Automatic machine learning accurately predicts the efficacy of immunotherapy for patients with inoperable advanced non-small cell lung cancer using a computed tomography-based radiomics model.

Journal: Diagnostic and interventional radiology (Ankara, Turkey)
PMID: 39817633

Abstract

PURPOSE: Patients with advanced non-small cell lung cancer (NSCLC) have varying responses to immunotherapy, but there are no reliable, accepted biomarkers to accurately predict its therapeutic efficacy. The present study aimed to construct individualized models through automatic machine learning (autoML) to predict the efficacy of immunotherapy in patients with inoperable advanced NSCLC.

Authors

  • Siyun Lin
    Huadong Hospital, Fudan University, Department of Thoracic Surgery, Shanghai, China.
  • Zhuangxuan Ma
    Department of Radiology, Huadong Hospital, Fudan University, Shanghai, China.
  • Yuanshan Yao
    Shanghai Chest Hospital, Shanghai JiaoTong University School of Medicine, Department of Thoracic Surgery, Shanghai, China.
  • Hou Huang
    Shanghai Key Laboratory of Clinical Geriatric Medicine, Shanghai, China.
  • Wufei Chen
    Department of Radiology, Huadong Hospital Affiliated to Fudan University, Shanghai, China.
  • Dongfang Tang
    Huadong Hospital, Fudan University, Department of Thoracic Surgery, Shanghai, China.
  • Wen Gao

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