Predicting lymph node metastasis in colorectal cancer using case-level multiple instance learning

Figure 7 Histopathological interpretation of high-attention morphological clusters. Representative image tiles from the six distinct clusters identified by the deep learning model. Each cluster corresponds to a specific histopathological phenotype associated with lymph node metastasis risk. Cluster 1: Poorly differentiated adenocarcinoma, characterized by solid sheets and trabecular growth patterns with a high nuclear-to-cytoplasmic ratio. Cluster 2: Prominent desmoplastic reaction, showing a dense fibroblastic stromal response to invading tumor cells. Cluster 3: Adenocarcinoma with complex glandular architecture, featuring cribriform, fused, or back-to-back glands indicative of high-grade tumor organization. Cluster 4: Micropapillary adenocarcinoma, a high-risk pattern defined by small, cohesive tufts of tumor cells floating in stromal spaces without a true fibrovascular core. Cluster 5: Overt invasion, showcasing clear evidence of lymphovascular invasion and perineural invasion, where tumor cells infiltrate lymphatic channels and surround nerve fibers. Cluster 6: Signet-ring cell carcinoma, a distinct subtype composed of tumor cells containing large intracytoplasmic mucin vacuoles that displace the nucleus to the periphery.