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<p>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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月17日

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  • Xinyi Zang,
  • Yujia Xia,
  • Haibing Xiao,
  • Haolun Luo,
  • Minggui Si,
  • Naiqiao Hou,
  • Axide Haoni,
  • Tianyi Chen,
  • Ziyi Liu,
  • Xinyuan Pu,
  • Xiangyu Zi,
  • Lijun Xu,
  • Jin Zhu,
  • Zhipeng Xu,
  • Jianning Wang,
  • Zaoyu Wang,
  • Jun Xia,
  • Dengfeng Cao,
  • Yu Yin,
  • Jieying Wang,
  • Xiaorong Wu,
  • Wen Kong,
  • Jiwei Huang,
  • Jin Zhang,
  • Yonghui Chen,
  • Yiran Huang,
  • David Ka-Wai Leung,
  • Jeremy Yuen-Chun Teoh,
  • Keliang Wang,
  • Chaozhao Liang,
  • Junhua Zheng,
  • Zhangsheng Yu,
  • Wei Zhai

摘要

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.