  Objectives To develop and validate a deep learning-based diagnostic model
incorporating uncertainty estimation so as to facilitate radiologists in the
preoperative differentiation of the pathological subtypes of renal cell
carcinoma (RCC) based on CT images. Methods Data from 668 consecutive patients,
pathologically proven RCC, were retrospectively collected from Center 1. By
using five-fold cross-validation, a deep learning model incorporating
uncertainty estimation was developed to classify RCC subtypes into clear cell
RCC (ccRCC), papillary RCC (pRCC), and chromophobe RCC (chRCC). An external
validation set of 78 patients from Center 2 further evaluated the model's
performance. Results In the five-fold cross-validation, the model's area under
the receiver operating characteristic curve (AUC) for the classification of
ccRCC, 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 external
validation 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 robust
performance in predicting the pathological subtypes of RCC, while the
incorporated uncertainty emphasized the importance of understanding model
confidence, which is crucial for assisting clinical decision-making for
patients with renal tumors. Clinical relevance statement Our deep learning
approach, integrated with uncertainty estimation, offers clinicians a dual
advantage: accurate RCC subtype predictions complemented by diagnostic
confidence references, promoting informed decision-making for patients with
RCC.
