Enhancing Whole Slide Image Classification in Renal Cell Carcinoma via Swin Transformer-Based Multiple Instance Learning.

Renal cell carcinoma (RCC) comprises histologic subtypes with distinct prognosis and treatment implications. This single-cohort study evaluated slide-level weakly supervised subtype classification for clear cell RCC (ccRCC), papillary RCC (pRCC), and chromophobe RCC (chRCC) using 928 diagnostic H&E whole-slide images (WSIs) from 928 patients in TCGA-RCC. We propose Swin-CLAM, a controlled modification of CLAM in which the conventional CNN patch encoder is replaced by an ImageNet-pretrained Swin-Tiny Transformer, while the CLAM-SB bag-level aggregation module is kept unchanged. WSIs were segmented, tiled into non-overlapping 256×256 patches at an effective 20× magnification, encoded offline, and classified using slide-level labels only. In five-fold patient-level cross-validation on TCGA-RCC, Swin-CLAM achieved a macro-averaged AUC of 0.976±0.008, an accuracy of 94.8±1.0%, and a macro-F1 of 0.940±0.012, with the largest gain observed for chRCC. Attention heatmaps and t-SNE plots were used as qualitative, exploratory analyses rather than formal evidence of interpretability. These results suggest that stronger patch-level representation can improve CLAM-based RCC subtype classification under a fixed MIL aggregator. However, the study does not establish clinical readiness, and external validation, calibration, domain-shift analysis, and expert region-level assessment are needed before practical deployment.