Incorporating global-local tissue changes to predict future breast cancer from longitudinal screening mammograms

Abstract

Early detection of breast cancer (BC) through mammography screening is critical for reducing mortality and improving patient outcomes. However, full-population-based, age-driven screening might not lead to optimal resource use and may enlarge screening associated harms in low risk women. Accurate and interpretable BC risk prediction is essential to improve strategies and make screening more personalized. Although recent deep learning models have shown promise in leveraging mammograms for risk stratification, challenges remain in interpretable modeling of temporal changes, efficiently capturing multi-scale risk tissue features from large-scale images, and precise time prediction to enhance clinical interpretability. In this study, we propose T arcking- A ware Brea st C ancer R isk model (TA-BreaCR), a novel framework that integrates local-to-global multiscale longitudinal tissue changes and explicitly models the ordinal relationship of time to BC events, enabling joint prediction of both future BC risk and estimated time to onset. The model is evaluated on two datasets (In-house and EMBED), outperforming existing and state-of-the-art methods in both risk classification and time-to-event prediction tasks. Visualization analysis reveals consistent attention to high-risk regions over time, enhancing interpretability. These results highlight the potential of TA-BreaCR to support individualized BC screening and prevention.