Improved prognostication of overall survival after radiotherapy in lung cancer patients by an interpretable machine learning model integrating lung and tumor radiomics and clinical parameters.

Journal: La Radiologia medica
Published Date: Nov 14, 2024

Abstract

BACKGROUND: Accurate prognostication of overall survival (OS) for non-small cell lung cancer (NSCLC) patients receiving definitive radiotherapy (RT) is crucial for developing personalized treatment strategies. This study aims to construct an interpretable prognostic model that combines radiomic features extracted from normal lung and from primary tumor with clinical parameters. Our model aimed to clarify the complex, nonlinear interactions between these variables and enhance prognostic accuracy.

Authors

  • Tianchen Luo
    Institute of System Science, National University of Singapore, 119260, Singapore.
  • Meng Yan
    School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei, 430074, China.
  • Meng Zhou
    College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, People's Republic of China. biofomeng@hotmail.com.
  • Andre Dekker
    Department of Radiation Oncology (MAASTRO Clinic), Dr. Tanslaan 12, Maastricht, The Netherlands.
  • Ane L Appelt
    Leeds Institute of Medical Research at St James's, University of Leeds, and Leeds Cancer Centre, St James's University Hospital, Leeds, UK.
  • Yongling Ji
    Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang, 310022, China.
  • Ji Zhu
    Department of Statistics, University of Michigan, Ann Arbor, Michigan.
  • Dirk de Ruysscher
    a Department of Radiation Oncology (MAASTRO), GROW - School for Oncology and Developmental Biology , Maastricht University Medical Centre , Maastricht , The Netherlands.
  • Leonard Wee
    Maastricht University Medical Centre, Netherlands.
  • Lujun Zhao
    Department of Radiation Oncology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, Tianjin's Clinical Research Center for Cancer, 300060, China. Electronic address: zhaolujun@tjmuch.com.
  • Zhen Zhang
    School of Pharmacy, Jining Medical University, Rizhao, Shandong, China.

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