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Objectives To develop and validate a deep learning-based diagnostic modelincorporating uncertainty estimation so as to facilitate radiologists in thepreoperative differentiation of the pathological subtypes of renal cellcarcinoma (RCC) based on CT images. Methods Data from 668 consecutive patients,pathologically proven RCC, were retrospectively collected from Center 1. Byusing five-fold cross-validation, a deep learning model incorporatinguncertainty estimation was developed to classify RCC subtypes into clear cellRCC (ccRCC), papillary RCC (pRCC), and chromophobe RCC (chRCC). An externalvalidation set of 78 patients from Center 2 further evaluated the model'sperformance. Results In the five-fold cross-validation, the model's area underthe receiver operating characteristic curve (AUC) for the classification ofccRCC, pRCC, and chRCC was 0.868 (95% CI: 0.826-0.923), 0.846 (95% CI:0.812-0.886), and 0.839 (95% CI: 0.802-0.88), respectively. In the externalvalidation set, the AUCs were 0.856 (95% CI: 0.838-0.882), 0.787 (95% CI:0.757-0.818), and 0.793 (95% CI: 0.758-0.831) for ccRCC, pRCC, and chRCC,respectively. Conclusions The developed deep learning model demonstrated robustperformance in predicting the pathological subtypes of RCC, while theincorporated uncertainty emphasized the importance of understanding modelconfidence, which is crucial for assisting clinical decision-making forpatients with renal tumors. Clinical relevance statement Our deep learningapproach, integrated with uncertainty estimation, offers clinicians a dualadvantage: accurate RCC subtype predictions complemented by diagnosticconfidence references, promoting informed decision-making for patients withRCC.

A Robust Deep Learning Method with Uncertainty Estimation for the Pathological Classification of Renal Cell Carcinoma based on CT Images