SideAge-RiskNet: Predicted Mammographic-Age Features for Future Breast Cancer Risk and Laterality Prediction

Abstract

Whether breast-specific mammographic aging provides information beyond chronological age for future cancer prediction remains unclear. We propose SideAge-RiskNet, a framework integrating predicted mammographic-age features with breast-specific representations for multi-year breast cancer risk and conditional laterality prediction. A side-aware DenseNet121 trained on never-cancer reference examinations estimates left and right breast ages. Age-adjusted residuals and bilateral age descriptors are subsequently fused with corresponding image representations, while clinical variables contribute through an image–clinical risk branch. The framework jointly predicts cumulative cancer risk over 1–5 years and the subsequently affected breast among unilateral cancer cases. Across 4,480 EMBED and 248 external examinations, pre-cancer scans demonstrated pronounced bilateral age asymmetry. SideAge-RiskNet established state-of-the-art performance in 1–5-year risk assessment over Mirai and AsymMirai with mean risk AUROCs of 81.73% on EMBED and 72.83% on CSMC. Furthermore, it enabled conditional cancer laterality prediction, achieving AUROCs of 73.26% and 82.53%, respectively. This demonstrates the critical utility of bilateral aging phenotypes in localized breast cancer risk surveillance.