Publication Name	World Journal of Gastrointestinal Oncology
Manuscript ID	119889
Country	China

Category	Gastroenterology & Hepatology
Manuscript Type	Retrospective Study
Article Title	Multicenter deep learning model for pancreatic cancer detection using endoscopic ultrasound
Manuscript Source	Unsolicited Manuscript
All Author List	Xin-Ying Yu, Jun-Qiang Ye, Zhen He and Qiang He
Funding Agency and Grant Number
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.
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; In press

Received	2026-02-10 02:52
Peer-Review Started	2026-02-10 02:53
First Decision by Editorial Office Director	2026-03-20 10:55
Return for Revision	2026-03-20 11:04
Revised	2026-03-29 10:51
Publication Fee Transferred	2026-03-31 04:11
Second Decision by Editor	2026-05-12 02:44
Second Decision by Editor-in-Chief
Final Decision by Editorial Office Director	2026-05-12 06:10
Articles in Press	2026-05-12 06:10
Edit the Manuscript by Language Editor
Typeset the Manuscript

ISSN	1948-5204 (online)
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Copyright	©Author(s) (or their employer(s)) 2026. No commercial re-use. See Permissions. Published by Baishideng Publishing Group Inc.
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