Data Sheet 1_Radiomics in precision medicine for colorectal cancer: a bibliometric analysis (2013–2023).docx

BackgroundThe incidence and mortality of colorectal cancer (CRC) have been rising steadily. Early diagnosis and precise treatment are essential for improving patient survival outcomes. Over the past decade, the integration of artificial intelligence (AI) and medical imaging technologies has positioned radiomics as a critical area of research in the diagnosis, treatment, and prognosis of CRC.
MethodsWe conducted a comprehensive review of CRC-related radiomics literature published between 1 January 2013 and 31 December 2023 using the Web of Science Core Collection database. Bibliometric tools such as Bibliometrix, VOSviewer, and CiteSpace were employed to perform an in-depth bibliometric analysis.
ResultsOur search yielded 1,226 publications, revealing a consistent annual growth in CRC radiomics research, with a significant rise after 2019. China led in publication volume (406 papers), followed by the United States (263 papers), whereas the United States dominated in citation numbers. Notable institutions included General Electric, Harvard University, University of London, Maastricht University, and the Chinese Academy of Sciences. Prominent researchers in this field are Tian J from the Chinese Academy of Sciences, with the highest publication count, and Ganeshan B from the University of London, with the most citations. Journals leading in publication and citation counts are Frontiers in Oncology and Radiology. Keyword and citation analysis identified deep learning, texture analysis, rectal cancer, image analysis, and management as prevailing research themes. Additionally, recent trends indicate the growing importance of AI and multi-omics integration, with a focus on improving precision medicine applications in CRC. Emerging keywords such as deep learning and AI have shown rapid growth in citation bursts over the past 3 years, reflecting a shift toward more advanced technological applications.
ConclusionsRadiomics plays a crucial role in the clinical management of CRC, providing valuable insights for precision medicine. It significantly contributes to predicting molecular biomarkers, assessing tumor aggressiveness, and monitoring treatment efficacy. Future research should prioritize advancing AI algorithms, enhancing multi-omics data integration, and further expanding radiomics applications in CRC precision medicine.

研究背景：结直肠癌（colorectal cancer, CRC）的发病率与死亡率持续攀升，早期诊断与精准治疗是改善患者生存结局的核心举措。近十年来，人工智能（artificial intelligence, AI）与医学影像技术的深度融合，使得放射组学（radiomics）成为结直肠癌诊断、治疗及预后研究的关键领域。
研究方法：本研究基于Web of Science核心合集数据库，对2013年1月1日至2023年12月31日发表的结直肠癌相关放射组学文献开展全面综述。采用Bibliometrix、VOSviewer及CiteSpace等文献计量工具，进行深入的文献计量分析。
研究结果：本次检索共获取1226篇文献，结果显示结直肠癌放射组学研究的发文量呈持续逐年增长态势，2019年后增长幅度显著提升。在发文总量上，中国以406篇位居全球首位，其次为美国（263篇）；但在总被引频次方面，美国占据主导地位。该领域的重要研究机构包括通用电气（General Electric）、哈佛大学（Harvard University）、伦敦大学（University of London）、马斯特里赫特大学（Maastricht University）及中国科学院。本领域的核心研究者分别为来自中国科学院的Tian J（发文量最高）与来自伦敦大学的Ganeshan B（总被引频次最高）。发文量与被引频次领先的期刊为《Frontiers in Oncology》与《Radiology》。关键词与引文分析表明，深度学习、纹理分析、直肠癌、影像分析及临床管理为当前主流研究主题。此外，近期研究趋势显示，人工智能与多组学整合的重要性日益凸显，研究焦点聚焦于提升结直肠癌精准医学的应用水平。近三年来，深度学习与人工智能等新兴关键词的引文突现量快速增长，反映出该领域向更先进的技术应用方向转型的趋势。
研究结论：放射组学在结直肠癌的临床管理中发挥着关键作用，可为精准医学提供极具价值的研究视角。其在预测分子生物标志物、评估肿瘤侵袭性及监测治疗疗效方面均具有重要贡献。未来研究应优先推进人工智能算法的优化升级、强化多组学数据整合，并进一步拓展放射组学在结直肠癌精准医学中的应用场景。