+-----------------------------------------------------------------------+
| Early Lung Cancer Screening Workshop                                  |
|                                                                       |
| National Cancer Institute / American Cancer Society                   |
|                                                                       |
| March 7^th^ and 8^th^, 2001                                           |
|                                                                       |
| #### Doubletree Hotel, Rockville, MD                                  |
+-----------------------------------------------------------------------+

#### 

#### [Executive Summary]{.underline} .......................................................................................\... 2

#### 

#### [Group 1: Impact of Technology Changes on Screening Studies]{.underline} ......\.............. 4

#### 

#### [Group 2: Methodologies for Evaluation of Screening]{.underline} .................................... 8

#### 

#### [Group 3: Data Standardization and Informatics]{.underline} ............................................. 9

#### 

#### [Group 4: New Frontiers of Science for Screening]{.underline} ......................................\..... 10

#### Group 1 Participants .......................................................................................... 17

#### Group 2 Participants .......................................................................................... 19

#### Group 3 Participants .....................\..................................................................... 20

#### Group 4 Participants ......................................................................................... 21

\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_ \| Revised June
15, 2001 \| \_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_

+-----------------------------------------------------------------------+
| #### **Executive Summary**                                            |
+-----------------------------------------------------------------------+

> Recommendations

[Group 1]{.underline}

a **--** Explore and develop clinical trial designs that can adapt to
changing information. Draw sequential statistical methods and Bayesian
methods from the existing statistical literature on adaptive designs.
Plan for changing technology during the course of a trial and
characterize technology in an ongoing way.

b **--** Observer performance may dominate technology differences and
the effects of screening. Therefore, measure early and train observers
appropriately.

c **--** Consider modest, short-term, intermediate endpoints to allow
cutting losses early.

d **--** Modeling -- do for overall evaluation at variable times
depending upon goal and with detailed data on lead time, length time,
and survival characteristics by cell type for patients treated and not
treated.

e **--** Costs -- consider measuring during trial with conditions
mentioned above; cost effectiveness analysis (CEA) -- worthwhile if
effects are present and "large."

#### f **--** Be aggressive about quality assurance.

[Group 2]{.underline}

# a -- Studies should be designed and performed to answer practice-relevant questions: e.g., "What should be done with small lesions?" 

# b -- Develop models to evaluate varying screening and treatment strategies and risk/outcome in terms of cost and effectiveness.

# c -- There is a need for coordinated and standardized surveillance of both screening and treatment in the community with emphasis on quality assurance.

# d -- All prospective studies of lung cancer screening should be organized to achieve a multi-disciplinary, state-of-the-art approach to detection, treatment, and follow-up. 

# e -- Screening programs provide a unique opportunity to educate patients on risk factors such as smoking. There is a need to evaluate different strategies to reduce smoking.

[Group 3]{.underline}

## a -- Support the development of a repository of standard data elements with an explicit data dictionary that defines key characteristics, such as: the variables to be included, the structure of the data set (field values), XML schema, and data collection methods (e.g., the format of possible questions; prompts used by interviewers; in the future, CAPI, CATI systems). 

b **--** Support innovation in the use of information technology for
data collection, transmission, management, and analysis.

c **--** In order to facilitate research that requires the pooling of
data across diverse studies, support is needed for convening a group of
experts to agree on core data elements, for managing the data system,
for data analysis, and for the creation of public-use research
resources.

[Group 4]{.underline}

a **--** Evaluate spirometry of current and former smokers age fifty
five or younger as an eligibility criterion for lung cancer screening.

b **--** In lung cancer screening trials, collect blood samples to
evaluate genetic polymorphisms that activate and detoxify tobacco
carcinogens and possibly DNA repair as part of a lung cancer screening
trial. This could be undertaken within a subset of patients.

c **--** Collect specimens of blood, sputum and sampling of oral
epithelial cells (e.g. mouthwash or buccal smears) from all screened
participants.

d **--** At the time of resection, preserve samples of blood and tumor
both to validate detection markers and to develop prognostic markers for
metastasis and survival.

## e -- Include biomarker analysis in lung cancer screening trials. 

f **--** Support an infrastructure, such as EDRN, for validation trials
for biomarker profiles of early lung cancer.

g **--** Establish programs and adequate resources for relating
"radiographic markers" of lung cancer to biomarkers of lung cancer.

h **--** Support development and validation of CAD methods for lung
cancer screening.

i **--** Provide adequate support for personnel resources for screening
trials.

j **--** Include collection of basic lung cancer risk data in screening
trials.

k **--** Acquire health economics, quality of life and other outcome
data as part of screening studies.

l -- The integration of the individual components to the entire lung
cancer screening process must be considered from the health care
provider, clinical research and patient advocate perspectives.

+-----------------------------------------------------------------------+
| #### **Group 1: Impact of Technology Changes on Screening Studies**   |
+-----------------------------------------------------------------------+

#### 

Purpose

How can the presumed future changes in low-dose helical computed
tomography (CT) technology be most effectively managed in the clinical
trials setting? How do we deal, from a scientific and research point of
view, with the fact that technology is changing faster than we can study
it? What are the potential issues associated with the integration of
computer-aided diagnosis (CAD) into screening technologies.

# Summary following Discussion

A. [Reader Variability]{.underline}

A random sample of the performance of United States mammographers on a
set of screening mammograms has shown great reader variability (Beam,
Layde, Sullivan, etc., see attached [Figure 1]{.underline}). A similar
situation might hold for readings of spiral CT screening studies. A
randomized controlled trial (RCT) of spiral CT screening will average
over and blur out this and other effects. It could be useful to have
ancillary multiple-reader studies to break out the variability of
overall system performance into its multiple components: e.g., patients
and technology, range of reader skill, relevant interactions or
correlations, and their dependence on lesion size, etc. Methods have now
been developed for separating these using multivariate receiver
operating characteristic (ROC) analysis (Beiden, Wagner, Campbell, Metz,
Jiang: Academic Radiology, July 2001, in press). Thus, it becomes
possible with the appropriate ROC methodology to measure many of the
relevant variables and to model the dependence of outcomes on lesion
size, reader training (with and without computer-assisted reading),
independent physical laboratory measurements, available therapy, and
even the interaction of all of the above with molecular and genetic
markers. These variables can either move performance along a given ROC
curve or up to higher ROC curves ([Figure 2]{.underline}). Thus, the
moving-target effect can be controlled or accounted for by a combination
of the appropriate measurements and refined models.

