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Articles Published Processes
5/15/2025 10:28:51 AM | Browse: 21 | Download: 42
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Received |
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2024-11-29 07:40 |
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Peer-Review Started |
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2024-11-29 07:40 |
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To Make the First Decision |
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Return for Revision |
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2025-01-03 08:46 |
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Revised |
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2025-01-08 12:42 |
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Second Decision |
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2025-02-28 02:34 |
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Accepted by Journal Editor-in-Chief |
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Accepted by Executive Editor-in-Chief |
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2025-02-28 06:53 |
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Articles in Press |
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2025-02-28 06:53 |
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Publication Fee Transferred |
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2025-01-13 07:07 |
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Edit the Manuscript by Language Editor |
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Typeset the Manuscript |
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2025-04-27 09:04 |
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Publish the Manuscript Online |
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2025-05-15 10:28 |
| ISSN |
1948-5204 (online) |
| 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: http://creativecommons.org/Licenses/by-nc/4.0/ |
| Copyright |
© The Author(s) 2025. Published by Baishideng Publishing Group Inc. All rights reserved. |
| Article Reprints |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/247
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| Permissions |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/207
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| Publisher |
Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA |
| Website |
http://www.wjgnet.com |
| Category |
Gastroenterology & Hepatology |
| Manuscript Type |
Scientometrics |
| Article Title |
Research status and trends of deep learning in colorectal cancer (2011-2023): Bibliometric analysis and visualization
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| Manuscript Source |
Unsolicited Manuscript |
| All Author List |
Lu-Ying Qi, Bai-Wang Li, Jie-Qiong Chen, Hu-Po Bian, Jing-Nan Xue and Hong-Xing Zhao |
| ORCID |
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| Funding Agency and Grant Number |
| Funding Agency |
Grant Number |
| Science and Technology Project of Huzhou City, Zhejiang Province |
2023GY33 |
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| 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 |
| Key Words |
Colorectal cancer; Deep learning; Artificial intelligence; Bibliometric analysis; Visualization |
| 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. |
| Publish Date |
2025-05-15 10:28 |
| Citation |
<p>Qi LY, Li BW, Chen JQ, Bian HP, Xue JN, Zhao HX. Research status and trends of deep learning in colorectal cancer (2011-2023): Bibliometric analysis and visualization. <i>World J Gastrointest Oncol</i> 2025; 17(5): 103667</p> |
| URL |
https://www.wjgnet.com/1948-5204/full/v17/i5/103667.htm |
| DOI |
https://dx.doi.org/10.4251/wjgo.v17.i5.103667 |
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