{"title":"Cancer immunotherapy efficacy and machine learning.","authors":"Yuting Fang, Xiaozhong Chen, Caineng Cao","doi":"10.1080/14737140.2024.2311684","DOIUrl":null,"url":null,"abstract":"<p><strong>Introduction: </strong>Immunotherapy is one of the major breakthroughs in the treatment of cancer, and it has become a powerful clinical strategy, however, not all patients respond to immune checkpoint blockade and other immunotherapy strategies. Applying machine learning (ML) techniques to predict the efficacy of cancer immunotherapy is useful for clinical decision-making.</p><p><strong>Areas covered: </strong>Applying ML including deep learning (DL) in radiomics, pathomics, tumor microenvironment (TME) and immune-related genes analysis to predict immunotherapy efficacy. The studies in this review were searched from PubMed and ClinicalTrials.gov (January 2023).</p><p><strong>Expert opinion: </strong>An increasing number of studies indicate that ML has been applied to various aspects of oncology research, with the potential to provide more effective individualized immunotherapy strategies and enhance treatment decisions. With advances in ML technology, more efficient methods of predicting the efficacy of immunotherapy may become available in the future.</p>","PeriodicalId":12099,"journal":{"name":"Expert Review of Anticancer Therapy","volume":null,"pages":null},"PeriodicalIF":2.9000,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Expert Review of Anticancer Therapy","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1080/14737140.2024.2311684","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/2/12 0:00:00","PubModel":"Epub","JCR":"Q2","JCRName":"ONCOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Introduction: Immunotherapy is one of the major breakthroughs in the treatment of cancer, and it has become a powerful clinical strategy, however, not all patients respond to immune checkpoint blockade and other immunotherapy strategies. Applying machine learning (ML) techniques to predict the efficacy of cancer immunotherapy is useful for clinical decision-making.
Areas covered: Applying ML including deep learning (DL) in radiomics, pathomics, tumor microenvironment (TME) and immune-related genes analysis to predict immunotherapy efficacy. The studies in this review were searched from PubMed and ClinicalTrials.gov (January 2023).
Expert opinion: An increasing number of studies indicate that ML has been applied to various aspects of oncology research, with the potential to provide more effective individualized immunotherapy strategies and enhance treatment decisions. With advances in ML technology, more efficient methods of predicting the efficacy of immunotherapy may become available in the future.
期刊介绍：
Expert Review of Anticancer Therapy (ISSN 1473-7140) provides expert appraisal and commentary on the major trends in cancer care and highlights the performance of new therapeutic and diagnostic approaches.
Coverage includes tumor management, novel medicines, anticancer agents and chemotherapy, biological therapy, cancer vaccines, therapeutic indications, biomarkers and diagnostics, and treatment guidelines. All articles are subject to rigorous peer-review, and the journal makes an essential contribution to decision-making in cancer care.
Comprehensive coverage in each review is complemented by the unique Expert Review format and includes the following sections:
Expert Opinion - a personal view of the data presented in the article, a discussion on the developments that are likely to be important in the future, and the avenues of research likely to become exciting as further studies yield more detailed results
Article Highlights – an executive summary of the author’s most critical points.
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