The comparative effectiveness of incorporating novel DClS prognostic markers into

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PROJECT SUMMARY Prior to widespread screening mammography, ductal carcinoma in situ (DCIS) was a rare diagnosis. Now almost 20% of breast cancer diagnoses, and nearly 30% of screen-detected breast cancers, are DCIS. Since limitations in our understanding of the natural history of DCIS prevent identification of which 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 effectiveness at the population level. The UWBCS model, developed as part of the Cancer Intervention 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.