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

Aug 2, 2026

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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.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages10

Reading Time25 min

AccessFree to download

Introduction

Background and clinical needs

Motivation for bibliometric analysis

Methods

Data source and tools

Results

Publication counts and leading contributors

Keyword trends and application focus

Conclusions

Implications for future research hotspots

TYPE Original Research PUBLISHED 17 March 2023 DOI 10.3389/fonc.2023.1082423 OPEN ACCESS EDITED BY Tonghe Wang, Memorial Sloan Kettering Cancer Center, United States REVIEWED BY Li-Sheng Geng, Beihang University, China Jian Bin Li, Shandong Cancer Hospital, China *CORRESPONDENCE Zhengwen Cai [99cczzww@sina.com](99cczzww@sina.com)[ ](99cczzww@sina.com)Wenqi Liu [liuwenqigx@163.com](liuwenqigx@163.com) †These authors have contributed equally to this work and share ﬁrst authorship SPECIALTY SECTION This article was submitted to Radiation Oncology, a section of the journal Frontiers in Oncology RECEIVED 28 October 2022 ACCEPTED 20 February 2023 PUBLISHED 17 March 2023 CITATION Zhang 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. Front. Oncol. 13:1082423 . doi: 10.3389/fonc.2023.1082423 COPYRIGHT © 2023 Zhang, Zhu, Wang, Chen, Li, Chen, Chen, Cai and Liu. This is an open-access article distributed under the terms of the Creative 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. Machine learning in non-small cell lung cancer radiotherapy: A bibliometric analysis Jiaming 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* 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 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. Results: 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 non-small cell lung cancer, radiotherapy, machine learning, computer science, bibliometric analysis Frontiers in Oncology 01 [frontiersin.org](frontiersin.org) 1 Introduction Lung 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

FAQs

What is the purpose of the bibliometric analysis in this study?

To investigate research progress, current hotspots, and potential future hot areas for machine learning applied to NSCLC radiotherapy.

Which database and tools were used to collect and analyze the studies?

Studies were retrieved from the Web of Science Core Collection, and bibliometric analysis was performed using R-studio (Bibliometrix) and VOSviewer.

What did the analysis identify as the most frequent keyword and main application area?

“Radiomics” was the most frequent keyword, and machine learning was mainly used to analyze medical images in NSCLC radiotherapy.

Aug 2, 2026

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