Trial reportGastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association2024
Diagnosing and grading gastric atrophy and intestinal metaplasia using semi-supervised deep learning on pathological images: development and validation study.
Trial report in Gastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 1 of them a synthesis that pooled it.
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Who cites it
30 citing papers in PubMed, 1 synthesis or guideline pooled it, 25 citations in OpenAlex.
- Artificial intelligence in gastric cancer: a systematic review of machine learning and deep learning applications.Abdominal radiology (New York) · 2026Pooled it
- Gastric mucosa intestinal metaplasia segmentation and grading via transformer-CNN fusion architecture: an interpretable digital pathology diagnostic framework.Medical & biological engineering & computing · 2026Article
- Artificial Intelligence for Endoscopic Diagnosis of Gastric Premalignant Lesions.The Korean journal of helicobacter and upper gastrointestinal research · 2026Review
- Research progress of machine learning applications in gastric cancer diagnosis and therapy.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026Review
- Dual-stream token fusion with Swin Transformer and lesion-aware tokens for gastric metaplasia classification in IoMT-assisted deployment.BMC medical imaging · 2026Article
- Endoscopic Diagnosis of Chronic Atrophic Gastritis and Early Gastric Cancer: From Basics to Advanced Imaging.Cancers · 2026Review
- Subspecialty-specific foundation model for intelligent gastrointestinal pathology.NPJ digital medicine · 2026Article
- Expanding role of artificial intelligence in gastric neoplasms: moving beyond resource limitations.Clinical endoscopy · 2026Article
- The LGR5/TFF2 ratio as a stem cell biomarker predicts high-risk status in chronic atrophic gastritis: a retrospective cohort study.BMC gastroenterology · 2026Article
- Gastric intestinal metaplasia: Management and surveillance strategies.World journal of gastrointestinal pathophysiology · 2026Review
- Towards deep-learning based detection and quantification of intestinal metaplasia on digitized gastric biopsies: a multi-expert comparative study.Scientific reports · 2026Article
- Artificial intelligence in digital pathology diagnosis and analysis: technologies, challenges, and future prospects.Military Medical Research · 2026Review
- An interpretable deep learning framework for intestinal metaplasia detection in gastric histopathology images.Frontiers in oncology · 2026Article
- Recent advances, comparative performance, and data requirements of artificial intelligence-assisted technologies for the diagnosis of gastric cancer.Frontiers in gastroenterology (Lausanne, Switzerland) · 2026Review
- A Non-Endoscopic Dynamic Nomogram for Atrophic Gastritis Risk Stratification Based on Serum Indicators and Clinical Factors.Journal of inflammation research · 2026Article
- Stratified management of residual gastric cancer risk afterFrontiers in microbiology · 2026Article
- A multi-task learning model for evaluating non-tumor gastric diseases indicators in whole slide images.Scientific reports · 2025Article
- Role of artificial intelligence in the detection and characterization of gastrointestinal premalignant and early malignant lesions.World journal of gastroenterology · 2025Review
- Hybrid Deep Learning Models for Analyzing Histological Images of the Zebrafish Intestine Under Oxidative Stress.Diagnostics (Basel, Switzerland) · 2025Article
- Role of artificial intelligence in screening and medical imaging of precancerous gastric diseases.World journal of clinical oncology · 2025Review
Corrections and comments
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Authors and funding
20 authors at 9 institutions in 2 countries.
Funding
Abstract
objectivePatients with gastric atrophy and intestinal metaplasia (IM) were at risk for gastric cancer, necessitating an accurate risk assessment. We aimed to establish and validate a diagnostic approach for gastric biopsy specimens using deep learning and OLGA/OLGIM for individual gastric cancer risk classification.
methodsIn this study, we prospectively enrolled 545 patients suspected of atrophic gastritis during endoscopy from 13 tertiary hospitals between December 22, 2017, to September 25, 2020, with a total of 2725 whole-slide images (WSIs). Patients were randomly divided into a training set (n = 349), an internal validation set (n = 87), and an external validation set (n = 109). Sixty patients from the external validation set were randomly selected and divided into two groups for an observer study, one with the assistance of algorithm results and the other without. We proposed a semi-supervised deep learning algorithm to diagnose and grade IM and atrophy, and we compared it with the assessments of 10 pathologists. The model's performance was evaluated based on the area under the curve (AUC), sensitivity, specificity, and weighted kappa value.
resultsThe algorithm, named GasMIL, was established and demonstrated encouraging performance in diagnosing IM (AUC 0.884, 95% CI 0.862-0.902) and atrophy (AUC 0.877, 95% CI 0.855-0.897) in the external test set. In the observer study, GasMIL achieved an 80% sensitivity, 85% specificity, a weighted kappa value of 0.61, and an AUC of 0.953, surpassing the performance of all ten pathologists in diagnosing atrophy. Among the 10 pathologists, GasMIL's AUC ranked second in OLGA (0.729, 95% CI 0.625-0.833) and fifth in OLGIM (0.792, 95% CI 0.688-0.896). With the assistance of GasMIL, pathologists demonstrated improved AUC (p = 0.013), sensitivity (p = 0.014), and weighted kappa (p = 0.016) in diagnosing IM, and improved specificity (p = 0.007) in diagnosing atrophy compared to pathologists working alone.
conclusionGasMIL shows the best overall performance in diagnosing IM and atrophy when compared to pathologists, significantly enhancing their diagnostic capabilities.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.