ArticleGastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association2018
Automated histological classification of whole-slide images of gastric biopsy specimens.
Article in Gastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers, 1 of them a synthesis that pooled it.
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Who cites it
43 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Automated classification of cancer from fine needle aspiration cytological image use neural networks: A meta-analysis.Diagnostic cytopathology · 2020Pooled it
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- AI caption generation model for digital pathology of adenocarcinoma in endoscopic histopathology using multi-instance attention mechanisms.Scientific reports · 2026Article
- The Laryngovibrogram as a normalized spatiotemporal representation of vocal fold dynamics.Scientific reports · 2025Article
- Identification of histopathological classification and establishment of prognostic indicators of gastric adenocarcinoma based on deep learning algorithm.Medical molecular morphology · 2024Article
- Gastric Cancer Genetics and Its Implications for Diagnosis, Prognosis, and Treatment of the Disease.The Korean journal of helicobacter and upper gastrointestinal research · 2024Review
- Evaluation of the value of combined detection of tumor markers CA724, carcinoembryonic antigen, CA242, and CA19-9 in gastric cancer.World journal of gastrointestinal oncology · 2024Article
- Curcumin inhibits the growth and invasion of gastric cancer by regulating long noncoding RNA AC022424.2.World journal of gastrointestinal oncology · 2024Article
- Applications of artificial intelligence in digital pathology for gastric cancer.Frontiers in oncology · 2024Review
- Deep Learning for the Pathologic Diagnosis of Hepatocellular Carcinoma, Cholangiocarcinoma, and Metastatic Colorectal Cancer.Cancers · 2023Article
- Chitosan-coated halloysite nanotube magnetic microspheres for carcinogenic colorectal hemorrhage and liver laceration in albino rats.RSC advances · 2023Article
- Recent advances in endoscopic management of gastric neoplasms.World journal of gastrointestinal endoscopy · 2023Review
- Review
- Multi-modality artificial intelligence in digital pathology.Briefings in bioinformatics · 2022Review
- Development and multi-institutional validation of an artificial intelligence-based diagnostic system for gastric biopsy.Cancer science · 2022Article
- Endocytoscopic Observation of Esophageal Lesions: Our Own Experience and a Review of the Literature.Diagnostics (Basel, Switzerland) · 2022Review
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- Artificial Intelligence in Endoscopy.Digestive diseases and sciences · 2022Review
- Deep learning-based image-analysis algorithm for classification and quantification of multiple histopathological lesions in rat liver.Journal of toxicologic pathology · 2022Article
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9 authors.
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Abstract
backgroundAutomated image analysis has been developed currently in the field of surgical pathology. The aim of the present study was to evaluate the classification accuracy of the e-Pathologist image analysis software.
methodsA total of 3062 gastric biopsy specimens were consecutively obtained and stained. The specimen slides were anonymized and digitized. At least two experienced gastrointestinal pathologists evaluated each slide for pathological diagnosis. We compared the three-tier (positive for carcinoma or suspicion of carcinoma; caution for adenoma or suspicion of a neoplastic lesion; or negative for a neoplastic lesion) or two-tier (negative or non-negative) classification results of human pathologists and of the e-Pathologist.
resultsOf 3062 cases, 33.4% showed an abnormal finding. For the three-tier classification, the overall concordance rate was 55.6% (1702/3062). The kappa coefficient was 0.28 (95% CI, 0.26-0.30; fair agreement). For the negative biopsy specimens, the concordance rate was 90.6% (1033/1140), but for the positive biopsy specimens, the concordance rate was less than 50%. For the two-tier classification, the sensitivity, specificity, positive predictive value, and negative predictive value were 89.5% (95% CI, 87.5-91.4%), 50.7% (95% CI, 48.5-52.9%), 47.7% (95% CI, 45.4-49.9%), and 90.6% (95% CI, 88.8-92.2%), respectively.
conclusionsAlthough there are limitations and requirements for applying automated histopathological classification of gastric biopsy specimens in the clinical setting, the results of the present study are promising.
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