![](media/image1.wmf){width="2.6944444444444446in"
height="2.7083333333333335in"}

\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_

Changing Test Characteristics

> The effect of a new technology

A.  

B.  To move the performance to a point on a different ROC

> curve.

----------------------------------------------

B. [Assumptions and Ground Rules Agreed On For the
Discussion]{.underline}

> Spiral CT screening studies will have short accrual time (12-24
> months); screening
>
> will occur for 3-5 years; there will be long follow-up periods
>
> Technology changes would be considered for the time-frame of the next
> five years only.
>
> The inclusion of costs for cost-effectiveness evaluation should be
> considered.
>
> Investigators should plan for cost measurements during trial. But this
> is difficult to do (impossible, perhaps) because extra data collected
> as part of clinical trial probably does
>
> not reflect what would happen in the real world in the absence of
> trial.
>
> If cost data are collected, then there will be potential for
> cost-effectiveness evaluation at the end if the effectiveness is
> "large."

C. [What Will Change]{.underline}

> Computed Tomography (CT) -- incremental improvements in resolution
> with possibility to characterize lesions better; no *major* changes
> during accrual or screening period of
>
> next five years
>
> Computer Assisted Diagnosis (CAD) -- image processing and presentation
> will improve;
>
> ergonomic improvements in workstations will occur
>
> Biomarkers will emerge for the risk assessment of the patient and the
> characterization of lesions, including aggressiveness

D.

[Measuring Technology per se (controlling for observers)]{.underline}

> There is a need for the characterization of
>
> There is a neeThere physical performance characteristics across trials
> (e.g., detection of nodules, measurement of change in nodules)

E. [Other Factors Affecting Performance of a Technology]{.underline}

Case Mix -- other diseases and co-morbidity

> Reader Variability -- likely to dominate changes in technology because
> of major
>
> differences (15 points in TP and FP) across readers. Can education
> help? Can CAD help?
>
> Quality Assurance -- performed during study

F. [Current or Planned Clinical Studies on CT]{.underline}

> Observational (Non-Experimental)
>
> 1\. ELCAP, I-ELCAP, NY ELCAP
>
> 2\. Moffitt Cancer Center
>
> 3\. Munster

4\. Mayo

> 5\. Japan ................................................\... more
> than 20,000 patients
>
> Randomized Controlled Trials (RCT)
>
> 1\. Lung Special Study, NCI ................... 3400 patients, up to
> 15,000 by September 2002
>
> 2\. ACRIN, proposed .............................. start-up in late
> 2001 with up to 7000 patients by January 2003

G. [Questions: Non-Experimental vs RCT]{.underline}

Does one accrue more quickly than the other? *No*

> Do changes in technology impact one more than the other\...
>
> ...during accrual (18-24 months)? *No*
>
> ...
>
> during screening (3-5 years)? *No*
>
> ...during follow-up? *Yes, probably*
>
> ...at dissemination? *Maybe, depends on magnitude of changes*
>
> Are results of one more generalizable than the other in terms of
> ethnicity, geography,
>
> gender, co-morbidity, site of care, treatment elements, etc.? *No*

###### Does one have more potential than the other for intermediate endpoints to help with study design and maybe analysis, e.g., biomarkers or other surrogates, distribution of tumors by stage, incidence of interval cancers between screens? *Yes; RCTs may have better controls*

**Conclusions and Recommendations:**

####### Reimbursement considerations -- generally, it is not possible or desirable to freeze 

####### technology during study. 

####### Reimbursement considerations -- modeling can be used to estimate the impact of new performance characteristics of the imaging technology with the conditions indicated below 

####### in Recommendation 1(d). 

Study design -- Non-Experimental and RCT are both useful

...................................................................................................................................

...................................................................................................................................

*Recommendation 1(a).* Explore and develop clinical trial designs that
can adapt to changing information. Draw sequential statistical methods
and Bayesian methods from the existing statistical literature on
adaptive designs. Plan for changing technology during the course

of a trial and characterize technology in an ongoing way.

*Recommendation 1(b).* Observer performance may dominate technology
differences and

effects of screening. Therefore, measure early and train observers
appropriately.

*Recommendation 1(c).* Consider modest, short-term, intermediate
endpoints to allow

cutting losses early.

*Recommendation 1(d).* Modeling -- do for overall evaluation at variable
times depending upon goal and with detailed data on lead time, length
time, and survival characteristics by

cell type for patients treated and not treated.

*Recommendation 1(e).* Costs -- consider measuring during trial with
conditions mentioned

above; cost-effectiveness analysis (CEA) -- worthwhile if effects are
present and "large."

*Recommendation 1(f).* Be aggressive about quality assurance.

# 

# 

# 

+-----------------------------------------------------------------------+
| #### **Group 2: Methodologies for Evaluation of Screening**           |
+-----------------------------------------------------------------------+

# 

# Purpose

# An assessment of the various methodologic approaches to evaluating screening performance, efficacy and other aspects of new screening technologies in trials and observational data.

