Retrospective Study
Copyright ©The Author(s) 2025. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastroenterol. May 21, 2025; 31(19): 104897
Published online May 21, 2025. doi: 10.3748/wjg.v31.i19.104897
Application of deep learning models in the pathological classification and staging of esophageal cancer: A focus on Wave-Vision Transformer
Wei Wei, Xiao-Lei Zhang, Hong-Zhen Wang, Lin-Lin Wang, Jing-Li Wen, Xin Han, Qian Liu
Wei Wei, Xiao-Lei Zhang, Hong-Zhen Wang, Jing-Li Wen, Xin Han, Qian Liu, Department of Oncology, Dongying People’s Hospital, Dongying 257091, Shandong Province, China
Lin-Lin Wang, Department of Pathology, Dongying People’s Hospital, Dongying 257091, Shandong Province, China
Author contributions: Zhang XL and Wang HZ contributed to the experimental conception and design; Wang LL, Wen JL, and Han X conducted the experiments; Wei W and Liu Q collected and assembled the experimental data; Wang LL, Wen JL, and Han X contributed to data analysis and interpretation; Zhang XL and Wang HZ wrote the article. All authors approved the final manuscript. Wang LL, Wen JL, and Han X contributed equally to this work.
Institutional review board statement: This study did not involve human subjects or living animals.
Informed consent statement: Not applicable.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
Data sharing statement: All data can be provided as needed.
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: Wei Wei, PhD, Department of Oncology, Dongying People’s Hospital, No. 317 Dongcheng South Road, Dongying District, Dongying 257091, Shandong Province, China. ww19810122@163.com
Received: January 5, 2025
Revised: March 12, 2025
Accepted: April 27, 2025
Published online: May 21, 2025
Processing time: 135 Days and 22.6 Hours
Core Tip

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.