A multimodal AI model for precision prognosis in clear cell renal cell carcinoma: A multicenter study.
Authors
Affiliations (21)
Affiliations (21)
- Department of Urology, Renji Hospital, School of Medicine in Shanghai Jiao Tong University, Shanghai, China.
- Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China.
- SJTU-Yale Joint Center for Biostatistics and Data Science, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China.
- Department of Urology, The First Affiliated Hospital of Anhui Medical University, Anhui Medical University, Hefei, Anhui, China.
- Department of Urology, Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of Medicine, Kaohsiung, Taiwan.
- Department of Urology, The Fourth Affiliated Hospital of Harbin Medical University, Heilongjiang Key Laboratory of Scientific Research in Urology, Harbin, China.
- Department of Urology, The Second Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China.
- Department of Urology, The First Affiliated Hospital of Shandong First Medical University, Jinan, China.
- Department of Pathology, Renji Hospital, School of Medicine in Shanghai Jiao Tong University, Shanghai, China.
- Department of Pathology, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China.
- Department of Nephrology, Molecular Cell Laboratory for Kidney Disease, Shanghai Peritoneal Dialysis Research Center, Ren Ji Hospital, Uremia Diagnosis and Treatment Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
- Clinical Research Center, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
- S.H. Ho Urology Centre, Department of Surgery, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, China.
- Department of Urology, The Fourth Affiliated Hospital of Harbin Medical University, Heilongjiang Key Laboratory of Scientific Research in Urology, Harbin, China. [email protected].
- Department of Urology, The First Affiliated Hospital of Anhui Medical University, Anhui Medical University, Hefei, Anhui, China. [email protected].
- Department of Urology, Renji Hospital, School of Medicine in Shanghai Jiao Tong University, Shanghai, China. [email protected].
- Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China. [email protected].
- SJTU-Yale Joint Center for Biostatistics and Data Science, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China. [email protected].
- Clinical Research Institute, Shanghai Jiao Tong University School of Medicine, Shanghai, China. [email protected].
- Department of Urology, Renji Hospital, School of Medicine in Shanghai Jiao Tong University, Shanghai, China. [email protected].
- Shanghai Immune Therapy Institute State, Key Laboratory of Oncogenes and Related Genes, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China. [email protected].
Abstract
Patients with clear cell renal cell carcinoma (ccRCC) face a high risk of recurrence after surgery, but existing clinical tools based on clinicopathological factors or costly molecular profiling often lack precision and clinical feasibility. We developed the multimodal predictive recurrence score (MPRS), a multimodal prognostic model using clinical features, CT images, and histopathological whole-slide images (WSIs) from 1648 patients across six centers and the TCGA database. MPRS outperformed unimodal models and clinical tools (Leibovich and UISS scores, KEYNOTE-564 risk classification), achieving C-index values of 0.886 and 0.838 in the internal and external validation cohorts, respectively. Importantly, MPRS correctly reclassified 83.3% (50/60) of KEYNOTE-564-defined low-risk recurrence patients as high-risk, avoiding inadequate adjuvant therapy, while reclassifying 57.7% (15/26) of KEYNOTE-564-defined intermediate/high-risk non-recurrence patients as low-risk, preventing excessive adjuvant therapy. By leveraging routinely available data, MPRS provides a cost-effective and accurate approach for recurrence risk stratification, optimizing personalized ccRCC management and therapeutic decision-making.