# Summary following Discussion:

# To answer the question, "Is Screening Effective?" -- Majority opinion was that an RCT is 

# desirable; a feasibility study is underway by NCI

# Some participants favored an alternative design

For studies of natural history -- data from observational and
experimental studies are contributory to understanding predictors of
progression

# Conclusions and Recommendations:

# *Recommendation 2(a).* URGENT: Studies should be designed and performed to answer 

# practice-relevant questions: e.g., "What should be done with small lesions?" 

# Current opportunity -- randomized trial of small lesions to treatment or no-treatment groups; perform a feasibility study. Analyze accumulating data in a coordinated and standardized fashion 

# to evaluate experience with these small lesions.

# *Recommendation 2(b).* Develop models to evaluate varying screening and treatment 

# strategies and risk/outcome in terms of cost and effectiveness.

# *Recommendation 2(c).* There is a need for coordinated and standardized surveillance of 

# both screening and treatment in the community with emphasis on quality assurance.

# *Recommendation 2(d).* All prospective studies of lung cancer screening should be organized to 

# achieve a multi-disciplinary, state-of-the-art approach to detection, treatment, and follow-up. 

# *Recommendation 2(e*). Screening programs provide a unique opportunity to educate patients on risk factors such as smoking. There is a need to evaluate different strategies to reduce smoking.

# 

+-----------------------------------------------------------------------+
| #### **Group 3: Data Standardization and Informatics**                |
+-----------------------------------------------------------------------+

Purpose

The type of comparisons to be made across studies dictate the degree of
data standardization required for various factors, such as study
population, criteria for entry, follow-up, and intervention. What types
of comparisons are feasible across studies? What will be the influence
of rapidly evolving use of computerized systems for management of data
in research and in clinical practice?

Summary following Discussion:

## With lung cancer screening CT studies now beginning, the potential for data standardization and 

## informatics to facilitate combining and comparing data across diverse study designs is great.

Specifics of innovation in information technology and its influence on
research were not addressed.

## In the development of major collaborative initiatives, funding agencies should consider and plan for incorporating data collected in this process, such as CT images, into public-use research resources.

**Conclusions and Recommendations:**

## *Recommendation 3(a).* Support the development of a repository of standard data elements with an explicit data dictionary that defines key characteristics, such as: the variables to be included, the structure of the data set (field values), XML schema, and data collection methods (e.g., the format of 

## possible questions; prompts used by interviewers; in the future, CAPI, CATI systems). 

## This process should consider identifying minimum data elements that might be shared across diverse study designs. In the area of lung cancer screening by CT, minimum data elements are likely to include patient characteristics, imaging data, and pathology data. In the future, minimum data elements may also include systems data.

## *Recommendation 3(b).* Support innovation in the use of information technology for data 

## collection, transmission, management, and analysis. 

## *Recommendation 3(c).* In order to facilitate research that requires the pooling of data across diverse studies, support is needed for convening a group of experts to agree on core data elements, for managing the data system, for data analysis, and for the creation of public-use research resources.

### 

+-----------------------------------------------------------------------+
| #### **Group 4: New Frontiers of Science for Screening**              |
+-----------------------------------------------------------------------+

**Purpose**

An assessment of what other "science of early detection" should be
brought to bear as populations at high risk are identified and followed.

**Summary following Discussion:**

A. [Biomarker Specimen Collection]{.underline}

Trials of lung cancer screening with helical CT provide a unique
opportunity to define the molecular dynamics of very early lung cancer
that other studies do not offer. Potential

applications of biomarkers are:

i.  Identify individuals at high risk of developing radiologic and other
    early disease endpoints

ii. Complement CT screening and early lung cancer detection

iii. Refine work-up of CT positive findings (growing nodules) and
     thereby improve specificity

iv. Serve as intermediate endpoints to monitor medical management of
    growing nodules by

> phenotyping the status of critical molecular pathways in the early
> cancer.

These are new areas for biomarker application with the potential for
providing additional information at modest marginal costs and without
incurring morbidity to the screening subjects. Nevertheless, if the
specimens are not collected, preserved, and linked to patient
demographic, exposure and outcome data by design at the beginning of a
screening trial, the role of biomarkers

cannot be defined.

As these rare specimens of early lesions will represent a national
resource, optimal protocols for collecting, preserving, storing,
shipping and labeling for long term storage of such specimens should be
developed. To realize synergies from existing NCI research investments,
this validation work should be done in collaboration with expert groups
such as the Lung Cancer SPORES, the Early Detection Research Network
(EDRN) and the cohorts (e.g. Framingham, PLCO, EPIC). These protocols
could be developed at national workshops and the result made available
on web-

based forums.

The careful collection and handling of specimens at the screening
institutions is critical to maintain the value of the specimens for
future investigation. In its planning for lung cancer screening trials,
NCI should fund the support staff and data managers charged with
obtaining and processing, as well as the infrastructure required for
storage and information management of,

these most valuable specimens.

These specimens must be made available for sharing through a central
repository that includes best practices for specimen management, safety
and security, maintaining confidentiality and

administrative procedures for specimen sharing.

Additionally, specimens should be collected to address specific
hypothese.

B. [Potential Applications of Biomarkers]{.underline}

i\. To identify individuals at high risk of developing radiologic and
other early disease endpoints

