Despite recent decreases in smoking rates, lung cancer claimed more than 163,000 American lives in 2005. Interest in possible lung cancer screening programs is intense;several large ongoing trials are evaluating imaging technologies to detect early-stage lung cancer. In addition, public and private investment in genomic and proteomic research may add biomarkers to the list of tools for lung cancer screening. Decisions about the appropriate roles of imaging and biomarkers in lung cancer screening programs can be informed by modeling, a formal, transparent way to integrate available data. In the proposed independent phase, I will incorporate genomic and proteomic profiles into the Lung Cancer Policy Model, a comprehensive microsimulation model of lung cancer designed to evaluate the cost, effectiveness, and cost-effectiveness of screening programs. I am an outcomes researcher with experience in developing complex disease simulation models, including the natural history model at the core of the Lung Cancer Policy Model. My long term research agenda is to develop methods and approaches to making disease simulation models more robust and useful to decision makers. Specifically, future approaches for cancer screening, treatment, and surveillance promise to be increasingly tailored to the individual patient. Modeling cancer interventions will require much more detail on individual characteristics and clinical algorithms than is now typical in disease simulation models. The site of the proposed study, the Institute for Technology Assessment (ITA) at Massachusetts General Hospital, offers a rich combination of facilities, expertise, and mentorship. The proposed Sponsor, G. Scott Gazelle, MD MPH PhD, became my primary mentor when I joined the ITA in 1998 as a research assistant, and recently chaired my doctoral dissertation committee. We have established a comfortable and productive work environment and have developed a mentoring plan that will allow me to complete my transition to an independent researcher role. Protected time for coursework, meetings with an Advisory Committee of clinical and modeling experts, and completion of ongoing analyses involving the Lung Cancer Policy Model will position me appropriately to undertake the proposed research and apply for an independent research award.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Transition Award (R00)
Project #
5R00CA126147-05
Application #
8103076
Study Section
Special Emphasis Panel (ZCA1-RTRB-A (O1))
Program Officer
Chen, Huann-Sheng
Project Start
2007-08-01
Project End
2013-03-30
Budget Start
2011-07-01
Budget End
2013-03-30
Support Year
5
Fiscal Year
2011
Total Cost
$231,839
Indirect Cost
Name
Massachusetts General Hospital
Department
Type
DUNS #
073130411
City
Boston
State
MA
Country
United States
Zip Code
02199
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Cott Chubiz, Jessica E; Lee, Janie M; Gilmore, Michael E et al. (2013) Cost-effectiveness of alternating magnetic resonance imaging and digital mammography screening in BRCA1 and BRCA2 gene mutation carriers. Cancer 119:1266-76
Tramontano, Angela C; Cipriano, Lauren E; Kong, Chung Yin et al. (2013) Microsimulation model predicts survival benefit of radiofrequency ablation and stereotactic body radiotherapy versus radiotherapy for treating inoperable stage I non-small cell lung cancer. AJR Am J Roentgenol 200:1020-7
Moolgavkar, Suresh H; Holford, Theodore R; Levy, David T et al. (2012) Impact of reduced tobacco smoking on lung cancer mortality in the United States during 1975-2000. J Natl Cancer Inst 104:541-8
McMahon, Pamela M; Hazelton, William D; Kimmel, Marek et al. (2012) Chapter 13: CISNET lung models: comparison of model assumptions and model structures. Risk Anal 32 Suppl 1:S166-78
Lowry, Kathryn P; Lee, Janie M; Kong, Chung Y et al. (2012) Annual screening strategies in BRCA1 and BRCA2 gene mutation carriers: a comparative effectiveness analysis. Cancer 118:2021-30
McMahon, Pamela M; Kong, Chung Yin; Johnson, Bruce E et al. (2012) Chapter 9: The MGH-HMS lung cancer policy model: tobacco control versus screening. Risk Anal 32 Suppl 1:S117-24
Kong, Chung Y; Lee, Janie M; McMahon, Pamela M et al. (2012) Using radiation risk models in cancer screening simulations: important assumptions and effects on outcome projections. Radiology 262:977-84
McMahon, Pamela M; Kong, Chung Yin; Bouzan, Colleen et al. (2011) Cost-effectiveness of computed tomography screening for lung cancer in the United States. J Thorac Oncol 6:1841-8
Kong, Chung Yin; Nattinger, Kevin J; Hayeck, Tristan J et al. (2011) The impact of obesity on the rise in esophageal adenocarcinoma incidence: estimates from a disease simulation model. Cancer Epidemiol Biomarkers Prev 20:2450-6

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