Machine learning in non-small cell lung cancer radiotherapy: A bibliometric analysis - Zhang, Jiaming; Zhu, Huijun; Wang, Jue; Chen, Yulu; Li, Yihe; Chen, Xinyu; Chen, Menghua; Cai, Zhengwen; Liu, Wenqi (Frontiers in Oncology, 2023)
To investigate the progress in machine learning in radiotherapy for non-small cell lung cancer (NSCLC), we performed a bibliometric analysis of associated research and discuss the current research hotspots and potential hot areas in the future. The involved research was 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. 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 radiotherapy of NSCLC. In conclusion, the research we identified about machine learning in NSCLC radiotherapy was mainly related to the diagnosis and treatment 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.