Sylvia K. Plevritis

My research program takes a systems engineering approach to the study of cancer. Our projects range from developing computational models of cancer biology to developing decision analytic models that inform health policies for cancer control. Recently, we started to model signaling pathways perturbed in the cancer process with the goal of discovering molecular mechanisms underlying cancer initiation and progression and identifying molecular targets for therapy. We have been developing stochastic models of the natural history of cancer and derive estimates of the rates that cancer progresses from non-metastatic to metastatic states using clinical data. We embed our disease models of cancer into population-level simulation models that predict patient outcomes under differing medical interventions; here our work is primarily focused on simulating cancer screening trials and predicting the effectiveness and cost-effectiveness of new cancer imaging technologies.

Representative Publications

Plevritis SK, Salzman P, Sigal BM, Glynn P. A natural history model of stage progression applied to breast cancer, Statistics in Medicine, 2006, April 5 [Epub ahead of print; PubMed ID: 16598706]

Plevritis SK, Kurian A, Sigal Bronislava, Daniel B, Ikeda D, Stockdale F, Garber AM, Cost-effectiveness of Screening for Breast Cancer with Magnetic Resonance Imaging in BRCA1/2 Mutation Carriers, JAMA 24;295(20):2374-84, 2006.

Plevritis SK, Sigal BM, Salzman P, Glynn P, Rosenberg J, A stochastic simulation model of US breast cancer mortality trends from 1975 to 2000, Journal of the National Cancer Institute Monographs 36:86-95, 2006.

Berry DA, Cronin KA, Plevritis SK, Fryback DG, Clark LC, Zelen M, Mandelblatt JS, Yakovlev AY, Habbema JDF, Feuer EJ, Contributions of screening and adjuvant treatment to reduction in breast cancer mortality in the US from 1975 to 2000, New England Journal of Medicine, 353(17):1784-1792, 2005.

Plevritis SK, Decision analysis and simulation modeling for evaluating diagnostic tests on the basis of patient outcomes. American Journal of Radiology, 185(3):581-590, 2005.