A multimodal AI model for precision prognosis in clear cell renal cell carcinoma: A multicenter study

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.