> Evidence exists for testing the hypothesis that the enhanced lung
> cancer risk among obstructed (FEV~1~ / FVC \<70%), current and former
> smokers (i.e., between ages 45-54)
>
> justifies their inclusion in a lung cancer screening trial.
>
> Evidence also exists for testing the hypothesis that a valid lung
> cancer risk profile may be determined by genetic polymorphisms for
> enzymes that activate and detoxify
>
> tobacco carcinogens and possibly DNA repair.
>
> *Recommendation 4(a).* Evaluate spirometry of current and former
> smokers age fifty
>
> five or younger as an eligibility criterion for lung cancer screening.
>
> *Recommendation 4(b).* In lung cancer screening trials, collect blood
> samples to evaluate genetic polymorphisms that activate and detoxify
> tobacco carcinogens and possibly DNA repair as part of a lung cancer
> screening trial. This could be undertaken within a subset of patients.
>
> ii\. To complement CT screening and early lung cancer detection
>
> Biomarkers of lung cancer have the potential to enhance helical CT
> screening by identifying lung cancers at complementary stages (clonal,
> pre-invasive vs. invasive), at complementary locations (central vs.
> peripheral) and complementary cell type (epidermoid emphasis vs.
> adenocarcinoma emphasis). Biomarkers may validate which
>
> CT-detected lesions may progress and which may remain dormant.
>
> *Recommendation 4(c).* Collect specimens of blood, sputum and sampling
> of oral epithelial cells (e.g. mouthwash or buccal smears) from all
> screened participants. The serial acquisition of sputum or oral
> epithelial specimens should be extended to at least detected cases and
> comparable controls and a sampling of the false-positive cases. This
> is an opportunity of paramount importance to define the critical
> molecular events of early lung cancer. A portion of the epithelial
> cells should be acquired and stored in a fashion to ensure regular
> recovery of high quality RNA.
>
> iii\. To refine work-up of positive CT findings (growing nodules)
>
> The appropriate work-up and management of patients with very early
> lung cancer is not yet established. During the workup, specimens
> should be collected to test biomarkers of prognosis and staging
> ultimately to enhance clinical decision-making. If clinical management
> entails bronchoscopy, it is recommended that specimens be collected of
> bronchial lavage/brushings, and bronchial biopsies. Similarly
> specimens
>
> of clinically indicated needle biopsy should be preserved.
>
> The scope and span of specimen collection should be subject to careful
> statistical design with the view of maximizing opportunity while
> minimizing costs.   It will not be necessary or desirable to collect
> specimens from all subjects enrolled in a CT study.  It must be borne
> in mind that the *unique* opportunity presented by a lung cancer
> screening study is that of obtaining specimens representing the very
> earliest phases of lung cancer
>
> development. Specimen collection must be focussed on exploiting this
> opportunity.
>
> *Recommendation 4(d).* At the time of resection, preserve samples of
> blood and tumor both to validate detection markers and to develop
> prognostic markers for metastasis and survival. This may only be
> feasible at SPORE Sites and Cancer Centers with strong molecular
> diagnostic infrastructure but at least this opportunity should be
> included.
>
> iv\. To serve as intermediate endpoints to monitor medical management
> of growing nodules
>
> Randomized trials of medical vs. surgical management of growing
> nodules should be developed as a separate activity. Nevertheless,
> during the course of such trials, the collection of biomarkers to
> assess relevant molecular targets for pharmacologic intervention,
> treatment response and outcome should be added to the archive of the
>
> proposed lung cancer screening trial to maintain the integrity of the
> specimen resource.

## *Recommendation 4(e).* Include biomarker analysis in lung cancer screening trials. A biomarkers infrastructure is urgently needed to collect specimens from these earliest lung cancer lesions to characterize the molecular structure of early cancer recognized by CT. 

C. [Selection and Validation of Early Lung Cancer Markers in a Lung
Cancer Screening Trial]{.underline}

> Numerous lung cancer biomarkers have been extensively reported and are
> at various stages of inquiry and validation. It is anticipated that
> during the lifetime of this trial, several of these will be validated
> by existing NCI mechanisms (SPORE, EDRN) and become
>
> > appropriate for testing on the archived specimens collected here.
>
> An array of relevant markers will require a matrix of specimens. This
> will become the first opportunity to apply extraordinary
> groundbreaking molecular technology (i.e. high throughput microarray,
> genomics, proteomics) to the earliest lesions of lung cancer as
>
> detected by CT.
>
> *Recommendation 4(f).* Support an infrastructure, such as EDRN, for
> validation trials for biomarker profiles of early lung cancer.

# D. [Computer-Aided Diagnosis]{.underline}

> Computer-aided diagnosis (CAD) will be an essential part of CT lung
> cancer screening
>
> and should be included within a screening trial.
>
> *Background*
>
> Computer analysis of breast images has yielded extremely promising
> results.
>
> CAD is being developed for the detection and diagnosis of breast
> cancer as well as for breast cancer risk assessment. Use of computer
> analysis of screening mammography has one FDA-approved system and has
> been in routine clinical use for two years. Also, the use of computer
> analysis of diagnostic mammograms has been shown to be beneficial in
> observer performance studies and is currently being
>
> translated to the clinical environment.
>
> However, computer-aided diagnosis research is still in its infancy
> relative to the
>
> potential gains achievable by:
>
> • Expanding CAD research to multi-modality images (x-ray, ultrasound,
> MRI)
>
> • Expanding CAD research to other diseases
>
> • Expanding CAD research to other medical tasks, such as predicting
> prognosis
>
> • Optimizing training and evaluation based on specific patient
> populations
>
> • Incorporation of clinical information

# *Why the interest in CAD now?*

-   Quality of digital images is extremely good now

-   Computers are faster

-   Large databases of images are feasible now

-   There are new computer vision techniques

-   There is a recognized real medical need -- e.g., in screening
    mammography

-   Investigators are sensitive to constraints imposed by the end user,
    e.g., the radiologist

-   The public wants CAD

-   CAD is accepted and valued by the radiologist/clinician

-   There is a shortage of radiologists

# *Is there a potential synergy for imaging and biomarkers?*

> Use of computer analysis in the assessment of "normal" mammograms is
> being investigated for estimating breast cancer risk. Results from the
> computerized analysis of mammographic parenchymal patterns show that
> women at high risk for breast cancer have dense breasts and the
> pattern of the density tends to be coarse and low in contrast. Such
> computer analyses yield "radiographic markers" and these methods have
> been shown to be promising in 1) correlation studies with Gail and
> Claus "clinical markers," 2) ROC analysis between women at low risk
> for breast cancer and those women who have tested positive for the
> BRCA1/BRCA2 gene mutation biomarkers, and 3) ROC analysis between
> women at low risk for breast cancer and those women who have breast
> cancer. Identification and close follow-up of high-risk women may
> provide an opportunity for earlier detection of breast cancer
>
> as well as a means for monitoring prevention and treatment regimes.
>
> Computer analysis of lesions found on spiral CT can undergo
> computerized classification analysis to assess the likelihood that the
> lesion is cancerous and such
>
> radiographic markers could be related to biomarkers.
>
> *Recommendation 4(g).* Establish programs and adequate resources for
> relating "radiographic markers" of lung cancer to biomarkers of lung
> cancer.

