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Articles Published Processes
8/5/2026 5:45:44 AM | Browse: 1 | Download: 0
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Received |
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2026-02-26 02:43 |
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Peer-Review Started |
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2026-02-26 02:43 |
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First Decision by Editorial Office Director |
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2026-03-02 09:28 |
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Return for Revision |
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2026-03-02 09:28 |
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Revised |
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2026-03-12 16:00 |
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Publication Fee Transferred |
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2026-03-18 12:03 |
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Second Decision by Editor |
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2026-04-21 02:48 |
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Second Decision by Editor-in-Chief |
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Final Decision by Editorial Office Director |
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2026-04-21 07:21 |
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Articles in Press |
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2026-04-21 07:21 |
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Edit the Manuscript by Language Editor |
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Typeset the Manuscript |
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2026-07-30 03:48 |
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Publish the Manuscript Online |
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2026-08-05 05:45 |
| 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. |
| 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 |
Pathology |
| Manuscript Type |
Retrospective Study |
| Article Title |
Automatic recognition of tumour-infiltrating lymphocytes in pathological biopsy images of the gastric mucosa
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| 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 |
| ORCID |
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| Funding Agency and Grant Number |
| Funding Agency |
Grant Number |
| the Shenzhen Basic Research Special Natural Science Foundation Project |
No. JCYJ202506044185911015 |
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| 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. |
| Publish Date |
2026-08-05 05:45 |
| 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; 32(32): 120382
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| URL |
https://www.wjgnet.com/1007-9327/full/v32/i32/120382.htm |
| DOI |
https://doi.org/10.3748/wjg.120382 |
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