Weakly Supervised Learning for Multi-class RCC Classification: Multicenter Validation with Biological Interpretability

Abstract Accurate renal cell carcinoma (RCC) subtyping, especially challenging TFE3-rearranged RCC, is vital for treatment. We developed RCCNET (RCC Neural Enhancement Technology), a weakly supervised deep learning framework integrating a parallel cellular morphometric module for biological interpretability, for four-class classification (clear cell, papillary, chromophobe, TFE3-rearranged). Validated multicentrically on 340 patients (training n=233; external validation n=107), RCCNET achieved macro-average AUCs of 0.989 (training) and 0.966 (validation). For TFE3 RCC, AUC was 0.976 with 92.3% sensitivity, but a 66.7% positive predictive value necessitates molecular confirmation of all positive cases. Model predictions significantly correlated with quantitative morphological features, grounding decisions in histopathology. An economic analysis projected an RCCNET-assisted workflow could reduce costs by 83.2% and time by 45.2%. RCCNET provides an interpretable, cost-effective solution. We propose a confidence-based clinical integration framework, flagging uncertain TFE3 predictions for pathologist review to manage false positives and ensure safe deployment.