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World J Gastroenterol. Aug 28, 2026; 32(32): 120382
Published online Aug 28, 2026. doi: 10.3748/wjg.120382
Published online Aug 28, 2026. doi: 10.3748/wjg.120382
Automatic recognition of tumour-infiltrating lymphocytes in pathological biopsy images of the gastric mucosa
Yu Fan, Department of Pathology, Shaanxi Provincial Hospital of Traditional Chinese Medicine, Xi’an 710028, Shaanxi Province, China
Su-Nan Wang, Ying-Ying Li, Shenzhen Polytechnic University, Shenzhen 518055, Guangdong Province, China
Bo Jiang, Department of Pathology, People’s Liberation Army Joint Logistic Support Force 990th Hospital, Zhumadian 463000, Henan Province, China
Chao-Ya Zhu, Department of Pathology, Third Affiliated Hospital, Zhengzhou University, Zhengzhou 450052, Henan Province, China
Xing-Hai Liao, Department of Surgery, Southern Medical University Shenzhen Hospital, Shenzhen 518110, Guangdong Province, China
Fa-Shun Zhang, Department of Pathology, Xuchang Central Hospital, Xuchang 461099, Henan Province, China
Yang-Kun Wang, Department of Pathology, The Fourth People’s Hospital of Longgang District, Shenzhen 518123, Guangdong Province, China
Co-first authors: Yu Fan and Su-Nan Wang.
Author contributions: Wang YK conceived and designed the study; Fan Y ,Liao XH and Jiang B collected data, sorted pathological samples, and drafted the manuscript; Wang SN and Fan Y constructed the model, optimized the algorithm, implemented ablation experiments, and analyzed data; Jiang B and Fan Y performed pathological image annotation, established ground truth, and verified interobserver consistency; Li YY and Fan Y conducted image preprocessing including regions of interest extraction and colour deconvolution, and constructed the dataset; Zhu CY, Fan Y and Zhang FS collated clinical follow-up data and performed survival analysis of early gastric cancer patients; Liao XH , Fan Y and Zhang FS collected multicentre samples and confirmed clinical information; Zhang FS and Fan Y performed pathological diagnosis of gastric mucosal lesions and conducted double-blind verification of sample types; Wang YK acquired funding, revised the manuscript, and gave final approval of the version to be published. Fan Y and Wang SN contributed equally to this work as co-first authors.
Supported by Shenzhen Basic Research Special Natural Science Foundation Project, No. JCYJ202506044185911015.
Institutional review board statement: Approval from the Ethics Committee of Shaanxi Provincial Academy of Traditional Chinese Medicine and Shaanxi Provincial Hospital of Traditional Chinese Medicine, Exemption Approval No. (2023) Lunshenmian No. (15), date: January 10, 2023. The study complied with the Declaration of Helsinki and China’s Measures for Ethical Review of Life Science and Medical Research Involving Human Subjects.
Informed consent statement: Written informed consent was obtained from all participants.
Conflict-of-interest statement: The authors declare no conflicts of interest.
Data sharing statement: All data generated or analyzed during this study are included in this published article.
Corresponding author: Yang-Kun Wang, Department of Pathology, The Fourth People’s Hospital of Longgang District, No. 2 Jinjian Road, Nanwan Subdistrict, Longgang District, Shenzhen 518123, Guangdong Province, China. dr.wyk@163.com
Received: February 26, 2026
Revised: March 12, 2026
Accepted: April 21, 2026
Published online: August 28, 2026
Processing time: 159 Days and 11.7 Hours
Revised: March 12, 2026
Accepted: April 21, 2026
Published online: August 28, 2026
Processing time: 159 Days and 11.7 Hours
Core Tip
Core Tip: This study retrospectively collected 320 whole-slide images of gastric mucosal biopsies and constructed a two-stage convolutional neural network model, which underwent multi-step preprocessing and annotated training. The verification results showed the model achieved a 99.2% accuracy in tumour-infiltrating lymphocytes (TIL) identification. It was found that the gastric-artificial intelligence-TIL (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 early gastric cancer patients. This research provides a novel objective tool for the precise diagnosis and treatment of gastric cancer.