ArticleLaboratory investigation; a journal of technical methods and pathology2022
Interpretable tumor differentiation grade and microsatellite instability recognition in gastric cancer using deep learning.
Article in Laboratory investigation; a journal of technical methods and pathology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.
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20 citing papers in PubMed, 1 synthesis or guideline pooled it, 31 citations in OpenAlex.
- Deep learning prediction of MSI/dMMR, EBV, HER2, PD-L1, and CLDN18.2 status directly from hematoxylin and eosin-stained slides in gastric and gastroesophageal junction adenocarcinoma: a systematic review.Frontiers in oncology · 2026Pooled it
- Artificial Intelligence for Molecular Biomarker Identification in Gastrointestinal and Hepatobiliary Cancers.International journal of molecular sciences · 2026Review
- World Endoscopy Organization Position Statements for Artificial Intelligence in Endoscopic Diagnosis of Gastric Epithelial Neoplasia.Digestive endoscopy : official journal of the Japan Gastroenterological Endoscopy Society · 2026Review
- High-intensity focused ultrasound in the multimodal treatment of colorectal cancer liver metastases: A case report.Oncology letters · 2026Article
- Artificial intelligence for biomarker prediction in gastric cancer: from histopathology to multimodal integration.Frontiers in oncology · 2026Review
- AI-based neoadjuvant immunotherapy response prediction across pan-cancer: a comprehensive review.Cancer cell international · 2025Review
- Role of artificial intelligence in screening and medical imaging of precancerous gastric diseases.World journal of clinical oncology · 2025Review
- Computed tomography radiomics to predict microsatellite instability status and immunotherapy response in gastric cancer.Insights into imaging · 2025Article
- A hybrid multi-instance learning-based identification of gastric adenocarcinoma differentiation on whole-slide images.Biomedical engineering online · 2025Article
- Advances and challenges in gastric cancer testing: the role of biomarkers.Cancer biology & medicine · 2025Review
- AI in Histopathology Explorer for comprehensive analysis of the evolving AI landscape in histopathology.NPJ digital medicine · 2025Article
- Mismatch repair deficiency and its relationship with histopathological features in gastric cancer patients.Nagoya journal of medical science · 2025Article
- Machine learning methods for histopathological image analysis: Updates in 2024.Computational and structural biotechnology journal · 2025Review
- Detecting early gastrointestinal polyps in histology and endoscopy images using deep learning.Frontiers in artificial intelligence · 2025Article
- Deep learning application in prediction of cancer molecular alterations based on pathological images: a bibliographic analysis via CiteSpace.Journal of cancer research and clinical oncology · 2024Article
- Deep learning-driven diagnosis of multi-type vertebra diseases based on computed tomography images.Quantitative imaging in medicine and surgery · 2024Article
- Applications of artificial intelligence in digital pathology for gastric cancer.Frontiers in oncology · 2024Review
- Article
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- Artificial intelligence assists precision medicine in cancer treatment.Frontiers in oncology · 2022Review
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Gastric cancer possesses great histological and molecular diversity, which creates obstacles for rapid and efficient diagnoses. Classic diagnoses either depend on the pathologist's judgment, which relies heavily on subjective experience, or time-consuming molecular assays for subtype diagnosis. Here, we present a deep learning (DL) system to achieve interpretable tumor differentiation grade and microsatellite instability (MSI) recognition in gastric cancer directly using hematoxylin-eosin (HE) staining whole-slide images (WSIs). WSIs from 467 patients were divided into three cohorts: the training cohort with 348 annotated WSIs, the testing cohort with 88 annotated WSIs, and the integration testing cohort with 31 original WSIs without tumor contour annotation. First, the DL models comprehensibly achieved tumor differentiation recognition with an F1 values of 0.8615 and 0.8977 for poorly differentiated adenocarcinoma (PDA) and well-differentiated adenocarcinoma (WDA) classes. Its ability to extract pathological features about the glandular structure formation, which is the key to distinguishing between PDA and WDA, increased the interpretability of the DL models. Second, the DL models achieved MSI status recognition with a patient-level accuracy of 86.36% directly from HE-stained WSIs in the testing cohort. Finally, the integrated end-to-end system achieved patient-level MSI recognition from original HE staining WSIs with an accuracy of 83.87% in the integration testing cohort with no tumor contour annotation. The proposed system, therefore, demonstrated high accuracy and interpretability, which can potentially promote the implementation of artificial intelligence healthcare.
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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.