Scientometrics
Copyright ©The Author(s) 2025. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastrointest Oncol. May 15, 2025; 17(5): 103667
Published online May 15, 2025. doi: 10.4251/wjgo.v17.i5.103667
Research status and trends of deep learning in colorectal cancer (2011-2023): Bibliometric analysis and visualization
Lu-Ying Qi, Bai-Wang Li, Jie-Qiong Chen, Hu-Po Bian, Jing-Nan Xue, Hong-Xing Zhao
Lu-Ying Qi, Jie-Qiong Chen, Hu-Po Bian, Jing-Nan Xue, Department of Radiology, The First Affiliated Hospital of Huzhou University, Huzhou 313000, Zhejiang Province, China
Bai-Wang Li, Center of Gastrointestinal Endoscopy, The Fourth People’s Hospital of Jinan, Jinan 250031, Shandong Province, China
Hong-Xing Zhao, Department of Radiology, The First Affiliated Hospital of Huzhou University, Huzhou Key Laboratory of Precise Diagnosis and Treatment of Urinary Tumors, Huzhou 313000, Zhejiang Province, China
Author contributions: Qi LY, Bian HP, and Xue JN conceptualized and designed the research; Qi LY, Li BW, and Chen JQ analyzed the primary data and drafted the manuscript; Zhao HX reviewed and revised the manuscript. All the authors have read and approved the final manuscript.
Supported by Science and Technology Project of Huzhou City, Zhejiang Province, No. 2023GY33.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
PRISMA 2009 Checklist statement: The authors have read the PRISMA 2009 Checklist, and the manuscript was prepared and revised according to the PRISMA 2009 Checklist.
Open Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Hong-Xing Zhao, Chief Physician, Department of Radiology, The First Affiliated Hospital of Huzhou University, Huzhou Key Laboratory of Precise Diagnosis and Treatment of Urinary Tumors, No. 158 Square Back Road, Wuxing District, Huzhou 313000, Zhejiang Province, China. 50073@zjhu.edu.cn
Received: November 29, 2024
Revised: January 8, 2025
Accepted: February 28, 2025
Published online: May 15, 2025
Processing time: 169 Days and 13.8 Hours
Core Tip

Core Tip: This bibliometric analysis evaluated the application of deep learning in colorectal cancer and identifies valuable future directions for studying the diagnosis, treatment and prognosis of colorectal cancer. It is recommended to optimize deep learning models, such as convolutional neural networks and transformers, strengthen multicenter collaboration, and focus on emerging hotspots, such as microsatellite instability and autoencoder-based models.