Title: ROCkeTS - Refining Ovarian Cancer Test Accuracy Scores
Funder: Department of Health and Social Care

Abstract: Objectives: To identify, refine and validate tests and risk prediction models that estimate the probability of having ovarian cancer (OC) for post and premenopausal women with suspected OC, applicable to secondary and primary care. To define thresholds of predicted risk from the test and models that inform decisions for patient management. Design: Primary and secondary research in 4 phases. Phase 1 Systematic reviews, where possible, meta-analyses of the accuracy of individual tests (predictors) and existing risk prediction models. Phase 2 Refinement of existing models based on including additional predictors within datasets from UKCTOCS, UKOPS and IOTA studies. Phase 3. Building on Phases 1 and 2, most promising tests and risk prediction models will be externally validated in a prospective study. Phase 4. Development of models of pathways and cost comparisons of alternative tests. Pathways will incorporate the differences in patient management guided by different thresholds. Reference standard is histology or 12 month clinical follow-up. Strategy for reviewing literature: Systematic searches will focus on published and grey literature on serum markers, ultrasonogram (USG) scoring and symptom indices, combination of tests or scoring systems. Health technologies being assessed: At outline, we believe a new risk prediction model may incorporate: symptom scores, USG and serum markers, possibly CA125 and novel marker He4. If systematic searches identify other tests, we will evaluate and incorporate as appropriate. Setting for Phase 3 study - Outpatients, 2 week, inpatient, emergency referrals to secondary care. Target population: Post- and premenopausal women with suspected OC. Inclusion criteria: Women with symptoms of suspected OC and raised CA125 and/ or abnormal USG. Exclusion criteria: USG reveals non ovarian pathology e.g. fibroids or simple ovarian cysts < 5cm in size (very low risk of malignancy). Primary outcome measure: Identification of tests and the development and validation of a risk prediction model for estimating the probability of OC in women with suspected OC; key measures are the accuracy of tests and models in terms of discrimination ability (e.g. sensitivity, specificity) and calibration (observed versus predicted probabilities), and identification of thresholds to guide patient management decisions. Sample size: Postmenopausal women - Based on prevalence of 30% of OC in referred women, with sensitivity and specificity of RMI 250 assumed to be 70% and 90%, sample size of 1333 provides 90% power to detect an increase of sensitivity to 80% and specificity to 95% in paired data. With loss to follow-up <5%, sample size is 1400. Premenopausal women - Prevalence of OC in referred women with suspected OC is 10% (audit data). A sample size of 1000 with loss to follow-up<5%, sample size of 1050 will provide 100 OC events and be able to detect an increase in specificity from 46% to 54% and in sensitivity from 72% to 82%, with allowance of 5% loss to follow-up. Team: This is a strong multidisciplinary team comprising experts in OC detection, primary care, systematic reviews, test evaluation, biostatistics, risk prediction modelling, health economics, accredited trials unit, and service use

Lay abstract: Our research project will identify and confirm tests that can help hospital doctors and GP’s make a better diagnosis of ovarian cancer. Ovarian cancer affects 6500 women each year, only 40% of women will still be alive 5 years after diagnosis. Commonly, 80% of women are diagnosed at an advanced stage of cancer, and have to undergo extensive surgery and chemotherapy. NICE has issued guidelines to GP’s, to test women if they have certain symptoms of bloating/tummy discomfort with a blood test CA125 and then a pelvic ultrasound if the CA125 test result is abnormal. However a published survey of 258 GP’s and a hospital audit suggest that GPs refer patients if they have symptoms or if either test is abnormal. In fact the blood test CA125 can be raised in lots of other innocent conditions e.g. during menstrual periods. Also in younger women, non-cancerous cysts on the ovary are in fact very common. Conversely CA125 is only raised in half the women with early ovarian cancer. This means that many women are referred who actually have a very low risk of cancer, and others are not referred until their cancer has reached a more advanced stage. By identifying better tests for ovarian cancer, we may be able to diagnose more women with ovarian cancer early but also reduce unnecessary tests, hospital visits and distress in women who don’t have cancer. We also know that women with ovarian cancer who undergo thorough surgery by a gynaecological cancer specialist have the best outcomes, so improved testing in hospital after referral may also help to identify the patients who will need this. Our research project will first look at all the published papers on new blood tests, scan scoring and symptom scores. We will then use that information to test existing stored blood samples and data from previous large studies conducted by our team, to fine-tune tests and create new scoring systems (risk prediction models) which can be used to diagnose patients by GPs and after referral in hospital. We will then compare our new risk prediction model against the current prediction score (RMI) in a study of newly presenting patients with suspected ovarian cancer. This study will assess symptoms, scans and blood test results. Finally we will use all of this information to set out best pathways for GP’s and hospital specialists to follow in women with suspected ovarian cancer. We will also collect costs of treatments based on the tests used.

Research activity codes: 4.2 EVALUATION OF MARKERS AND TECHNOLOGIES
Health categories: CANCER AND NEOPLASMS