# *How soon will CAD influence lung cancer screening with spiral CT?*

> Computer detection is already a reality in breast cancer screening.
> Integration with CT image acquisition systems is expected to be easier
> than with mammography since CT is already "digital". Various
> investigators are developing computer methods for "nodules" in CT,
> although at this early stage, performance levels vary. Databases are
> essential in that sufficient numbers of cases need to be collected in
> order to develop, train, and validate computerized methods. Lung
> nodule detection CT has not yet undergone any observer studies but it
> is expected that CAD will only help since (1) the oversight error is
> similar to that in screening mammography and (2) the amount of
>
> image data is becoming overwhelming for human vision.
>
> Computerized methods for the detection of "nodules" on CT may be ready
> within one year from various groups. Note that it will be necessary to
> incorporate computer results
>
> into any modeling that is being performed for clinical trial design
> and extrapolation.
>
> Besides aiding in lung cancer screening with spiral CT, computer
> analysis of CT images is expected to also help in the assessment of
> tumor response -- e.g., objective
>
> measure of tumor volume.
>
> Thus, appropriate data format and archiving are important for the
> inclusion of CAD in lung cancer screening trials. Depending on when
> trials begin, CAD may be
>
> included from the start.
>
> *Recommendation 4(h).* Support development and validation of CAD
> methods for lung cancer screening. This would include database and
> algorithm development, validation with observer studies, and
> incorporation into clinical trials.

# 

# E. [Support Personnel]{.underline}

> The role of support personnel (e.g., research nurses, clinical
> research coordinators, data entry personnel) should not be
> underestimated for successful completion of trials. For example, for
> the epidemiological data collection, a full-time research nurse
> coordinator is needed in addition to an effective and efficient
> questionnaire. Such a person would be involved in explaining and
> obtaining consent, assuring that the questionnaire is correctly
>
> completed, and in accurate data entry. These areas are under-funded.
>
> *Recommendation 4(i).* Provide adequate support for personnel
> resources for screening trials. In fact, submitted proposals should be
> examined to insure that sufficient funding is requested to actually
> perform the clinical trial and collect all the necessary data.

# F. [Epidemiological Data]{.underline}

> The minimum standard for any screening trial must include planning for
> the collection of critical lung cancer covariates data as well as
> factors that might influence other markers, disease or radiologic
> endpoints. Questionnaire instruments and collection methods should be
> compatible with other studies. These data are essential for study
> results to be interpretable. For example, recent smoking data is
> crucial to distinguish image findings that are closely related to
> smoking from those that are due to early disease. Broadly, virtually
> every category of biomarker and other study question requires
> covariate data for proper interpretation. Core categories of
> information include basic demographics, detailed smoking history,
> history of respiratory illness, key occupational exposures, and family
> history of lung cancer. Other tobacco use, residential and
> reproductive history, environmental exposure, diet and other data
> might be desirable but may not be practical for collection. Proper
> procedures for maintaining confidentiality, coding, keying, storing
> and linking data, applying innovative technologies (i.e. CAPI) should
> be part of the basic design. Generally, some information collection
> (i.e. current smoking, weight change,
>
> treatment, etc) should accompany any subsequent bio-specimen
> collection or imaging.
>
> *Recommendation 4(j).* Include collection of basic lung cancer risk
> data in screening trials.

G. [Other Screening Issues]{.underline}

> Research into CT, biomarker and other potential lung cancer screening
> modalities should first characterize the performance (sensitivity and
> specificity) of the test when applied to
>
> particular target populations (predictive values).
>
> It is particularly useful to design trials that apply different
> screening tests to the same subjects at the same time, because it is
> the correlations of test errors that determine whether the tests are
> complementary and work well together or whether one is superior
>
> > to the other.
>
> The performance criteria for each screening test, including CT,
> biomarker and other tests, are model dependent. That is, they depend
> not only on the performance characteristics of all other tests in the
> screening process, but also on the morbidity and
>
> efficacy of the available therapeutic options.
>
> The screening model must include an estimation of cost effectiveness,
> accounting not
>
> only for dollar costs but also iatrogenic morbidity (i.e. quality of
> life).
>
> *Recommendation 4(k).* Acquire health economics, quality of life and
> other outcome data as part of screening studies.

### H. [Clinical Management of Early Lesions]{.underline}

> The eventual reduction of lung cancer mortality will be most
> completely realized in a setting where the lessons from other cancers
> are integrated into the design of the screening process. This entails
> defining all of the elements involved in the screening and optimizing
> each component. For example, the diagnostic evaluation of an
> individual with an "indeterminate finding" on spiral CT needs to be
> standardized based on some validated clinical management algorithm.
>
> Another area of strategic importance is the issue of how a person with
> a "positive" CT finding is going to be definitively treated. Initial
> management is likely to entail surgical resection of the involved lobe
> and mediastinal evaluation. Thoracic surgery community is already
> evaluating whether less invasive procedures would be sufficient to
> routinely permit effective control of the small volume primary lung
> cancer and to conserve lung tissue to enable management of
> metachronous primaries.
>
> Other innovative approaches may also emerge as particularly useful in
> the effective management of these small volume primary cancers and
> fostering research in this area should be an urgent priority. Examples
> of candidate managements in this regard include endoscopic surgical
> approaches, photodynamic laser therapy, conformal radiation therapy,
> brachytherapy as well as medical managements using direct drug
>
> delivery approaches (aerosols).
>
> *Recommendation 4(l).* The integration of the individual components to
> the entire lung cancer screening process must be considered from the
> health care provider, clinical research and patient advocate
> perspectives. In particular, urgent funding is required for clinical
> research into management of early lesions.

