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<p>High Ki-67 expression in clear cell renal cell carcinoma (ccRCC) predicts poor prognosis but requires postoperative assessment. In a multicenter retrospective study of 627 ccRCC patients, we developed and validated a multi-modal model, integrating multi-scale radiomics and deep learning (DL) features, for non-invasive, preoperative Ki-67 prediction. Using ensemble machine learning algorithms, unimodal models were constructed from preoperative CT-derived multi-scale radiomics (intratumoral, habitat, peritumoral), 2D/3D DL, and clinical features. A stacking strategy was used to fuse the best-performing unimodal models. The fusion model demonstrated superior performance, achieving an Area Under the Curve (AUC) of 0.756 (95% CI 0.692–0.821) in the external test set. The model demonstrated excellent calibration and the highest clinical net benefit, with habitat radiomics identified as the dominant predictive component via SHAP analysis. Our validated multi-modal model significantly improves the preoperative prediction of Ki-67 expression compared to unimodal approaches, offering a promising tool to guide individualized surgical and surveillance strategies.</p>

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Integration of multi-scale radiomics and deep learning for Ki-67 prediction in clear cell renal carcinoma

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月6日

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  • Jinshuai Li,
  • Dingyang Lv,
  • Zhiwei Guo,
  • Huiyu Zhou,
  • Xiaomei Yao,
  • Yi Rong,
  • Xiaodong Bian,
  • Lei Pang,
  • Tiantian Zhao,
  • Ying Qiao,
  • Weibing Shuang

摘要

High Ki-67 expression in clear cell renal cell carcinoma (ccRCC) predicts poor prognosis but requires postoperative assessment. In a multicenter retrospective study of 627 ccRCC patients, we developed and validated a multi-modal model, integrating multi-scale radiomics and deep learning (DL) features, for non-invasive, preoperative Ki-67 prediction. Using ensemble machine learning algorithms, unimodal models were constructed from preoperative CT-derived multi-scale radiomics (intratumoral, habitat, peritumoral), 2D/3D DL, and clinical features. A stacking strategy was used to fuse the best-performing unimodal models. The fusion model demonstrated superior performance, achieving an Area Under the Curve (AUC) of 0.756 (95% CI 0.692–0.821) in the external test set. The model demonstrated excellent calibration and the highest clinical net benefit, with habitat radiomics identified as the dominant predictive component via SHAP analysis. Our validated multi-modal model significantly improves the preoperative prediction of Ki-67 expression compared to unimodal approaches, offering a promising tool to guide individualized surgical and surveillance strategies.