Construction and validation of deep learning-based pathomics signature model for predicting postoperative recurrence of patients with clear cell renal cell carcinoma.

This study developed an attention-based multi-instance deep convolutional neural network (CRPNet) for the early prediction of postoperative recurrence in clear cell renal cell carcinoma (ccRCC). The model was trained on 183 whole slide images (WSIs) from ccRCC patients and validated on an internal cohort of 75 WSIs. Its prognostic performance was evaluated using Kaplan-Meier analysis, AUC, accuracy, precision, recall, false positive/negative rates (FPR/FNR), C-index, and hazard ratio (HR), and was compared against established tools including the UISS, SSIGN, and Karakiewicz nomograms. Results demonstrated that CRPNet-stratified high-risk groups had significantly poorer prognosis in both training and validation sets (P < 0.001), with consistency across subgroups based on T-stage, WHO/ISUP grade, and necrosis. In the training cohort, CRPNet achieved an AUC of 0.994 (95% CI: 0.974-1.000), accuracy of 97.70%, precision/recall of 95.56%, FPR of 1.55%, and FNR of 4.45%. In the validation cohort, it maintained an AUC of 0.879 (95% CI: 0.783-0.943), accuracy of 88.00%, precision of 85.71%, recall of 63.16%, FPR of 0%, and FNR of 36.84%, outperforming all comparator models. CRPNet also yielded a superior C-index compared to clinical parameters and traditional nomograms, and exhibited the highest HR (12.078, 95% CI: 1.611-90.539). In conclusion, CRPNet surpasses conventional prognostic models in recurrence prediction accuracy, AUC, precision, C-index, and risk stratification, while demonstrating lower FPR and FNR, thereby offering improved prognostication for metastatic ccRCC.