The artificial intelligence and machine learning in lung cancer immunotherapy
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 ar...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
BMC
2023-05-01
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| Series: | Journal of Hematology & Oncology |
| Online Access: | https://doi.org/10.1186/s13045-023-01456-y |
| _version_ | 1827939744567263232 |
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| author | Qing Gao Luyu Yang Mingjun Lu Renjing Jin Huan Ye Teng Ma |
| author_facet | Qing Gao Luyu Yang Mingjun Lu Renjing Jin Huan Ye Teng Ma |
| author_sort | Qing Gao |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-13T08:59:00Z |
| format | Article |
| id | doaj.art-03b95aa6e84f45a7893d108b6caa8a66 |
| institution | Directory Open Access Journal |
| issn | 1756-8722 |
| language | English |
| last_indexed | 2024-03-13T08:59:00Z |
| publishDate | 2023-05-01 |
| publisher | BMC |
| record_format | Article |
| series | Journal of Hematology & Oncology |
| spelling | doaj.art-03b95aa6e84f45a7893d108b6caa8a662023-05-28T11:25:34ZengBMCJournal of Hematology & Oncology1756-87222023-05-0116111810.1186/s13045-023-01456-yThe artificial intelligence and machine learning in lung cancer immunotherapyQing Gao0Luyu Yang1Mingjun Lu2Renjing Jin3Huan Ye4Teng Ma5Cancer Research Center, Beijing Chest Hospital, Capital Medical University, Beijing Tuberculosis and Thoracic Tumor Research InstituteDepartment of Respiratory and Critical Care Medicine, Beijing Chest Hospital, Capital Medical University, Beijing Tuberculosis and Thoracic Tumor InstituteCancer Research Center, Beijing Chest Hospital, Capital Medical University, Beijing Tuberculosis and Thoracic Tumor Research InstituteCancer Research Center, Beijing Chest Hospital, Capital Medical University, Beijing Tuberculosis and Thoracic Tumor Research InstituteDepartment of Respiratory and Critical Care Medicine, Beijing Chest Hospital, Capital Medical University, Beijing Tuberculosis and Thoracic Tumor InstituteCancer Research Center, Beijing Chest Hospital, Capital Medical University, Beijing Tuberculosis and Thoracic Tumor Research InstituteAbstract 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.https://doi.org/10.1186/s13045-023-01456-y |
| spellingShingle | Qing Gao Luyu Yang Mingjun Lu Renjing Jin Huan Ye Teng Ma The artificial intelligence and machine learning in lung cancer immunotherapy Journal of Hematology & Oncology |
| title | The artificial intelligence and machine learning in lung cancer immunotherapy |
| title_full | The artificial intelligence and machine learning in lung cancer immunotherapy |
| title_fullStr | The artificial intelligence and machine learning in lung cancer immunotherapy |
| title_full_unstemmed | The artificial intelligence and machine learning in lung cancer immunotherapy |
| title_short | The artificial intelligence and machine learning in lung cancer immunotherapy |
| title_sort | artificial intelligence and machine learning in lung cancer immunotherapy |
| url | https://doi.org/10.1186/s13045-023-01456-y |
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