Detection, localization, and staging of breast cancer lymph node metastasis in digital pathology whole slide images...

Accurate detection, localization, and staging of breast cancer lymph node metastases are critical for guiding treatment decisions and predicting patient outcomes. This study presents a selective neighborhood attention-based deep learning framework that combines nuclei-level features with high-level tissue embeddings to detect, annotate and stage breast cancer metastases in whole-slide images (WSIs) of lymph node biopsy specimens precisely. The proposed framework leverages a dual-path feature extractor, incorporating both nuclei segmentation/classification outputs and transformer-based tissue features, alongside a dynamic attention mechanism that selects and emphasizes neighboring patches based on similarity to the target patch. Experimental results on the CAMELYON16 test set demonstrate high performance in patch-level tumor detection, with sensitivity of 96.2 ± 1.5%, precision of 95.3 ± 2.4%, and an F1-score of 95.7 ± 3.1%. The model achieves accurate tumor boundary delineation, evidenced by a Dice score of 90.5 ± 2.0% and a Jaccard index of 82.6 ± 0.8%, along with a lesion-level free-response receiver operating characteristic (FROC) score of 84.6 ± 2.8%. Additionally, the slide-level classification achieves an area under the receiver operating characteristic (ROC) curve (AUC) of 0.96 ± 0.01, highlighting the system's strong diagnostic capability. Out-of-distribution evaluation on the CAMELYON17 dataset confirms the framework's generalizability, yielding an F1-score of 87.0 ± 1.8% at the patch level and an AUC of 0.88 ± 0.03 at the slide level. Furthermore, the proposed model achieves a kappa score of 0.94 ± 0.02 for automated pN-staging at the patient level, indicating near-expert concordance in detecting and classifying the extent of nodal metastasis. Ablation analyses underscore the importance of incorporating nuclei-based features and selective neighborhood attention, with noticeable performance degradation observed when either element is removed. By integrating cellular-level insights with tissue-level contextual information, the proposed framework replicates key aspects of human pathological assessment effectively and shows promise as a decision-support tool in the era of digital pathology.