Subregion-based radiomics analysis for predicting the histological grade of clear cell renal cell carcinoma.

Journal: Frontiers in oncology
Published Date: May 27, 2025

Abstract

PURPOSE: We explored the feasibility of constructing machine learning (ML) models based on subregion radiomics features (RFs) to predict the histological grade of clear cell renal cell carcinoma (ccRCC) and explore the molecular biological mechanisms associated with RFs.

Authors

  • Xue Lv
    Department of Radiology, The Third Affiliated Hospital of Chongqing Medical University, Chongqing, China.
  • Xiao-Mao Dai
    Department of Radiology, The Third Affiliated Hospital of Chongqing Medical University, Chongqing, China.
  • Dai-Quan Zhou
    Department of Radiology, The Third Affiliated Hospital of Chongqing Medical University, Chongqing, China.
  • Na Yu
    National Dental Centre Singapore, Singapore.
  • Yu-Qin Hong
    Department of Radiology, The Third Affiliated Hospital of Chongqing Medical University, Chongqing, China.
  • Qiao Liu
    MOE Key Laboratory of Bioinformatics; Bioinformatics Division and Center for Synthetic & Systems Biology, TNLIST; Department of Automation, Tsinghua University, Beijing 100084, China.

Keywords

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