Multimodal deep learning model for AI-based functional prognostic risk stratification in patients undergoing radical nephrectomy.
Authors
Luo Y,Wang Y,Zou X,Tang S,Luo X,Zhou Z,Xiong L,Peng Y,Yang C,Wang N,Song H,Zou G,Shi J,Zi X,Gao M,Jia N,Yang P,Yang F,Zheng Z,Wu P,Dong W,Dong P,Guo S,Han H,Zhu S,Chen J,Luo J,Zhai W,Xu Y,Chen J,Fan Y,Qu L,Chen X,Zhuang J,Chen H,Yu C,Zhang X,Liu Q,Zhou F,Zhang S,Luo W,Yao X,Zhang Z
Affiliations (35)
Affiliations (35)
- Department of Urology, Sun Yat-sen University Cancer Center, Guangzhou, China.
- State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, 510060, China.
- The Hong Kong University of Science and Technology, Hong Kong, China.
- Department of Urology, Peking University Third Hospital, Beijing, China.
- Department of Urologic Oncology, Tianjin Medical University Cancer Institute & Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin, 300060, China.
- Department of Urology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450003, China.
- Department of Urology, Peking University First Hospital, No. 8 Xishiku Street, Xicheng District, Beijing, China.
- Institute of Urology, Peking University, No. 8 Xishiku Street, Xicheng District, Beijing, China.
- The National Urological Cancer Center of China, No. 8 Xishiku Street, Xicheng District, Beijing, China.
- Department of Urology, Fujian Medical University Union Hospital, Fuzhou, China.
- Department of Urology, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China.
- Department of Urology, Renji Hospital, School of Medicine in Shanghai Jiao Tong University, 160 Pujian Road, Shanghai, 200127, China.
- Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China.
- Division of Nephrology, State Key Laboratory of Multi-organ Injury Prevention and Treatment, Nanfang Hospital, Southern Medical University, Guangzhou, China.
- Department of Pathology, Sun Yat-Sen University Cancer Center, Guangzhou, China.
- Department of Radiology, The Second Hospital of Tianjin Medical University, Tianjin, 300211, China.
- Department of Urology, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China.
- Department of Urology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China.
- Department of Urology, The Second Hospital of Tianjin Medical University, Tianjin, 300211, China.
- Department of Urologic Surgery, Zhejiang Cancer Hospital, Hangzhou, 310022, China.
- Department of Urology, First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
- Department of Urology, Zhujiang Hospital, Southern Medical University, Guangzhou, China.
- Department of Urology, Shengjing Hospital of China Medical University, Shenyang, 110004, China.
- Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Hong Kong, China.
- Department of Chemical and Biological Engineering, The Hong Kong University of Science and Technology, Hong Kong, China.
- Division of Life Science, The Hong Kong University of Science and Technology, Hong Kong, China.
- HKUST Shenzhen-Hong Kong Collaborative Innovation Research Institute, Futian, Shenzhen, China.
- State Key Laboratory of Nervous System Disorders, The Hong Kong University of Science and Technology, Hong Kong, China.
- Department of Urology, Minimally Invasive Surgery Center, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
- Hong Kong Wiener Intelligence Technologies Limited, Hong Kong, China.
- Department of Urology, Peking University Third Hospital, Beijing, China. [email protected].
- The Hong Kong University of Science and Technology, Hong Kong, China. [email protected].
- Department of Urologic Oncology, Tianjin Medical University Cancer Institute & Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin, 300060, China. [email protected].
- Department of Urology, Sun Yat-sen University Cancer Center, Guangzhou, China. [email protected].
- State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, 510060, China. [email protected].
Abstract
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.
Topics
Journal Article