Care Therapeutics, National Cancer Institute, Rockville, Maryland 

Combined comments from the Evaluation of Genomics Applications in Practice and 

Prevention (EGAPP)/Centers for Disease Control and Prevention (CDC) Discussion 

Group 

Nominations for peer reviewers were solicited from several sources, including the project’s 

technical expert panel and interested federal agencies. The list of nominees was vetted and 

approved by the Agency for Healthcare Research and Quality (AHRQ).