||2P01CA154292-11 Interpret this number
||University Of California At Davis
||Advancing Equitable Risk-Based Breast Cancer Screening and Surveillance in Community Practice
Breast cancer remains the second leading cause of cancer death in United States women, with racial and ethnic disparities in breast cancer stage at diagnosis, rates of second breast cancers, and mortality. Our Program renewal follows the premise that screening and surveillance will be most effective and equitable when all women have access to high-quality risk assessment and breast imaging, and when screening and surveillance strategies are targeted to clinically meaningful outcomes. Our current Program has advanced the science of risk-based screening and surveillance by: (1) identifying clinical risk factors most predictive of invasive breast cancer for the general population and for racial and ethnic groups; (2) defining and evaluating advanced cancer as a screening outcome; (3) assessing new screening technologies and their use in underserved populations; (4) identifying multilevel factors that influence women’s views of risk-based screening; and (5) identifying breast cancer survivors at high risk of an interval second breast cancer. During the next funding period, we propose three complementary Projects supported by three Cores. Project 1 aims to develop equitable advanced breast cancer risk models that incorporate imaging features, artificial intelligence (AI) algorithms, and clinical factors; and compare the benefits and harms of targeted screening frequency and supplemental MRI based on advanced cancer risk. Project 2 takes a multilevel approach to identify woman-, neighborhood-, and facility-level factors that drive inequities in breast cancer screening performance and outcomes, and to explore whether targeted AI use and other interventions can improve population outcomes with attention to health equity. Project 3 focuses on improving surveillance imaging in breast cancer survivors through equitably predicting women at high risk of a surveillance failure (i.e., interval 2nd breast cancer), improving surveillance performance through AI, and examining social determinants of health as multilevel drivers of surveillance failures and targets for future interventions. The Administrative Core will provide overall scientific leadership and administration for an integrated Program. The Biostatistics and Data Management Core will provide centralized coordination of high-quality data collection, management, analysis, and sharing. The Comparative Effectiveness Core will provide specialized multidisciplinary expertise in decision sciences, risk communication, and qualitative research along with three established Cancer Intervention and Surveillance Modeling Network (CISNET) modeling groups to support the clinical and policy translation of Program findings. The Program leverages the Breast Cancer Surveillance Consortium, an established research network with robust, community-based, prospective data collection from geographically and socio-demographically diverse settings. Program findings will play a critical role in public health efforts to promote equitable, risk-based screening and surveillance and reduce breast cancer disparities.