Table_4_Bibliometric and visual analysis of radiomics for evaluating lymph node status in oncology.XLSX

BackgroundRadiomics, which involves the conversion of digital images into high-dimensional data, has been used in oncological studies since 2012. We analyzed the publications that had been conducted on this subject using bibliometric and visual methods to expound the hotpots and future trends regarding radiomics in evaluating lymph node status in oncology.
MethodsDocuments published between 2012 and 2023, updated to August 1, 2024, were searched using the Scopus database. VOSviewer, R Package, and Microsoft Excel were used for visualization.
ResultsA total of 898 original articles and reviews written in English and be related to radiomics for evaluating lymph node status in oncology, published between 2015 and 2023, were retrieved. A significant increase in the number of publications was observed, with an annual growth rate of 100.77%. The publications predominantly originated from three countries, with China leading in the number of publications and citations. Fudan University was the most contributing affiliation, followed by Sun Yat-sen University and Southern Medical University, all of which were from China. Tian J. from the Chinese Academy of Sciences contributed the most within 5885 authors. In addition, Frontiers in Oncology had the most publications and transcended other journals in recent 4 years. Moreover, the keywords co-occurrence suggested that the interplay of “radiomics” and “lymph node metastasis,” as well as “major clinical study” were the predominant topics, furthermore, the focused topics shifted from revealing the diagnosis of cancers to exploring the deep learning-based prediction of lymph node metastasis, suggesting the combination of artificial intelligence research would develop in the future.
ConclusionThe present bibliometric and visual analysis described an approximately continuous trend of increasing publications related to radiomics in evaluating lymph node status in oncology and revealed that it could serve as an efficient tool for personalized diagnosis and treatment guidance in clinical patients, and combined artificial intelligence should be further considered in the future.

研究背景：放射组学（Radiomics）是将数字图像转换为高维数据的技术，自2012年起便应用于肿瘤学研究。本研究采用文献计量学与可视化分析方法，对该领域已发表的相关文献进行梳理，以阐明肿瘤学领域中放射组学评估淋巴结状态的研究热点与未来趋势。
研究方法：本研究检索了2012年至2023年（更新至2024年8月1日）发表的相关文献，检索数据库为斯高帕斯数据库（Scopus）。采用VOSviewer、R包（R Package）及Microsoft Excel进行可视化分析。
研究结果：本研究共检索到2015年至2023年发表的898篇英文原创研究论文与综述，均围绕放射组学评估肿瘤学领域淋巴结状态这一主题。相关文献发表量呈显著增长趋势，年增长率达100.77%。文献产出主要集中于三个国家，其中中国的文献发表量与被引频次均位居首位。发文贡献度最高的机构为复旦大学，其次为中山大学与南方医科大学，上述机构均来自中国。在总计5885位作者中，来自中国科学院的Tian J.为发文贡献最多的作者。此外，近四年发文量最多的期刊为《Frontiers in Oncology（肿瘤学前沿）》，其发文量远超其他期刊。关键词共现分析结果显示，“放射组学（Radiomics）”与“淋巴结转移（Lymph Node Metastasis）”、“大型临床研究（Major Clinical Study）”的交互关联为核心研究主题；同时，研究热点从揭示癌症诊断转向探索基于深度学习的淋巴结转移预测模型，提示未来将朝着与人工智能（Artificial Intelligence）研究融合的方向发展。
结论：本项文献计量与可视化分析表明，肿瘤学领域中放射组学评估淋巴结状态的相关文献发表量呈近似持续增长的趋势；研究同时证实，放射组学可作为临床患者个性化诊断与治疗指导的高效工具，未来需进一步探索其与人工智能的融合应用。