Interpretable Machine Learning Radiomics Model Predicts 5-year Recurrence-Free Survival in Non-metastatic Clear Cell Renal Cell Carcinoma: A Multicenter and Retrospective Cohort Study.

Journal: Academic radiology
Published Date: Apr 10, 2025

Abstract

RATIONALE AND OBJECTIVES: To develop and validate a computed tomography (CT) radiomics-based interpretable machine learning (ML) model for predicting 5-year recurrence-free survival (RFS) in non-metastatic clear cell renal cell carcinoma (ccRCC).

Authors

  • Jia Zhang
    Department of Geriatrics, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China.
  • Wenxiang Huang
  • Yang Li
    Occupation of Chinese Center for Disease Control and Prevention, Beijing, China.
  • Xuan Zhang
  • Yong Chen
    Department of Urology, Chongqing University Fuling Hospital, Chongqing, China.
  • Shaohao Chen
    Department of Urology, Urology Research Institute, The First Affiliated Hospital of Fujian Medical University, Fujian 350005, China (S.C.).
  • Qiu Ming
    Department of Urology, The People's Hospital of Dazu, Chongqing 402360, China (Q.M.).
  • Qing Jiang
    Department of Urology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China.
  • Yingjie Xv
    Department of Urology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.

Keywords

External Resources

Popular Topics
Recent Journals