Fig. 1

Deep learning pipeline for predicting recurrence risk following adjuvant C-ET in HR+/HER2- EBC. (A) WSIs with manually annotated tumor regions were divided into nonoverlapping 224 × 224 pixel tiles at 10× magnification. (B) Features were extracted from each tile using a self-supervised feature encoder CTransPath, resulting in 768-dimensional vector features. (C) Patient-level recurrence risk prediction was achieved by aggregating all feature vectors from each WSI into a bag using the multiple branch attention mechanism-based ACMIL, resulting in the generation of the final risk score output. (D) The model was trained to classify prognostic outcomes for WSIs, assigning attention scores to each tile. Attention heatmaps showed the attention scores assigned by the model to each tile for predicting patient recurrence, with blue indicating low attention and red indicating high attention. (E) Patients were categorized into high risk and low risk groups based on the median risk score from the training set, which served as the threshold for classification. These groups were then utilized for subsequent survival analysis. C-ET, chemo-endocrine therapy; EBC, early breast cancer; WSIs, whole slide images