The artificial intelligence and machine learning in lung cancer immunotherapy.

Journal: Journal of hematology & oncology
Published Date: May 24, 2023

Abstract

Since the past decades, more lung cancer patients have been experiencing lasting benefits from immunotherapy. It is imperative to accurately and intelligently select appropriate patients for immunotherapy or predict the immunotherapy efficacy. In recent years, machine learning (ML)-based artificial intelligence (AI) was developed in the area of medical-industrial convergence. AI can help model and predict medical information. A growing number of studies have combined radiology, pathology, genomics, proteomics data in order to predict the expression levels of programmed death-ligand 1 (PD-L1), tumor mutation burden (TMB) and tumor microenvironment (TME) in cancer patients or predict the likelihood of immunotherapy benefits and side effects. Finally, with the advancement of AI and ML, it is believed that "digital biopsy" can replace the traditional single assessment method to benefit more cancer patients and help clinical decision-making in the future. In this review, the applications of AI in PD-L1/TMB prediction, TME prediction and lung cancer immunotherapy are discussed.

Authors

  • Qing Gao
    College of Guangling, Yangzhou University, Yangzhou University, Yangzhou 225002, PR China (X.Z.) College of Chemistry & Chemical Engineering, Yangzhou University, Yangzhou University, Yangzhou 225002, PR China (Q.L., Q.G., W.L., X.Z.).
  • Luyu Yang
    Department of Respiratory and Critical Care Medicine, Beijing Chest Hospital, Capital Medical University, Beijing Tuberculosis and Thoracic Tumor Institute, Beijing, 101149, China.
  • Mingjun Lu
    Cancer Research Center, Beijing Chest Hospital, Capital Medical University, Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing, 101149, China.
  • Renjing Jin
    Cancer Research Center, Beijing Chest Hospital, Capital Medical University, Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing, 101149, China.
  • Huan Ye
    Institute of Biomaterials and Medical Devices, Southeast University, Suzhou, Jiangsu, 215163, China.
  • Teng Ma
    Honghui Hospital, Xi'an Jiaotong University, Xi'an, China.

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