Interpretable CT Radiomics-based Machine Learning Model for Preoperative Prediction of Ki-67 Expression in Clear Cell Renal Cell Carcinoma.

Journal: Academic radiology
PMID: 39788813

Abstract

RATIONALE AND OBJECTIVES: To develop and externally validate interpretable CT radiomics-based machine learning (ML) models for preoperative Ki-67 expression prediction in clear cell renal cell carcinoma (ccRCC).

Authors

  • Yingjie Xv
    Department of Urology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
  • Bangxin Xiao
    Department of Urology, The First Affiliated Hospital of Chongqing Medical University.
  • Zongjie Wei
    Department of Urology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
  • Youde Cao
    Department of Basic Medical Sciences, University of Chongqing Medical University, Chongqing, China (Y.C.).
  • Qing Jiang
    Department of Urology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China.
  • Feng Li
    Department of General Surgery, Shanghai Traditional Chinese Medicine (TCM)-INTEGRATED Hospital of Shanghai University of Traditional Chinese Medicine, Shanghai, China.
  • Fajin Lv
    Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
  • Canjie Peng
    Department of Urology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
  • Xingshu Li
    Department of Obstetrics, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China (X.L.).
  • Mingzhao Xiao
    Department of Urology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.

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