Qi Lu-Ying, Li Bai-Wang, Chen Jie-Qiong, Bian Hu-Po, Xue Jing-Nan, Zhao Hong-Xing
Department of Radiology, The First Affiliated Hospital of Huzhou University, Huzhou 313000, Zhejiang Province, China.
Center of Gastrointestinal Endoscopy, The Fourth People's Hospital of Jinan, Jinan 250031, Shandong Province, China.
World J Gastrointest Oncol. 2025 May 15;17(5):103667. doi: 10.4251/wjgo.v17.i5.103667.
Colorectal cancer (CRC) is the third-most prevalent cancer and the cancer with the second-highest mortality rate worldwide, representing a high public health burden. Deep learning (DL) offers advantages in the diagnosis, identification, localization, classification and prognosis of CRC patients. However, few bibliometric analyses of research hotspots and trends in the field have been performed.
To use bibliometric approaches to analyze and visualize the current research state and development trend of DL in CRC as well as to anticipate future research directions and hotspots.
Datasets were retrieved from the Web of Science Core Collection for the period January 2011 to December 2023. Scimago Graphica (1.0.45), VOSviewer (1.6.20) and CiteSpace (6.3.1) were used to analyze and visualize the nation, institution, journal, author, reference and keyword indicators. Origin (2022) was utilized for plotting, and Excel (2021) was used to construct the tables.
A total of 1275 publications in 538 journals from 74 countries and 2267 institutions were collected. The number of annual publications has increased over time. China (371, 29.1%), the United States (265, 20.8%) and Japan (155, 12.2%) contributed significantly to the number of articles published, accounting for 62.1% of the total publications. The United States had the strongest ties to other nations. Sun Yat-sen University in China had the highest number of publications (32). The journal with the most publications was (34, Q2), whereas had the most co-citations (1053, Q1). Kather JN, was the author with the most articles (12) and co-citations (287). The most frequently cited reference was . Keywords were divided into six clusters, with "colorectal cancer" (12.34) having the highest outbreak intensity.
This study highlights the current status and most active directions of the use of DL in CRC. This approach has important applications in the identification, diagnosis, localization, classification and prognosis of the disease and will remain a central focus in the future.
结直肠癌（CRC）是全球第三大常见癌症，也是死亡率第二高的癌症，给公共卫生带来了沉重负担。深度学习（DL）在CRC患者的诊断、识别、定位、分类和预后方面具有优势。然而，该领域关于研究热点和趋势的文献计量分析较少。
运用文献计量学方法分析和可视化DL在CRC领域的当前研究状况和发展趋势，并预测未来的研究方向和热点。
从科学网核心合集检索2011年1月至2023年12月期间的数据集。使用Scimago Graphica（1.0.45）、VOSviewer（1.6.20）和CiteSpace（6.3.1）分析和可视化国家、机构、期刊、作者、参考文献和关键词指标。利用Origin（2022）进行绘图，使用Excel（2021）构建表格。
共收集了来自74个国家和2267个机构的538种期刊上的1275篇出版物。年出版物数量随时间增加。中国（371篇，29.1%）、美国（265篇，20.8%）和日本（155篇，12.2%）对发表文章数量贡献显著，占总出版物的62.1%。美国与其他国家联系最为紧密。中国中山大学发表的文章数量最多（32篇）。发表文章最多的期刊是《 》（34篇，Q2分区），而《 》的共被引次数最多（1053次，Q1分区）。Kather JN是发表文章最多（12篇）且共被引次数最多（287次）的作者。被引用最频繁的参考文献是《 》。关键词分为六个聚类，“结直肠癌”（爆发强度为12.34）的爆发强度最高。
本研究突出了DL在CRC领域的当前应用状况和最活跃的研究方向。该方法在疾病的识别、诊断、定位、分类和预后方面具有重要应用，并且在未来仍将是核心研究重点。
