almost 20% of breast cancer diagnoses, and neariy 30% of screen-detected breast cancers, are DCIS. Since limitations in our understanding ofthe natural history of DCIS prevent identification ofwhich DCIS tumors will progress into invasive cancers, the management of DCIS requires treatment similar to therapies for Invasive breast cancer even though relative survival after DCIS approaches 100%. Researchers are actively searching for methods to optimize the screening process by identifying prognostic markers to identify DCIS with malignant potential.
We aim to (1) compare current screening processes with a comprehensive, personalized breast cancer screening process that considers DCIS prognostic markers such as those under investigation in Projects 1 and 2. We further aim to (2) perform subgroup analyses to determine how the use of new DCIS prognostic markers affects the benefits and harms of screening for women with varying rates of DCIS (e.g., by age and race), and to (3) evaluate the impact of increasing digital mammography and MRI use on DCIS incidence, overtreatment, and the comparative effectiveness of new DCIS prognostic markers. To address these aims, we will use the University of Wisconsin Breast Cancer Simulation (UWBCS) model to examine comparative effecfiveness at the population level. The UWBCS model, developed as part of the Cancer Inten/ention and Surveillance Modeling Network (CISNET), Is a discrete-event, stochastic simulation model designed to replicate breast cancer incidence and mortality rates in the U.S. population. Data from the Vermont Breast Cancer Surveillance System and other sources, including the Wisconsin In Situ Cohort, will provide essential new inputs to the UWBCS model for this project. Multiple measures of the benefits and harms associated with breast cancer screening will be evaluated. Simulation modeling is ideally suited for comparative effectiveness since numerous screening process variables can be considered simultaneously, data sources can be combined to address gaps, and long term outcomes can be evaluated in a timely manner. Our comparative effectiveness analysis will provide a framework by which new prognostic markers can be evaluated for their potential impacts on the benefits and harms of screening, with a focus on those breast cancer diagnoses with excellent prognosis that are primarily only found through screening. This project will address a critical need to assess whether novel new personalized treatment decision-making approaches tied to emerging screening tests can maximize quality of life by avoiding overtreatment in all populations.
| Sprague, Brian L; Vacek, Pamela M; Herschorn, Sally D et al. (2018) Time-varying risks of second events following a DCIS diagnosis in the population-based Vermont DCIS cohort. Breast Cancer Res Treat : |
| Plevritis, Sylvia K; Munoz, Diego; Kurian, Allison W et al. (2018) Association of Screening and Treatment With Breast Cancer Mortality by Molecular Subtype in US Women, 2000-2012. JAMA 319:154-164 |
| Trentham-Dietz, Amy; Ergun, Mehmet Ali; Alagoz, Oguzhan et al. (2018) Comparative effectiveness of incorporating a hypothetical DCIS prognostic marker into breast cancer screening. Breast Cancer Res Treat 168:229-239 |
| Conklin, Matthew W; Gangnon, Ronald E; Sprague, Brian L et al. (2018) Collagen Alignment as a Predictor of Recurrence after Ductal Carcinoma In Situ. Cancer Epidemiol Biomarkers Prev 27:138-145 |
| Lee, Janie M; Abraham, Linn; Lam, Diana L et al. (2018) Cumulative Risk Distribution for Interval Invasive Second Breast Cancers After Negative Surveillance Mammography. J Clin Oncol 36:2070-2077 |
| van den Broek, Jeroen J; van Ravesteyn, Nicolien T; Mandelblatt, Jeanne S et al. (2018) Comparing CISNET Breast Cancer Models Using the Maximum Clinical Incidence Reduction Methodology. Med Decis Making 38:112S-125S |
| Mandelblatt, Jeanne S; Near, Aimee M; Miglioretti, Diana L et al. (2018) Common Model Inputs Used in CISNET Collaborative Breast Cancer Modeling. Med Decis Making 38:9S-23S |
| McCarthy, Anne Marie; Barlow, William E; Conant, Emily F et al. (2018) Breast Cancer With a Poor Prognosis Diagnosed After Screening Mammography With Negative Results. JAMA Oncol 4:998-1001 |
| Alagoz, Oguzhan; Ergun, Mehmet Ali; Cevik, Mucahit et al. (2018) The University of Wisconsin Breast Cancer Epidemiology Simulation Model: An Update. Med Decis Making 38:99S-111S |
| Onega, T; Zhu, W; Weiss, J E et al. (2018) Preoperative breast MRI and mortality in older women with breast cancer. Breast Cancer Res Treat 170:149-157 |
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