Interpretable machine learning model integrating contrast-enhanced CT environmental radiomics and clinicopathological features for predicting postoperative recurrence in lung adenocarcinoma: a retrospective pilot study.

Journal: Frontiers in oncology
Published Date: May 23, 2025

Abstract

PURPOSE: This study aims to develop an interpretable predictive model combining contrast-enhanced CT (CECT) radiomics features with clinicopathological parameters to assess 3-year recurrence risk after surgery for lung adenocarcinoma (LA).

Authors

  • Song Lin
    Eye Institute and School of Optometry, Tianjin Medical University Eye Hospital, Tianjin, China.
  • Yanli Niu
    Department of Radiology and Surgery, Xiangyang No.1 People's Hospital, Hubei University of Medicine, Xiangyang, China.
  • Lina Song
    Department of Cardiology, Radiology, and Surgery, Xiangyang No.1 People's Hospital, Hubei University of Medicine, Xiangyang, 441000, China.
  • Yingjian Ye
    Department of Cardiology, Radiology, and Surgery, Xiangyang No.1 People's Hospital, Hubei University of Medicine, Xiangyang, 441000, China.
  • Jinfang Yang
    Department of Cardiology, Radiology, and Surgery, Xiangyang No.1 People's Hospital, Hubei University of Medicine, Xiangyang, 441000, China.
  • Junjie Liu
    Tianjin Key Lab of Indoor Air Environmental Quality Control, School of Environmental Science and Engineering, Tianjin University, Tianjin, 300072, China.
  • Xin Zhou
    School of Mechatronic Engineering, China University of Mining & Technology, Xuzhou 221116, China.
  • Peng An
    Department of Radiology, Xiangyang NO.1 People's Hospital Affiliated to Hubei University of Medicine Xiangyang, Hubei, China.

Keywords

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