Personalized prediction of immunotherapy response in lung cancer patients using advanced radiomics and deep learning.

Journal: Cancer imaging : the official publication of the International Cancer Imaging Society
PMID: 39350284

Abstract

BACKGROUND: Lung cancer (LC) is a leading cause of cancer-related mortality, and immunotherapy (IO) has shown promise in treating advanced-stage LC. However, identifying patients likely to benefit from IO and monitoring treatment response remains challenging. This study aims to develop a predictive model for progression-free survival (PFS) in LC patients with IO based on clinical features and advanced imaging biomarkers.

Authors

  • Chien-Yi Liao
    Department of Biomedical Imaging and Radiological Sciences, National Yang Ming Chiao Tung University, Taipei, Taiwan.
  • Yuh-Min Chen
    Department of Chest Medicine, Taipei Veterans General Hospital, Taipei, Taiwan; School of Medicine, National Yang-Ming University, Taipei, Taiwan.
  • Yu-Te Wu
    Department of Biomedical Imaging and Radiological Sciences, National Yang-Ming University, Taipei, Taiwan. ytwu@ym.edu.tw.
  • Heng-Sheng Chao
    Department of Chest Medicine, Taipei Veteran General Hospital, Taipei, Taiwan.
  • Hwa-Yen Chiu
    Center of Sleep Medicine, Taipei Veterans General Hospital, Taipei, Taiwan.
  • Ting-Wei Wang
    Department of Medical Engineering, California Institute of Technology, Pasadena, CA 91125, USA.
  • Jyun-Ru Chen
    Department of Biomedical Imaging and Radiological Sciences, National Yang Ming Chiao Tung University, Taipei, Taiwan.
  • Tsu-Hui Shiao
    Department of Chest Medicine, Taipei Veteran General Hospital, Taipei, Taiwan.
  • Chia-Feng Lu
    Department of Biomedical Imaging and Radiological Sciences, National Yang-Ming University, Taipei, Taiwan.

Keywords

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