Artificial intelligence links CT images to pathologic features and survival outcomes of renal masses

Ying Xiong, Linpeng Yao, Jinglai Lin, Jiaxi Yao, Qi Bai, Yuan Huang, Xue Zhang, Risheng Huang, Run Wang, Kang Wang, Yu Qi, Pingyi Zhu, Haoran Wang, Li Liu, Jianjun Zhou (), Jianming Guo (), Feng Chen (), Chenchen Dai () and Shuo Wang ()
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Ying Xiong: Zhongshan Hospital, Fudan University
Linpeng Yao: The First Affiliated Hospital, Zhejiang University School of Medicine
Jinglai Lin: Zhongshan Hospital (Xiamen), Fudan University
Jiaxi Yao: Zhangye People’s Hospital affiliated to Hexi University
Qi Bai: Zhongshan Hospital, Fudan University
Yuan Huang: Centre for Mathematical Sciences, University of Cambridge
Xue Zhang: the First People’s Hospital of Lianyungang
Risheng Huang: Quanzhou First Hospital, Fujian Medical University
Run Wang: Sir Run Run Shaw Hospital
Kang Wang: Fudan University
Yu Qi: Zhongshan Hospital, Fudan University
Pingyi Zhu: Zhongshan Hospital, Fudan University
Haoran Wang: Fudan University
Li Liu: Zhongshan Hospital, Fudan University
Jianjun Zhou: Zhongshan Hospital (Xiamen), Fudan University
Jianming Guo: Zhongshan Hospital, Fudan University
Feng Chen: The First Affiliated Hospital, Zhejiang University School of Medicine
Chenchen Dai: Zhongshan Hospital, Fudan University
Shuo Wang: Fudan University

Nature Communications, 2025, vol. 16, issue 1, 1-14

Abstract: Abstract Treatment decisions for an incidental renal mass are mostly made with pathologic uncertainty. Improving the diagnosis of benign renal masses and distinguishing aggressive cancers from indolent ones is key to better treatment selection. We analyze 13261 pre-operative computed computed tomography (CT) volumes of 4557 patients. Two multi-phase convolutional neural networks are developed to predict the malignancy and aggressiveness of renal masses. The first diagnostic model designed to predict the malignancy of renal masses achieves area under the curve (AUC) of 0.871 in the prospective test set. This model surpasses the average performance of seven seasoned radiologists. The second diagnostic model differentiating aggressive from indolent tumors has AUC of 0.783 in the prospective test set. Both models outperform corresponding radiomics models and the nephrometry score nomogram. Here we show that the deep learning models can non-invasively predict the likelihood of malignant and aggressive pathology of a renal mass based on preoperative multi-phase CT images.

Date: 2025
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DOI: 10.1038/s41467-025-56784-z

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