[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117770-en":3,"doc-seo-117770-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},117770,1099513958762,"Logic","https://ap-avatar.wpscdn.com/avatar/1000023916a998db790?x-image-process=image/resize,m_fixed,w_180,h_180&k=1784791008015729253",8,"Research & Report","Machine learning in non-small cell lung cancer radiotherapy - A bibliometric analysis","Machine learning has rapidly advanced in non-small cell lung cancer (NSCLC) radiotherapy, yet the overall research trend and hotspot areas remain insufficiently clarified. This bibliometric analysis examines published studies indexed in the Web of Science Core Collection to map publication volume, leading journals, institutions, and contributing countries. It identifies “radiomics” as the most frequent keyword and shows that machine learning mainly supports medical image analysis for treatment planning and for predicting treatment effects and adverse events. The findings highlight emerging research directions for future investigations.","TYPE Original Research PUBLISHED 17 March 2023  \nDOI 10.3389/fonc.2023.1082423  \nOPEN ACCESS  \nEDITED BY  \nTonghe Wang,  \nMemorial Sloan Kettering Cancer Center, United States  \nREVIEWED BY  \nLi-Sheng Geng, Beihang University, China Jian Bin Li,  \nShandong Cancer Hospital, China  \n*CORRESPONDENCE Zhengwen Cai  \n [99cczzww@sina.com](99cczzww@sina.com)[ ](99cczzww@sina.com)Wenqi Liu  \n [liuwenqigx@163.com](liuwenqigx@163.com)  \n†These authors have contributed equally to this work and share first authorship  \nSPECIALTY SECTION  \nThis article was submitted to Radiation Oncology, a section of the journal Frontiers in Oncology  \nRECEIVED 28 October 2022  \nACCEPTED 20 February 2023  \nPUBLISHED 17 March 2023  \nCITATION  \nZhang J, Zhu H, Wang J, Chen Y, Li Y, Chen X, Chen M, Cai Z and Liu W (2023) Machine learning in non-small cell lung cancer radiotherapy: A bibliometric analysis.  \nFront. Oncol. 13:1082423 .  \ndoi: 10.3389/fonc.2023.1082423  \nCOPYRIGHT  \n© 2023 Zhang, Zhu, Wang, Chen, Li, Chen, Chen, Cai and Liu. This is an open-access article distributed under the terms of the  \nCreative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nMachine learning in non-small cell lung cancer radiotherapy:  \nA bibliometric analysis  \nJiaming Zhang 1†, Huijun Zhu 1†, Jue Wang 1, Yulu Chen 1, Yihe Li 1, Xinyu Chen 1, Menghua Chen 1, Zhengwen Cai 2* and Wenqi Liu 1*  \n1 Department of Radiation Oncology, The Second Affiliated Hospital of Guangxi Medical University, Nanning, China, 2 Department of Oncology, The Second Affiliated Hospital of Guangxi Medical University, Nanning, China  \nBackground: Machine learning is now well-developed in non-small cell lung cancer (NSCLC) radiotherapy. But the research trend and hotspots are still unclear. To investigate the progress in machine learning in radiotherapy NSCLC, we performed a bibliometric analysis of associated research and discuss the current research hotspots and potential hot areas in the future.  \nMethods: The involved researches were obtained from the Web of Science Core Collection database (WoSCC) . We used R-studio software, the Bibliometrix package and VOSviewer (Version 1.6.18) software to perform bibliometric analysis.  \nResults: We found 197 publications about machine learning in radiotherapy for NSCLC in the WoSCC, and the journal Medical Physics contributed the most articles. The University of Texas MD Anderson Cancer Center was the most frequent publishing institution, and the United States contributed most of the publications. In our bibliometric analysis, “radiomics” was the most frequent keyword, and we found that machine learning is mainly applied to analyze medical images in the radiotherapy of NSCLC.  \nResults: The research we identified about machine learning in NSCLC radiotherapy was mainly related to the radiotherapy planning of NSCLC and the prediction of treatment effects and adverse events in NSCLC patients who were under radiotherapy. Our research has added new insights into machine learning in NSCLC radiotherapy and could help researchers better identify hot research areas in the future.  \nKEYWORDS  \nnon-small cell lung cancer, radiotherapy, machine learning, computer science, bibliometric analysis  \nFrontiers in Oncology 01 [frontiersin.org](frontiersin.org)  \n1 Introduction  \nLung cancer is one of the most common malignant tumors (1, 2). According to Cancer Statistics, 2020, the 5-year survival rate of lung cancer is only 19%(1). Non-small cell lung cancer (NSCLC) is defined as a subtype of lung cancer that accounts for over 80% of lung cancer cases (3). Although there are many treatments for NSCLC, the longterm survival rate of advanced NSCLC patients i","cbCaiaqJr4p1l2H4","https://ap.wps.com/l/cbCaiaqJr4p1l2H4","pdf",3975629,1,10,"English","en",105,"# Introduction\n## Background and clinical needs\n## Motivation for bibliometric analysis\n# Methods\n## Data source and tools\n# Results\n## Publication counts and leading contributors\n## Keyword trends and application focus\n# Conclusions\n## Implications for future research hotspots","[{\"question\":\"What is the purpose of the bibliometric analysis in this study?\",\"answer\":\"To investigate research progress, current hotspots, and potential future hot areas for machine learning applied to NSCLC radiotherapy.\"},{\"question\":\"Which database and tools were used to collect and analyze the studies?\",\"answer\":\"Studies were retrieved from the Web of Science Core Collection, and bibliometric analysis was performed using R-studio (Bibliometrix) and VOSviewer.\"},{\"question\":\"What did the analysis identify as the most frequent keyword and main application area?\",\"answer\":\"“Radiomics” was the most frequent keyword, and machine learning was mainly used to analyze medical images in NSCLC radiotherapy.\"}]","Machine learning in non-small cell lung cancer radiotherapy - A bibliometric analysis | PDF",1785679466,25,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"machine-learning-in-non-small-cell-lung-cancer-radiotherapy-a-bibliometric-analysis","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-in-non-small-cell-lung-cancer-radiotherapy-a-bibliometric-analysis/117770/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-02",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the purpose of the bibliometric analysis in this study?","Question",{"text":75,"@type":76},"To investigate research progress, current hotspots, and potential future hot areas for machine learning applied to NSCLC radiotherapy.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which database and tools were used to collect and analyze the studies?",{"text":80,"@type":76},"Studies were retrieved from the Web of Science Core Collection, and bibliometric analysis was performed using R-studio (Bibliometrix) and VOSviewer.",{"name":82,"@type":73,"acceptedAnswer":83},"What did the analysis identify as the most frequent keyword and main application area?",{"text":84,"@type":76},"“Radiomics” was the most frequent keyword, and machine learning was mainly used to analyze medical images in NSCLC radiotherapy.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":21,"slug":133},"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]