Artificial Intelligence for Tumor Tissue Detection in Stomach Cancer: A Retrospective Algorithm Development and Validation Study.
| Title: | Artificial Intelligence for Tumor Tissue Detection in Stomach Cancer: A Retrospective Algorithm Development and Validation Study. |
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| Authors: | Karnaukhov N; A.S. Loginov Moscow Clinical Scientific Center, 111123 Moscow, Russia.; Institute of Clinical Morphology and Digital Pathology, Sechenov University, 119991 Moscow, Russia.; Palumbo VD; Triolo-Zancla Hospital, 90133 Palermo, Italy.; Voloshin M; A.S. Loginov Moscow Clinical Scientific Center, 111123 Moscow, Russia.; Mongolin A; Institute of Artificial Intelligence, 420500 Innopolis, Russia.; Nova Information Management School, Universidade Nova de Lisboa, Campus de Campolide, 1070-312 Lisboa, Portugal.; Skvortsov A; Institute of Artificial Intelligence, 420500 Innopolis, Russia.; Karimov A; Institute of Artificial Intelligence, 420500 Innopolis, Russia.; Gorbachev Y; A.S. Loginov Moscow Clinical Scientific Center, 111123 Moscow, Russia.; Abramov K; A.S. Loginov Moscow Clinical Scientific Center, 111123 Moscow, Russia.; Zabruntseva A; A.S. Loginov Moscow Clinical Scientific Center, 111123 Moscow, Russia.; Yakubovsky G; A.S. Loginov Moscow Clinical Scientific Center, 111123 Moscow, Russia.; Asaturova A; National Medical Research Center for Obstetrics, Gynecology and Perinatology Named After Academician V.I. Kulakov, Ministry of Health of Russia, 117513 Moscow, Russia.; Department of Pathological Anatomy and Clinical Pathological Anatomy, Institute of Human Biology and Pathology, Pirogov Russian National Research Medical University, 117997 Moscow, Russia.; Palicelli A; Pathology Unit, Azienda USL-IRCCS di Reggio Emilia, 42123 Reggio Emilia, Italy.; Khomeriki S; A.S. Loginov Moscow Clinical Scientific Center, 111123 Moscow, Russia.; Khatkov I; A.S. Loginov Moscow Clinical Scientific Center, 111123 Moscow, Russia. |
| Source: | Journal of clinical medicine [J Clin Med] 2026 Apr 28; Vol. 15 (9). Date of Electronic Publication: 2026 Apr 28. |
| Publication Type: | Journal Article |
| Language: | English |
| Journal Info: | Publisher: MDPI AG Country of Publication: Switzerland NLM ID: 101606588 Publication Model: Electronic Cited Medium: Print ISSN: 2077-0383 (Print) Linking ISSN: 20770383 NLM ISO Abbreviation: J Clin Med Subsets: PubMed not MEDLINE |
| Imprint Name(s): | Original Publication: Basel, Switzerland : MDPI AG, [2012]- |
| Abstract: | Background: Gastric cancer remains one of the leading causes of cancer-related mortality worldwide, underscoring the need for more effective diagnostic strategies. This study aims to use annotated digitized histological slides of gastric cancer and precancerous lesions to develop artificial intelligence algorithms for the diagnosis of gastric lesions. Materials and Methods: We developed a deep learning tool using a training cohort of 970 digitized gastric biopsy slides. Convolutional neural networks (CNNs) were trained for histological recognition and ICD-10 code assignment. The model was validated on an independent test cohort of 250 cases, with expert consensus as the reference standard. Performance was assessed using sensitivity, specificity, and Cohen's kappa. Survival analysis used Kaplan-Meier, log-rank tests (SPSS 16.0; p < 0.05 significant). Results: Analysis of the training cohort led to a scoring system predicting fatal outcomes based on age and morphology (high-grade component > 70%, ulceration, absence of metaplasia/dysplasia). High-risk patients (4-5 points) had significantly worse survival than low-risk patients (0-3 points) (Log Rank = 14,754; p < 0.0001). One-year survival was 71% (low-risk) vs. 40% (high-risk); mean survival was 19.2 vs. 11.3 months. In the test cohort, the AI algorithm demonstrated 79.6% sensitivity and 86.7% specificity (p < 0.0001) for differentiating malignant from benign gastric lesions. Conclusions: A system combining AI-based analysis with a prognostic scoring model has been developed to reduce diagnostic errors and improve risk stratification in gastric cancer pathology. |
| Grant Information: | Agreement No. 142/21 (dated August 18, 2021) Neural network training and the required computational infrastructure were provided under Agreement No. 142/21 (dated August 18, 2021) on joint implementation of AI research activities between the Loginov Moscow Clinical Scientific Center (industrial part |
| Contributed Indexing: | Keywords: artificial intelligence (AI); artificial neural networks (ANNs); convolutional neural networks (CNNs); stomach cancer |
| Entry Date(s): | Date Created: 20260513 Date Completed: 20260513 Latest Revision: 20260515 |
| Update Code: | 20260515 |
| PubMed Central ID: | PMC13163764 |
| DOI: | 10.3390/jcm15093370 |
| PMID: | 42123100 |
| Database: | MEDLINE |
Journal Article