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<p>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 &gt;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.</p>

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Multimodal deep learning model for AI-based functional prognostic risk stratification in patients undergoing radical nephrectomy

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月28日

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  • Yunhan Luo,
  • Yatian Wang,
  • Xiangpeng Zou,
  • Shiying Tang,
  • Xin Luo,
  • Zhaohui Zhou,
  • Longbin Xiong,
  • Yulu Peng,
  • Chunsen Yang,
  • Ning Wang,
  • Haitian Song,
  • Gaoyu Zou,
  • Jinhao Shi,
  • Xiangyu Zi,
  • Ming Gao,
  • Nan Jia,
  • Ping Yang,
  • Fengfeng Yang,
  • Zaosong Zheng,
  • Peng Wu,
  • Wen Dong,
  • Pei Dong,
  • Shengjie Guo,
  • Hui Han,
  • Shimiao Zhu,
  • Jinchao Chen,
  • Junhang Luo,
  • Wei Zhai,
  • Yawen Xu,
  • Jianhui Chen,
  • Yu Fan,
  • Le Qu,
  • Xiaonan Chen,
  • Jiaxin Zhuang,
  • Hao Chen,
  • Chunping Yu,
  • Xuepei Zhang,
  • Qifeng Liu,
  • Fangjian Zhou,
  • Shudong Zhang,
  • Wenhan Luo,
  • Xin Yao,
  • Zhiling Zhang

摘要

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.