ArticleBMC medicine2026
Artificial intelligence empowers full-stack histopathological diagnosis and prognosis of renal cell tumor: a multi-center study with external validation.
Article in BMC medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundThe 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.
methodThis 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.
resultsThe 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).
conclusionsWe 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.
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