Nature communications · 2026
Multimodal deep learning model for AI-based functional prognostic risk stratification in patients undergoing radical nephrectomy.
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Luo Yunhan, Wang Yatian, Zou Xiangpeng, Tang Shiying, Luo Xin, Zhou Zhaohui, Xiong Longbin, Peng Yulu, Yang Chunsen, Wang Ning, Song Haitian, Zou Gaoyu, Shi Jinhao, Zi Xiangyu, Gao Ming, Jia Nan, Yang Ping, Yang Fengfeng, Zheng Zaosong, Wu Peng, Dong Wen, Dong Pei, Guo Shengjie, Han Hui, Zhu Shimiao, Chen Jinchao, Luo Junhang, Zhai Wei, Xu Yawen, Chen Jianhui, Fan Yu, Qu Le, Chen Xiaonan, Zhuang Jiaxin, Chen Hao, Yu Chunping, Zhang Xuepei, Liu Qifeng, Zhou Fangjian, Zhang Shudong, Luo Wenhan, Yao Xin, Zhang Zhiling
Аннотация
Making the decision between technically challenging partial nephrectomy (PN) and radical nephrectomy (RN) in patients with complex renal cell carcinoma (RCC) remains a significant challenge for urologists. Rapid glomerular filtration rate (GFR) decline (annual decline >3 mL/min/1.73 m²) after RN is considered an abnormal renal function state, and if this risk can be predicted preoperatively, PN may be pursued even when technically demanding. We retrospectively analyze contrast-enhanced computed tomography images and clinical data from 1621 patients across multiple centers. A multimodal deep learning model is developed to predict rapid GFR decline after RN. The model achieves an area under the curve of 0.788-0.873 in external test sets. It stratifies patients into high- and low-risk groups with significantly different risks of chronic kidney disease progression. Here we show that the model demonstrates potential for assisting treatment decisions in patients with complex RCC for whom PN is challenging but feasible.