Artificial intelligence empowers full-stack histopathological diagnosis and prognosis of renal cell tumor: a multi-center study with external validation.

Abstract

The rapid advancement of digital pathology has opened unprecedented opportunities for intelligent diagnosis in renal cell tumor. However, there remains a significant gap in the availability of reliable deep learning models capable of comprehensive kidney cancer detection, classification, grading, and survival prediction. This study retrospectively analyzed 11,135 whole-slide images (WSIs) from 7033 patients with renal tumor, sourced from four medical centers and two public cohorts. Histopathological representations were extracted using the foundation model Prov-GigaPath. A full-stack renal tumor diagnosis and prognosis framework was developed by combining fully supervised learning and weakly supervised multi-instance learning to enable both regional characterization and patient-level inference. The deep learning model demonstrated high accuracy in identifying normal tissue (AUC = 0.990), tumor tissue (AUC = 0.982), necrosis tissue (AUC = 0.994), sarcomatoid differentiation (AUC = 0.967), and pseudocapsule tissue (AUC = 0.990) across various pathological types of renal cell tumor. For nine major subtypes of renal cell tumor, classification AUC reached 0.956-0.998 across multi-center validation cohorts. WHO/ISUP nuclear grade prediction for clear cell renal cell carcinoma (ccRCC) and papillary renal cell carcinoma (pRCC) achieved an AUC of 0.867. A whole-slide-derived pan-renal cell tumor pathological risk score independently predicted overall survival and significantly outperformed WHO/ISUP grading in prognostic stratification (p < 0.001). We developed and validated a comprehensive AI framework integrating tissue-region detection, renal tumor subtype classification, nuclear grading, and survival prediction. These findings support its potential as a decision-support tool for renal tumor pathology, while prospective workflow-based studies are warranted to determine its clinical utility and impact on pathologist performance.