[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-381154-105":59,"doc-detail-381154-en":130},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":123,"head_meta":125,"extra_data":127,"updated_unix":129},105,"en","the-artificial-intelligence-and-machine-learning-in-lung-cancer-immunotherapy","The artificial intelligence and machine learning in lung cancer immunotherapy","","Lung cancer patients increasingly benefit from immunotherapy, yet response rates differ widely and reliable patient selection remains challenging. This review explains how artificial intelligence and machine learning model medical data to predict immunotherapy suitability and outcomes. By integrating radiology, pathology, genomics and proteomics, studies estimate PD-L1 expression, tumor mutation burden and tumor microenvironment features, supporting efficacy and side-effect risk prediction. Advances further suggest “digital biopsy” may streamline assessments and improve clinical decision-making.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":35,"@type":76,"position":81},"https://docshare.wps.com/document/healthcare/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/the-artificial-intelligence-and-machine-learning-in-lung-cancer-immunotherapy/381154/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/the-artificial-intelligence-and-machine-learning-in-lung-cancer-immunotherapy/381154.png","ImageObject",300,407,{"name":92,"@type":93},"Miles","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-23","2026-09-24",true,{"@type":102,"interactionType":103,"userInteractionCount":44},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"Why is accurate patient selection important for lung cancer immunotherapy?","Question",{"text":112,"@type":113},"Immunotherapy shows promising results, but objective response rates vary considerably among patients, so identifying patients likely to benefit is essential.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How can AI and ML help predict immunotherapy efficacy?",{"text":117,"@type":113},"AI and ML can model and predict medical information by integrating radiology, pathology, genomics and proteomics data to estimate biomarkers and tumor features linked to response.",{"name":119,"@type":110,"acceptedAnswer":120},"What key biomarkers are discussed for AI/ML prediction in lung cancer immunotherapy?",{"text":121,"@type":113},"The review focuses on predicting PD-L1 expression, tumor mutation burden (TMB), and tumor microenvironment (TME), which influence immunotherapy outcomes.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},381154,1790645894,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":34,"category_name":35,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":44,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":144,"read_time":145},13056703019404,"https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0","Gao et al. Journal of Hematology & Oncology (2023) 16:55 [https://doi.org/10.1186/s13045-023-01456-y](https://doi.org/10.1186/s13045-023-01456-y)  \nJournal of Hematology & Oncology  \n REVIEW Open Access  \nThe artificial intelligence and machine learning in lung cancer immunotherapy  \nQing Gao1, Luyu Yang2, Mingjun Lu1, Renjing Jin1, HuanYe2 and Teng Ma 1*  \nAbstract  \nSince the past decades, more lung cancer patients have been experiencing lasting benefits from immunotherapy.  \nIt 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.  \nIntroduction  \nLung cancer is the deadliest cancer type in China and one of the deadliest cancers in the world [1]. Currently, immunotherapy has shown promising results in lung cancer patients. However, the objective response rates vary considerably among patients. Therefore, it is important to accurately identify lung cancer patients sensitive to immunotherapy.  \nAI has become increasingly relevant to all aspects of human life due to the development of statistical methodology and big data science. AI focuses on simulating human intelligence, thinking, and reasoning models to solve problems, provide decisions and automate labor.  \n*Correspondence:  \nTeng Ma[mateng82913@163.com](mateng82913@163.com)  \n1 Cancer Research Center, Beijing Chest Hospital, Capital Medical University, Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing 101149, China  \n2 Department of Respiratory and Critical Care Medicine, Beijing Chest Hospital, Capital Medical University, Beijing Tuberculosis and Thoracic Tumor Institute, Beijing 101149, China  \nAs a subset of AI, ML is defined as a method of analyzing a large amount of sample data with a target task and then, parsing that data into predictive models and clustering by itself, which is then analyzed by the computer [2]. AI specifically refers to the concept of a \"thinking machine,\" emphasizing the computer’s ability to make independent decisions, while ML refers to a \"learning machine,\" which can complete tasks without explicit programming instructions by inputting data and implementing algorithms to create a computing framework [3]. Deep learning (DL) algorithms, a subset of ML, are AI-driven algorithms that can profoundly impact biomedical research, personalized medicine, and precision medicine [4]. By analyzing genomics, pathomics, imaging, and other biological data with computers, mathematical modeling, and applying it to clinical and scientific research, ML is a method for discovering new things about patients. It has become a hot topic of development these days to cross-fertilize medicine and artificial intelligence [5].  \nPD-L1 and programmed cell death protein 1 (PD- 1)-based lung cancer immunotherapy is the most successful immune checkpoint blockade (ICB) therapy. The  \n© The Author(s) 2023. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original au","cbCaioqt36Mpz5UW","https://ap.wps.com/l/cbCaioqt36Mpz5UW","pdf",5267880,18,"English","# Abstract\n# Introduction\n# AI and ML\n## The conceptions and applications of AI and ML","[{\"question\":\"Why is accurate patient selection important for lung cancer immunotherapy?\",\"answer\":\"Immunotherapy shows promising results, but objective response rates vary considerably among patients, so identifying patients likely to benefit is essential.\"},{\"question\":\"How can AI and ML help predict immunotherapy efficacy?\",\"answer\":\"AI and ML can model and predict medical information by integrating radiology, pathology, genomics and proteomics data to estimate biomarkers and tumor features linked to response.\"},{\"question\":\"What key biomarkers are discussed for AI/ML prediction in lung cancer immunotherapy?\",\"answer\":\"The review focuses on predicting PD-L1 expression, tumor mutation burden (TMB), and tumor microenvironment (TME), which influence immunotherapy outcomes.\"}]","The artificial intelligence and machine learning in lung cancer immunotherapy | PDF",1790243839,45]