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.

翻译：目的 开发并验证一种融合不确定性估计的深度学习诊断模型，以辅助放射科医生基于CT图像术前鉴别肾细胞癌（RCC）的病理亚型。方法 回顾性收集来自中心1的668例经病理证实为RCC的连续患者数据。采用五折交叉验证方法，构建融合不确定性估计的深度学习模型，将RCC亚型分为透明细胞RCC（ccRCC）、乳头状RCC（pRCC）和嫌色细胞RCC（chRCC）。来自中心2的78例患者外部验证集进一步评估模型性能。结果 在五折交叉验证中，模型对ccRCC、pRCC和chRCC分类的受试者工作特征曲线下面积（AUC）分别为0.868（95% CI：0.826-0.923）、0.846（95% CI：0.812-0.886）和0.839（95% CI：0.802-0.88）。在外部验证集中，ccRCC、pRCC和chRCC的AUC分别为0.856（95% CI：0.838-0.882）、0.787（95% CI：0.757-0.818）和0.793（95% CI：0.758-0.831）。结论 所开发的深度学习模型在预测RCC病理亚型方面展现出鲁棒性能，而融合的不确定性估计则强调了理解模型置信度的重要性，这对辅助肾肿瘤患者的临床决策至关重要。临床相关性声明 我们的深度学习方法与不确定性估计相结合，为临床医生提供了双重优势：准确的RCC亚型预测辅以诊断置信度参考，促进为RCC患者做出知情决策。