I. [The Future Lung Cancer Screening Journey Cascade]{.underline}

> Group 4 considered the long-term aim of developing an early lung
> cancer detection cascade based on the integration of spiral CT with
> genotyping and biomarkers. This model ([Figure 3]{.underline})
> provides a scenario that could be considered for identifying
> individuals at risk of developing lung cancer, by undertaking subgroup
> analysis prior to using imaging techniques. This approach may be
> feasible within the health economics of the nation.

![](media/image1.bmp)![](media/image1.bmp)

+-----------------------------------------------------------------------+
| #### Group 1 Participants                                             |
+-----------------------------------------------------------------------+

Craig A. Beam, Ph.D.

Associate Professor

Radiology Research

Medical College of Wisconsin

8701 Watertown Plank Road

Milwaukee, WI 53226

Ph: 414 805-4458

Fx: 414 805-4354

e-mail: <cbeam@mcw.edu>

Joseph Chin, M.D., M.S.

Health Care Financing Administration

S3-23-07, South

Baltimore, MD

Ph: 410 786-4371

e-mail: <jchin@hcfa.gov>

Bruce J. Hillman, M.D.

Principal Investigator

Department of Radiology

University of Virginia, Health Sciences

Center

P.O. Box 800170

Charlottesville, VA 22908

Ph: 804 982-0211

Fx: 804 924-8349

e-mail: <bjh8a@virginia.edu>

G. Scott Gazelle, M.D., M.P.H., Ph.D.

Director of Decision Analysis and

Technology Assessment Group

Massachusetts General Hospital

MGH Data Group

Zero Emerson Place, Suite 2H

Boston, MA 02114

Ph: 617 726-4047

Fx: 617 726-9414

e-mail: <scott@the-data-group.org>

Steven Krosnick, M.D.

Congressionally Directed Medical

Research Program

U.S. Army Medical Research &

Material Command

MCMR-PLF

1077 Patchel Street

Fort Detrick, MD 21702-5024

Ph: 301 619-7522

Fx: 301 619-7796

e-m: <steven.krosnick@det.amedd.army.mil>

Stanley Fox, Ph.D.

Manager, CT Advance Applications

General Electric

P.O. Box 414

Milwaukee, WI 53201

Ph: 262 785-5143

Fx: 262 785-5493

e-mail: <Stanley.fox@med.ge.com>

Parthiv J. Mahadevia, M.D.

Robert Wood Johnson Clinical Scholar

John Hopkins University

600 North Wolfe Street, Carnegie 291

Baltimore, MD 21287-6220

Ph: 410 502-7505

Fx: 410 614-9068

e-mail: <pmahadev@jhmi.edu>

Pamela Marcus, Ph.D.

Epidemiologist

Biometry Research Group

Division of Cancer Prevention

National Cancer Institute

6130 Executive Blvd., Room 344

Rockville, MD 20852

Ph: 301 496-7468

Fx: 301 401-0816

e-mail: <marcusp@mail.nih.gov>

Barbara J. McNeil, M.D., Ph.D.

Ridley Watts Professor of Health

Care Policy and Professor of Radiology

Harvard Medical School and Brigham and

Women's Hospital

180 Longwood Avenue

Department of Health Care Policy, HMS

Boston, MA 02115

Ph: 617 432-1909

Fx: 617 432-3503

e-mail: <mcneil@hcp.med.harvard.edu>

Steven Seltzer, M.D.

Brigham and Women's Hospital

75 Francis Street

Boston, MA 02115-6092

Ph: 617 732-6273

Fx: 617 732-6336

e-mail: [seltzer@ulna.bwh.harvard.edu](mailto:seltzers@mail.nih.gov)

Richard Simon, D.Sc.

Chief, Biometric Research Branch

Head, Molecular Statistics & Bioinformatics

Section

Division of Cancer Treatment & Diagnosis

National Cancer Institute

6130 Executive Blvd., Room 8134

Rockville, MD 20852

Ph: 301 496-0975

Fx: 301 402-0560

e-mail: <rsimon@mail.nih.gov>

Edward Staab, M.D.

Branch Chief

Diagnostic Imaging Branch

Biomedical Imaging Program

National Cancer Institute

6130 Executive Blvd., Suite 6000

Rockville, MD 20852

Ph: 301 435-9184

Fx: 301 480-3507

e-mail: <staabe@mail.nih.gov>

Sean Tunis, M.D.

Health Care Financing Administration

S3-09-27, South

Baltimore, MD

Ph: 410-786-2744

e-mail: <stunis@hcfa.gov>

Michael W. Vannier, M.D.

Professor

Department of Radiology

University of Iowa

200 Hawkins Drive

Iowa City, IA 52242-1077

Ph: 319 356-3371

Fx: 319 356-2220

e-mail: <Michael-vannier@uiowa.edu>

Robert Wagner, Ph.D.

US Food and Drug Administration

12720 Twinbrook Parkway

Rockville, MD 20857

Ph: 301 443-5020, ext. 143

Fx: 301 443-9101

e-mail: <rfw@cdrh.fda.gov>

David Yankelevitz, M.D.

Professor of Radiology

Department of Radiology

1201 East 21 Street

Brooklyn, NY 11210

Ph: 212 746-2011

Fx: 212 746-2811

e-mail: <dyankele@med.cornell.edu>

+-----------------------------------------------------------------------+
| #### Group 2 Participants                                             |
+-----------------------------------------------------------------------+

William C. Black, M.D.

Professor of Radiology & Community and

Family Medicine

Department of Radiology

Dartmouth-Hitchcock Medical Center

One Medical Center Drive

Lebanon, NH 03756

Ph: 603 650-5846

Fx: 603 650-5455

e-mail: <William.black@hitchcock.org>

Peter Boyle, Ph.D.

