Multimodal data deep learning method for predicting symptomatic pneumonitis caused by lung cancer radiotherapy combined with immunotherapy.

Journal: Frontiers in immunology
PMID: 39845959

Abstract

OBJECTIVES: The pairing of immunotherapy and radiotherapy in the treatment of locally advanced nonsmall cell lung cancer (NSCLC) has shown promise. By combining radiotherapy with immunotherapy, the synergistic effects of these modalities not only bolster antitumor efficacy but also exacerbate lung injury. Consequently, developing a model capable of accurately predicting radiotherapy- and immunotherapy-related pneumonitis in lung cancer patients is a pressing need. Depth image features extracted from deep learning, combined with radiomics and clinical characteristics, were used to create a deep learning model. This model was developed to forecast symptomatic pneumonitis (SP) (≥Grade 2) in lung cancer patients undergoing thoracic radiotherapy in combination with immunotherapy.

Authors

  • Mingyu Yang
    Department of Orthopedics, Orthopedic Center of Chinese PLA, Southwest Hospital, Third Military Medical University, Chongqing, 400038, P.R.China.
  • Jianli Ma
    Harbin Medical University Cancer Hospital, Harbin, China.
  • Chengcheng Zhang
    Department of Computer Science and Technology, Harbin Institute of Technology, Harbin, China.
  • Liming Zhang
    Department of Gastroenterology, Peking University People's Hospital, Beijing, China.
  • Jianyu Xu
    Harbin Medical University Cancer Hospital, Harbin, China.
  • Shilong Liu
    Harbin Medical University Cancer Hospital, Harbin, China.
  • Jian Li
    Fujian Key Laboratory of Traditional Chinese Veterinary Medicine and Animal Health, College of Animal Science, Fujian Agriculture and Forestry University, Fuzhou, China.
  • Jiabin Han
    School of Business Administration, Liaoning Technical University, Huludao, China.
  • Songliu Hu
    Harbin Medical University Cancer Hospital, Harbin, China.

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