Publication Name	World Journal of Gastroenterology
Manuscript ID	104897
Country	China

Category	Pathology
Manuscript Type	Retrospective Study
Article Title	Application of deep learning models in the pathological classification and staging of esophageal cancer: A focus on Wave-Vision Transformer
Manuscript Source	Unsolicited Manuscript
All Author List	Wei Wei, Xiao-Lei Zhang, Hong-Zhen Wang, Lin-Lin Wang, Jing-Li Wen, Xin Han and Qian Liu
Funding Agency and Grant Number
Corresponding Author	Wei Wei, PhD, Department of Oncology, Dongying People’s Hospital, No. 317 Dongcheng South Road, Dongying District, Dongying Dongying, Shandong Province, China. ww19810122@163.com
Key Words	Esophageal cancer; Deep learning; Wave-Vision Transformer; Pathological classification; Staging; Early detection
Core Tip	This study demonstrates the application of deep learning models, particularly Wave-Vision Transformer, for the pathological classification and staging of esophageal cancer. Wave-Vision Transformer outperformed other models such as transformer, residual network, and multi-layer perceptron, achieving the highest accuracy of 88.97% with low computational complexity. This innovative approach shows promise for improving early detection and personalized treatment strategies for esophageal cancer, potentially enhancing clinical outcomes in real-time applications.
Citation	Wei W, Zhang XL, Wang HZ, Wang LL, Wen JL, Han X, Liu Q. Application of deep learning models in the pathological classification and staging of esophageal cancer: A focus on Wave-Vision Transformer. World J Gastroenterol 2025; In press

Received	2025-01-05 13:05
Peer-Review Started	2025-01-05 13:05
To Make the First Decision
Return for Revision	2025-02-13 22:19
Revised	2025-03-12 07:39
Second Decision	2025-04-27 02:41
Accepted by Journal Editor-in-Chief
Accepted by Executive Editor-in-Chief	2025-04-27 07:04
Articles in Press	2025-04-27 07:04
Publication Fee Transferred
Edit the Manuscript by Language Editor
Typeset the Manuscript

ISSN	1007-9327 (print) and 2219-2840 (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/
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