Director

Division of Epidemiology and Biostatistics

European Institute of Oncology

Via Ripamonti 435

20141 Milan, Italy

Ph: 39 025 748-9815

Fx: 39 025 748-9922

e-mail: <peter.boyle@ieo.it>

Martin Brown, Ph.D.

Division of Cancer Control and

Population Sciences

National Cancer Institute

6130 Executive Blvd., 320F

Rockville, MD 20852

Ph: 301 496-5716

Fx: 301 425-3710

e-mail: <brownm@mail.nih.gov>

Constantine Gatsonis, Ph. D.

Professor of Medical Science (Biostatistics)

and Applied Mathematics

Director, Center for Statistical Sciences

Brown University, Box G-H

Providence, RI 02912

Ph: 401 863-9183

Fx: 401 863-9182

e-mail: <gatsonis@stat.brown.edu>

Marek Kimmel, Ph.D.

Professor of Statistics

Department of Statistics, MS 138

Rice University

6100 Main Street

Houston, TX 77005

Ph: 713 348-5255

Fx: 713 348-5476

e-mail: <kimmel@stat.rice.edu>

Jack S. Mandel, Ph.D., M.P.H.

Group Vice President

Exponent

149 Commonwealth Drive

Menlo Park, CA 94025

Ph: 650 688-1773

Fx: 650-688-1799

e-mail: <jmandel@exponent.co>

Olli S. Miettinen, M.D., Ph.D.

Department of Epidemiology & Biostatistics

McGill University

1020 Pine Avenue, West

Montreal, PQH3A1A2 Canada

Ph: 514 398-2600

Fx: 514 398-4503

e-mail: <osm@epid.lan.mcgill.ca>

Eugenio Paci, M.D.

Head of Department

Unit of Descriptive and Clinical

Epidemiology

Center for Studies & Prevention of Cancer

Via Di S. Salvi 12

50135 Florence, Italy

Ph: 39 055 626-3610

Fx: 39 055 67-9954

e-mail: <epid1@user.ats.it>

Philip C. Prorok, Ph.D.

Chief, Biometry Research Group

Division of Cancer Prevention

National Cancer Institute

6130 Executive Plaza North, Room 344

Rockville, MD 20892

Ph: 301 496-7709

Fx: 301 402-0816

e-mail: <prorokp@mail.nih.gov>

Joseph Selby, M.D.

Director for Division of Research

Kaiser Permanente Northern California

3505 Broadway

Oakland, CA 94611

Ph: 510 450-2106

Fx: 510 450-2073

e-mail: <jvs@dor.kaiser.org>

+-----------------------------------------------------------------------+
| #### Group 3 Participants                                             |
+-----------------------------------------------------------------------+

Rachel Ballard-Barbash, M.D., M.P.H.

Division of Cancer Control and Population

Sciences

National Cancer Institute

6130 Executive Blvd., Room 320D

Rockville, MD 20852

Ph: 301 402-4366

Fx: 301 435-3710

e-mail: <barbashr@mail.nih.gov>

Christine D. Berg, M.D.

Director

Suburban Hospital Cancer Center

Affiliated with Johns Hopkins

Oncology Center

6410 Rockledge Drive, Suite 640

Bethesda, MD 20817

Ph: 301 896-3021

Fx: 301 214-2280

e-mail: <cberg@suburbanhospital.org>

Jules Berman, M.D., Ph.D.

Program Director

Division of Cancer Treatment and Diagnosis

National Cancer Insitute

6130 Executive Boulevard, Room 6035A

Rockville, MD 20852

Ph: (301) 496-7147

Fx: (301) 402-7819

e-mail: <jb426q@nih.gov>

Rob Boer, Ph.D.

Natural Scientist

Health Unit

RAND

1700 Main Street, M-10

P.O. Box 2138

Santa Monica, CA 90407-2138

Ph: 310 393-0411

Fx: 310 393-4818

e-mail: <boer@rand.org>

Richard Fagerstrom, Ph.D.

Mathematical Statistician

Division of Cancer Prevention

National Cancer Institute

6130 Executive Blvd., Room 344

Rockville, MD 20852

Ph: 301 496-7458

Fx: 301b 402-0816

e-mail: <fagerstr@mail.nih.gov>

Yen-pen Chiange, Ph.D.

Agency for Healthcare Research and

Quality

6010 Executive Blvd., Suite 300

Rockville, MD 20852

Ph: 301 594-4035

Fx: 301 594-3211

e-mail: <ychiang@ahrq.gov>

Sherri de Coronado, M.S., M.B.A.

Program Analyst

National cancer Institute

6116 Executive Boulevard, Room 2019

Rockville, MD 20852

Ph: 301 435-3870

Fx: 301 402-9636

e-mail: <decorons@mail.nih.gov>

Ilana F. Gareen, Ph.D.

Center for Statistical Sciences

Brown University, Box G-H

Providence, RI 02912

phone: (401) 863-1758

fax: (401) 863-9182

e-mail: <igareen@stat.brown.edu>

Claudia I. Henschke, M.D., Ph.D.

Professor of Radiology

Weil Medical College of Cornell

University

525 East 68^th^ Street

New York, NY 10021

Ph: 212 746-2529

Fx: 212 746-2811

e-mail: <chensch@mail.med.cornell.edu>

Anthony P. Reeves, Ph.D.

Associate Professor

Electrical Engineering

Cornell University

331 Rhodes Hall

Ithaca, NY 14853-5401

Ph: 607 255-2342

Fx: 607 255-9072

e-mail: <reeves@ee.cornell.edu>

Denise Warzel

Bioinformatics

National Cancer Institute

6116 Executive Blvd.

Rockville, MD 20852

Ph: 303 722-9446

e-mail: <denise@warzel.com>

Neil Caporaso, M.D.

