Neoadjuvant therapy (NAT) is crucial for locally advanced breast cancer, but post-NAT lymph node assessment is challenging due to histological changes. Current methods like immunohistochemistry (IHC) are labor-intensive and imprecise in distinguishing isolated tumor cells (ITCs), micro-metastases (Micro), and macro-metastases (Macro). We aimed to develop and validate an AI-driven model for precise classification of lymph node metastasis status (negative, ITC, Micro, Macro) in breast cancer patients post-NAT.

We used a weakly supervised Clustering-constrained Attention Multiple Instance Learning (CLAM) framework to analyze 7764 lymph node samples from 7 cohorts. The CLAM model identifies high-diagnostic-value subregions within whole-slide images (WSIs) and generates high-resolution interpretability heatmaps. Performance was evaluated using binary and multi-class metrics, with external validation on diverse datasets. A human-AI comparative analysis was conducted on 24 patient-derived lymph node sections.

The AI model achieved an AUROC of 0.97 (95% CI: 0.962–0.977) in binary classification and an overall accuracy of 0.8436 (95% CI: 0.8282–0.8562) for multi-class differentiation. In the human-AI comparison, the model outperformed junior pathologists, reducing diagnostic discrepancies by 83%.

This study establishes a robust AI model that significantly improves the accuracy and efficiency of post-NAT lymph node metastasis assessment in breast cancer, automating classification and reducing pathologist workload.