Chen Zijian, Liu Yangqi, Lin Zeying, Huang Weizhe
Department of Cardiothoracic Surgery, The Second Affiliated Hospital of Shantou University Medical College, Shantou, China.
Open Med (Wars). 2024 Feb 9;19(1):20230874. doi: 10.1515/med-2023-0874. eCollection 2024.
Advances in lung cancer research applying machine learning (ML) technology have generated many relevant literature. However, there is absence of bibliometric analysis review that aids a comprehensive understanding of this field and its progress. Present article for the first time performed a bibliometric analysis to clarify research status and focus from 2010 to 2021. In the analysis, a total of 2,312 relevant literature were searched and retrieved from the Web of Science Core Collection database. We conducted a bibliometric analysis and further visualization. During that time, exponentially growing annual publication and our model have shown a flourishing research prospect. Annual citation reached the peak in 2017. Researchers from United States and China have produced most of the relevant literature and strongest partnership between them. and appeared to bring more attention to the public. The computer-aided diagnosis, precision medicine, and survival prediction were the focus of research, reflecting the development trend at that period. ML did make a big difference in lung cancer research in the past decade.
应用机器学习（ML）技术的肺癌研究进展产生了许多相关文献。然而，缺乏有助于全面了解该领域及其进展的文献计量分析综述。本文首次进行了文献计量分析，以阐明2010年至2021年的研究现状和重点。在分析中，从Web of Science核心合集数据库中总共检索到2312篇相关文献。我们进行了文献计量分析并进一步可视化。在此期间，年度出版物呈指数增长，我们的模型显示出蓬勃的研究前景。年度引用量在2017年达到峰值。美国和中国的研究人员发表了大部分相关文献，且他们之间的合作最为紧密。 和 似乎更受公众关注。计算机辅助诊断、精准医学和生存预测是研究重点，反映了当时的发展趋势。在过去十年中，机器学习在肺癌研究中确实发挥了重要作用。
