Machine learning in non-small cell lung cancer radiotherapy: A bibliometric analysis

Jiaming Zhang; Huijun Zhu; Jue Wang; Yulu Chen; Yihe Li; Xinyu Chen; Menghua Chen; Zhengwen Cai; Wenqi Liu

doi:10.3389/fonc.2023.1082423

Machine learning in non-small cell lung cancer radiotherapy: A bibliometric analysis

Front Oncol. 2023 Mar 17:13:1082423. doi: 10.3389/fonc.2023.1082423. eCollection 2023.

Authors

Jiaming Zhang¹, Huijun Zhu¹, Jue Wang¹, Yulu Chen¹, Yihe Li¹, Xinyu Chen¹, Menghua Chen¹, Zhengwen Cai², Wenqi Liu¹

Affiliations

¹ 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.

Abstract

Background: 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.

Methods: 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.

Results: 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.

Conclusion: 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.

Keywords: bibliometric analysis; computer science; machine learning; non-small cell lung cancer; radiotherapy.

Grants and funding

The authors declare that this study received funding from Guangxi key research and development program No. (GK) AB18221080, The Basic Ability Enhancement Program for Young and Middle-aged Teachers of Guangxi (2020KY03036), Youth Foundation of Guangxi Medical University(GXMUYSF201918), Foundation of Guangxi Health and Family Planning Commission (Z20190581), Foundation of the Second Affiliate Hospital of Guangxi Medical University (hbrc202104). The funders were not involved in the study design, collection, analysis, interpretation of data, the writing of this article or the decision to submit it for publication.