Publication Name	World Journal of Gastroenterology
Manuscript ID	120382
Country	China

Category	Pathology
Manuscript Type	Retrospective Study
Article Title	Automatic recognition of tumour-infiltrating lymphocytes in pathological biopsy images of the gastric mucosa
Manuscript Source	Unsolicited Manuscript
All Author List	Yu Fan, Su-Nan Wang, Bo Jiang, Ying-Ying Li, Chao-Ya Zhu, Xing-Hai Liao, Fa-Shun Zhang and Yang-Kun Wang
Funding Agency and Grant Number	Funding Agency Grant Number the Shenzhen Basic Research Special Natural Science Foundation Project No. JCYJ202506044185911015
Corresponding Author	Yang-Kun Wang, Department of Pathology, The Fourth People's Hospital of Longgang District, Shenzhen, No. 2 Jinjian Road, Nanwan Subdistrict, Longgang District, Shenzhen 518123, Guangdong Province, China. dr.wyk@163.com
Key Words	Artificial intelligence; Gastric cancer; Tumour-infiltrating lymphocytes; Deep learning; Prognosis; Digital pathology
Core Tip	This study constructs a multiscale two-stage convolutional neural network (CNN) model to realize fully automated and high-precision identification of tumour-infiltrating lymphocytes (TILs) in gastric mucosa. It pioneers the gastric-artificial intelligence-TIL (G-AI-TIL) index, which enables quantitative grading of gastric mucosal lesions and serves as an independent protective factor for the prognosis of early gastric cancer (EGC). The model addresses the subjectivity of traditional TIL assessment methods, and its performance is verified by multicentre data to be significantly superior to manual evaluation, with high efficiency and great potential for clinical translation. This study retrospectively collected 320 whole-slide images of gastric mucosal biopsies and constructed a two-stage CNN model, which underwent multi-step preprocessing and annotated training. The verification results showed the model achieved a 99.2% accuracy in TIL identification. It was found that the G-AI-TIL index increases with the elevated malignancy of gastric mucosal lesions, and a high G-AI-TIL index acts as an independent protective factor for the survival of EGC patients. This research provides a novel objective tool for the precise diagnosis and treatment of gastric cancer.
Citation	Fan Y, Wang SN, Jiang B, Li YY, Zhu CY, Liao XH, Zhang FS, Wang YK. Automatic recognition of tumour-infiltrating lymphocytes in pathological biopsy images of the gastric mucosa. World J Gastroenterol 2026; In press

Received	2026-02-26 02:43
Peer-Review Started	2026-02-26 02:43
First Decision by Editorial Office Director	2026-03-02 09:28
Return for Revision	2026-03-02 09:28
Revised	2026-03-12 16:00
Publication Fee Transferred	2026-03-18 12:03
Second Decision by Editor	2026-04-21 02:48
Second Decision by Editor-in-Chief
Final Decision by Editorial Office Director	2026-04-21 07:21
Articles in Press	2026-04-21 07:21
Edit the Manuscript by Language Editor
Typeset the Manuscript

ISSN	1007-9327 (print) and 2219-2840 (online)
Open Access	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.
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