Machine learning in non-small cell lung cancer radiotherapy: A bibliometric analysis
BackgroundMachine 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 t...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-03-01
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| Series: | Frontiers in Oncology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fonc.2023.1082423/full |
| _version_ | 1827965268874231808 |
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| author | Jiaming Zhang Huijun Zhu Jue Wang Yulu Chen Yihe Li Xinyu Chen Menghua Chen Zhengwen Cai Wenqi Liu |
| author_facet | Jiaming Zhang Huijun Zhu Jue Wang Yulu Chen Yihe Li Xinyu Chen Menghua Chen Zhengwen Cai Wenqi Liu |
| author_sort | Jiaming Zhang |
| collection | DOAJ |
| description | BackgroundMachine 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.MethodsThe 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.ResultsWe 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.ConclusionThe 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. |
| first_indexed | 2024-04-09T17:30:24Z |
| format | Article |
| id | doaj.art-715d1284b282454ca8c280d85a5b9fa7 |
| institution | Directory Open Access Journal |
| issn | 2234-943X |
| language | English |
| last_indexed | 2024-04-09T17:30:24Z |
| publishDate | 2023-03-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Oncology |
| spelling | doaj.art-715d1284b282454ca8c280d85a5b9fa72023-04-18T05:26:22ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2023-03-011310.3389/fonc.2023.10824231082423Machine learning in non-small cell lung cancer radiotherapy: A bibliometric analysisJiaming Zhang0Huijun Zhu1Jue Wang2Yulu Chen3Yihe Li4Xinyu Chen5Menghua Chen6Zhengwen Cai7Wenqi Liu8Department of Radiation Oncology, The Second Affiliated Hospital of Guangxi Medical University, Nanning, ChinaDepartment of Radiation Oncology, The Second Affiliated Hospital of Guangxi Medical University, Nanning, ChinaDepartment of Radiation Oncology, The Second Affiliated Hospital of Guangxi Medical University, Nanning, ChinaDepartment of Radiation Oncology, The Second Affiliated Hospital of Guangxi Medical University, Nanning, ChinaDepartment of Radiation Oncology, The Second Affiliated Hospital of Guangxi Medical University, Nanning, ChinaDepartment of Radiation Oncology, The Second Affiliated Hospital of Guangxi Medical University, Nanning, ChinaDepartment of Radiation Oncology, The Second Affiliated Hospital of Guangxi Medical University, Nanning, ChinaDepartment of Oncology, The Second Affiliated Hospital of Guangxi Medical University, Nanning, ChinaDepartment of Radiation Oncology, The Second Affiliated Hospital of Guangxi Medical University, Nanning, ChinaBackgroundMachine 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.MethodsThe 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.ResultsWe 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.ConclusionThe 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.https://www.frontiersin.org/articles/10.3389/fonc.2023.1082423/fullnon-small cell lung cancerradiotherapymachine learningcomputer sciencebibliometric analysis |
| spellingShingle | Jiaming Zhang Huijun Zhu Jue Wang Yulu Chen Yihe Li Xinyu Chen Menghua Chen Zhengwen Cai Wenqi Liu Machine learning in non-small cell lung cancer radiotherapy: A bibliometric analysis Frontiers in Oncology non-small cell lung cancer radiotherapy machine learning computer science bibliometric analysis |
| title | Machine learning in non-small cell lung cancer radiotherapy: A bibliometric analysis |
| title_full | Machine learning in non-small cell lung cancer radiotherapy: A bibliometric analysis |
| title_fullStr | Machine learning in non-small cell lung cancer radiotherapy: A bibliometric analysis |
| title_full_unstemmed | Machine learning in non-small cell lung cancer radiotherapy: A bibliometric analysis |
| title_short | Machine learning in non-small cell lung cancer radiotherapy: A bibliometric analysis |
| title_sort | machine learning in non small cell lung cancer radiotherapy a bibliometric analysis |
| topic | non-small cell lung cancer radiotherapy machine learning computer science bibliometric analysis |
| url | https://www.frontiersin.org/articles/10.3389/fonc.2023.1082423/full |
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