PROJECT SUMMARY – Project 3 The incidence of second breast cancer in survivors is seven times higher than first breast cancer incidence in women without this history; these cancers are associated with increased morbidity and breast-cancer specific mortality. Annual surveillance mammography is recommended for survivors for early detection of a second breast cancer to reduce morbidity and mortality. However, surveillance mammography is imperfect. Survivors have 4x higher surveillance failure rates (cancers diagnosed within 12 months of a negative mammogram) than women without a history of breast cancer. Project 3’s overarching goal is to reduce surveillance failures in breast cancer survivors through equitably predicting women at high risk of a surveillance failure (i.e., interval 2nd breast cancer), improving cancer detection through artificial intelligence (AI), and examining social determinants of health as multilevel drivers of surveillance failures and targets for future interventions. Having an accurate surveillance failure risk prediction tool across broad populations of survivors would support a future of targeted surveillance imaging. Understanding whether predictive performance differs by race is critical to validating an accurate risk prediction model. External validation is also key to ensuring the generalizability of the model. Additionally, acceptability of future risk-based surveillance strategies for referring physicians and women is important to consider and has not yet been evaluated. In the current P01, we developed the first risk model of surveillance failures that can be applied to individual survivors. In the next funding cycle, our Aim 1 will address feasibility of adopting the BCSC 5-year surveillance failure risk prediction model by assessing performance equity by race group external generalizability, and acceptability. Our Aim 2 will evaluate whether AI algorithms improve sensitivity at a fixed specificity equivalent to that of BCSC radiologists. Commercially available AI algorithms for mammography interpretation are already being implemented. However, these algorithms were developed in screening populations and have not yet been validated in survivors. Lastly, our Aim 3 will identify the relative contribution of selected multilevel social determinants of health that contribute to surveillance failures. Multiple studies have shown social determinants of health including income, education, housing, and race are associated with higher rates of second breast cancers and mortality. No prior study has evaluated how multilevel social determinants of health that may be amenable to intervention are associated with interval second breast cancers in survivors. Understanding the relative contribution of multilevel factors to surveillance failures can be used to guide future targeted interventions to decrease surveillance failures. Together, this Project will generate evidence to improve surveillance through a multipronged effort to ...