Breast cancer recurrence risk prediction based on MIL

Predicting breast cancer recurrence risk is a critical clinical challenge. This study investigates the potential of computational pathology to stratify patients using deep learning on routine hematoxylin and eosin (H&E) stained whole-slide images (WSIs). We developed and compared three multiple instance learning (MIL) frameworks-CLAM-SB, ABMIL, and ConvNeXt-MIL-XGBoost-on an in-house dataset of 210 patient cases. The models were trained to predict 5-year recurrence risk, categorized into three tiers (low, medium, high), with ground truth labels established by the 21-gene recurrence score. Features were extracted using the UNI and CONCH pre-trained models. In a 5-fold cross-validation, the modified CLAM-SB model achieved the strongest performance, achieving a mean area under the curve (AUC) of 0.836 and a classification accuracy of 76.2%. Our findings demonstrate the feasibility of using deep learning on standard histology slides for automated, genomics-correlated risk stratification, highlighting a promising pathway toward rapid and cost-effective clinical decision support.