Chief, Pharmacogenetics Section

Genetic Epidemiology Branch

Division of Cancer Epidemiology and

Genetics

National Cancer Institute

6120 Executive Blvd., Room 7116

Rockville, MD 20852

Ph: 301 496-4375

Fx: 301 402-4489

e-mail: <caporasn@mail.nih.gov>

John K. Field, Ph.D.

Director, Roy Castle International Centre for

Lung Cancer Research

University of Liverpool

200 London Road

Liverpool L39TA, UK

Ph: 44 151 794-8900

Fx: 44 151 794-8989

e-mail: [**j.k.field@liv.ac.uk**](mailto:j.k.field@liv.ac.uk)

David Garner, Ph.D.

Senior Scientist

Cancer Imaging Department

British Columbia Cancer Research Center

601 West 10^th^ Avenue

Vancouver, BC, Canada

Ph: 604 877-6000, ext. 3047

Fx: 604 877-6063

e-mail: [**dgarner@bccancer.bc.ca**](mailto:dgarner@bccancer.bc.ca)

Maryellen Giger, Ph.D.

Professor of Radiology

The University of Chicago

Department of Radiology, MC 2026

5841 South Maryland Avenue

Chicago, IL 60637

Ph: 773 702-6778

Fx: 773 702-0371

e-mail: <m-giger@uchicago.edu>

James L. Mulshine, M.D.

Head, Intervention Section

National Cancer Institute

Building 10, Room 12N226

9000 Rockville Pike

Rockville, MD 20817

Ph: 301 402-3721

Fx: 301 435-8036

e-mail: <mulshinej@bprb.nci.nih.gov>

Branko Palcic, Ph.D.

Professor of Pathology and Physics

University of British Columbia

Director

Technology Development

British Columbia Cancer Agency

601 West 10^th^ Avenue

Vancouver, BC, Canada

Ph: 604 877-6000, ext. 3037

Fx: 604 877-6063

e-mail: [**bpalcic@bccancer.bc.ca**](mailto:bpalcic@bccancer.bc.ca)

Edward F. Patz, Jr., M.D.

Professor of Radiology

Department of Radiology, Box 3808

Duke University Medical Center

Durham, NC 27710

Ph: 919 684-7311

Fx: 919 684-7123

e-mail: <patz0002@mc.duke.edu>

Elizabeth Slate, Ph.D.

Associate Professor

Department of Biometry and Epidemiology

Medical University of South Carolina

135 Rutledge Avenue, Suite 1148

P.O. Box 250551

Charleston, SC 29425

Ph: 843 876-1100

Fx: 843 876-1126

e-mail: <slateeh@musc.edu>

Sudhir Srivastava, Ph.D., M.P.H.

Chief, Cancer Biomarkers Research Group

Division of Cancer Prevention

National Cancer Institute

6130 Executive Blvd., Room 330

Rockville, MD 20852

Ph: 301 496-3983

Fx: 301 402-0816

e-mail: <srivasts@mail.nih.gov>

Eva Szabo, M.D.

Chief, Lung and Upper Aerodigestive

Cancer Research Group

Division of Cancer Prevention

National Cancer Institute

6130 Executive Blvd., Room 2137

Rockville, MD 20852

Ph: 301 435-2456

Fx: 301 480-3924

e-mail: <szaboe@mail.nih.gov>

Melvyn S. Tockman, M.D., Ph.D.

Professor of Oncology and Medicine

Director, Molecular Screening Program

H. Lee Moffitt Cancer Center & USF

12902 Magnolia Drive

MDC OSWFBB, 2^nd^ Floor

Tampa, FL 33647

Ph: 813 632-1714

Fx: 813 632-1720

e-mail: <tockman@moffitt.usf.edu>

John Gohagan, Ph.D.

Chief, Early Detection Research Group

Division of Cancer Prevention

National Cancer Institute

6130 Executive Blvd., Room 330

Rockville, MD 20892

Ph: 301 496-3982

Fx: 301 402-0816

e-mail: <gohaganj@mail.nih.gov>

Peter Greenwald, M.D., Dr.P.H.

Director

Division of Cancer Prevention

National Cancer Institute

Building 31, Room 10A52

Bethesda, MD 20892

Ph: 301 496-6616

Fx: 301 496-9931

e-mail: <greenwap@mail.nih.gov>

Richard D. Klausner, M.D.

Director

National Cancer Institute

31 Center Drive, Room 11A48

Bethesda, MD 20892

Ph: 301 496-5615

Fx: 301 402-0338

e-mail: <klausner@nih.gov>

Anne Menkens, Ph.D.

Program Director

Molecular Imaging Branch

Biomedical Imaging Program

National Cancer Institute

6130 Executive Blvd., Suite 6000

Rockville, MD 20852

Ph: 301 435-9024

Fx: 301 480-3507

e-mail: <menkensa@mail.nih.gov>

Scott Rivers

Program Manager

Alliance for Lung Cancer Advocacy, Support, and Education (ALCASE)

P.O. Box 849

Vancouver WA 98666

Ph: (360) 696-2436 -or- (800) 298-2436

Fx: (360) 735-1305

e-mail: <sarivers@alcase.org>

John C. Ruckdeschel, M.D.

Center Director

H. Lee Moffitt Cancer Center & USF

12902 Magnolia Drive

Tampa, FL 33612

Ph: 813 979-7265

Fx: 813 979-3919

e-mail: <ruckdeschel@moffitt.usf.edu>

Robert Smith, Ph.D.

Director, Cancer Screening

American Cancer Society

1599 Clifton Road N.E.

Atlanta, GA 30329-4251

Ph: 404 329-7712

Fx: 404 325-2548

e-mail: <rsmith@cancer.org>

Daniel Sullivan, M.D.

Associate Director

Biomedical Imaging Program

National Cancer Institute

6130 Executive Blvd., Suite 6000

Rockville, MD 20852

Ph: 301 435-9532

Fx: 301 480-3507

e-mail: <sullivda@mail.nih.gov>