ArticleFrontiers in pharmacology2020
Artificial Intelligence-Based Multiclass Classification of Benign or Malignant Mucosal Lesions of the Stomach.
Article in Frontiers in pharmacology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
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Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it, 26 citations in OpenAlex.
- Use of artificial intelligence for the prediction of lymph node metastases in early-stage colorectal cancer: systematic review.BJS open · 2024Pooled it
- Prediction of lymph node metastasis in early colorectal cancer based on histologic images by artificial intelligence.Scientific reports · 2022Trial
- Artificial Intelligence for Tumor Tissue Detection in Stomach Cancer: A Retrospective Algorithm Development and Validation Study.Journal of clinical medicine · 2026Article
- A multi-task learning model for evaluating non-tumor gastric diseases indicators in whole slide images.Scientific reports · 2025Article
- Application of deep learning models in gastric cancer pathology image analysis: a systematic scoping review.BMC cancer · 2025Article
- A fully annotated pathology slide dataset for early gastric cancer and precancerous lesions.Scientific data · 2025Article
- Artificial intelligence efficiently predicts gastric lesions,Chinese journal of cancer research = Chung-kuo yen cheng yen chiu · 2024Article
- Artificial Intelligence for the Automatic Diagnosis of Gastritis: A Systematic Review.Journal of clinical medicine · 2024Review
- Artificial intelligence in digital pathology: a systematic review and meta-analysis of diagnostic test accuracy.NPJ digital medicine · 2024Review
- Clinical application of machine learning-based pathomics signature of gastric atrophy.Frontiers in oncology · 2024Article
- Recent Advancements in Deep Learning Using Whole Slide Imaging for Cancer Prognosis.Bioengineering (Basel, Switzerland) · 2023Review
- Application of artificial neural network algorithm in pathological diagnosis and prognosis prediction of digestive tract malignant tumors.Zhejiang da xue xue bao. Yi xue ban = Journal of Zhejiang University. Medical sciences · 2023Review
- A systematic review on application of deep learning in digestive system image processing.The Visual computer · 2023Review
- Artificial intelligence: Emerging player in the diagnosis and treatment of digestive disease.World journal of gastroenterology · 2022Review
- Artificial Intelligence-based Tumor Segmentation in Mouse Models of Lung Adenocarcinoma.Journal of pathology informatics · 2022Article
- Hybrid Deep Learning Model for Endoscopic Lesion Detection and Classification Using Endoscopy Videos.Diagnostics (Basel, Switzerland) · 2021Article
- Artificial intelligence in gastroenterology and hepatology: Status and challenges.World journal of gastroenterology · 2021Review
- Tracking cancer lesions on surgical samples of gastric cancer by artificial intelligent algorithms.Journal of Cancer · 2021Article
- Artificial Convolutional Neural Network in Object Detection and Semantic Segmentation for Medical Imaging Analysis.Frontiers in oncology · 2021Review
Corrections and comments
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Authors and funding
7 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Gastric cancer (GC) is one of the leading causes of cancer-related death worldwide. It takes some time from chronic gastritis to develop in GC. Early detection of GC will help patients obtain timely treatment. Understanding disease evolution is crucial for the prevention and treatment of GC. Here, we present a convolutional neural network (CNN)-based system to detect abnormalities in the gastric mucosa. We identified normal mucosa, chronic gastritis, and intestinal-type GC: this is the most common route of gastric carcinogenesis. We integrated digitalizing histopathology of whole-slide images (WSIs), stain normalization, a deep CNN, and a random forest classifier. The staining variability of WSIs was reduced significantly through stain normalization, and saved the cost and time of preparing new slides. Stain normalization improved the effect of the CNN model. The accuracy rate at the patch-level reached 98.4%, and 94.5% for discriminating normal → chronic gastritis → GC. The accuracy rate at the WSIs-level for discriminating normal tissue and cancerous tissue reached 96.0%, which is a state-of-the-art result. Survival analyses indicated that the features extracted from the CNN exerted a significant impact on predicting the survival of cancer patients. Our CNN model disclosed significant potential for adjuvant diagnosis of gastric diseases, especially GC, and usefulness for predicting the prognosis.
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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.