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Articles Published Processes
9/9/2026 9:45:08 AM | Browse: 1 | Download: 0
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Received |
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2026-02-10 02:52 |
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Peer-Review Started |
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2026-02-10 02:53 |
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First Decision by Editorial Office Director |
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2026-03-20 10:55 |
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Return for Revision |
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2026-03-20 11:04 |
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Revised |
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2026-03-29 10:51 |
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Publication Fee Transferred |
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2026-03-31 04:11 |
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Second Decision by Editor |
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2026-05-12 02:44 |
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Second Decision by Editor-in-Chief |
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Final Decision by Editorial Office Director |
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2026-05-12 06:10 |
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Articles in Press |
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2026-05-12 06:10 |
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Edit the Manuscript by Language Editor |
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Typeset the Manuscript |
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2026-08-21 09:27 |
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Publish the Manuscript Online |
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2026-09-09 09:45 |
| ISSN |
1948-5204 (online) |
| Open Access |
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial (CC BY-NC 4.0) license. No commercial re-use. See Permissions. Published by Baishideng Publishing Group Inc. |
| Copyright |
©Author(s) (or their employer(s)) 2026. No commercial re-use. See Permissions. Published by Baishideng Publishing Group Inc. |
| 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 |
Retrospective Study |
| Article Title |
Multicenter deep learning model for pancreatic cancer detection using endoscopic ultrasound
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| Manuscript Source |
Unsolicited Manuscript |
| All Author List |
Xin-Ying Yu, Jun-Qiang Ye, Zhen He and Qiang He |
| ORCID |
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| Funding Agency and Grant Number |
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| Corresponding Author |
Qiang He, Department of Gastroenterology, Beijing Tiantan Hospital, Capital Medical University, No. 119 South Fourth Ring Road West, Fengtai District, Beijing 100071, China. 229476289@qq.com |
| Key Words |
Pancreatic cancer lesions; Endoscopic ultrasound; Deep learning; Multicenter study; Computer-aided diagnosis; Attention mechanism |
| Core Tip |
This study introduces the first multicenter endoscopic ultrasound dataset for pancreatic lesions. We also propose a novel deep learning model, MCEUS-C2Net. It integrates local and global features with channel attention mechanisms. This design effectively overcomes cross-center imaging heterogeneity. The model accurately differentiates cancerous from noncancerous pancreatic lesions. During external validation, it demonstrated exceptional generalization. Ultimately, this research provides a valuable benchmark dataset and a robust deep learning network. This network holds the potential to enhance the accuracy of clinical decision-making in real-world settings. |
| Publish Date |
2026-09-09 09:45 |
| Citation |
Yu XY, Ye JQ, He Z, He Q. Multicenter deep learning model for pancreatic cancer detection using endoscopic ultrasound. World J Gastrointest Oncol 2026; 18(9): 119889
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| URL |
https://www.wjgnet.com/1948-5204/full/v18/i9/119889.htm |
| DOI |
https://doi.org/10.4251/wjgo.119889 |
Copyright © 1993-2026 Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA. All rights reserved, including rights relating to text and data mining, AI training, and similar technologies. For open-access content, the applicable copyright and licensing terms govern.
