ArticleWorld journal of gastroenterology2021
Prediction of genetic alterations from gastric cancer histopathology images using a fully automated deep learning approach.
Article in World journal of gastroenterology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed, 23 citations in OpenAlex.
- Artificial Intelligence for Molecular Biomarker Identification in Gastrointestinal and Hepatobiliary Cancers.International journal of molecular sciences · 2026Review
- Whole-slide deep-learning quantification of fibrosis and pathologist-defined proliferative/neoplastic regions in a thioacetamide-induced rat liver model.Frontiers in artificial intelligence · 2026Article
- Artificial intelligence for biomarker prediction in gastric cancer: from histopathology to multimodal integration.Frontiers in oncology · 2026Review
- AI-Driven Digital Pathology: Deep Learning and Multimodal Integration for Precision Oncology.International journal of molecular sciences · 2025Review
- A robust artificial intelligence system for predicting EBV status in gastric cancer biopsy and resection specimens.Scientific reports · 2025Article
- Beyond Biomarkers: Machine Learning-Driven Multiomics for Personalized Medicine in Gastric Cancer.Journal of personalized medicine · 2025Review
- An interpretable framework for gastric cancer classification using multi-channel attention mechanisms and transfer learning approach on histopathology images.Scientific reports · 2025Article
- Big Data and Artificial Intelligence in Drug Discovery for Gastric Cancer: Current Applications and Future Perspectives.Current medicinal chemistry · 2025Review
- Integrating transcriptomic data and digital pathology for NRG-based prediction of prognosis and therapy response in gastric cancer.Annals of medicine · 2024Article
- Deep learning-based analysis of EGFR mutation prevalence in lung adenocarcinoma H&E whole slide images.The journal of pathology. Clinical research · 2024Article
- Comprehensive Collection of Whole-Slide Images and Genomic Profiles for Patients with Bladder Cancer.Scientific data · 2024Article
- Applications of artificial intelligence in digital pathology for gastric cancer.Frontiers in oncology · 2024Review
- The Role of TP53, KRAS, CDH1, Demographic and Clinical Variables in Gastric Cancer.Materia socio-medica · 2024Article
- Deep Learning for the Pathologic Diagnosis of Hepatocellular Carcinoma, Cholangiocarcinoma, and Metastatic Colorectal Cancer.Cancers · 2023Article
- Focalizing regions of biomarker relevance facilitates biomarker prediction on histopathological images.iScience · 2023Article
- Optimization of deep learning models for the prediction of gene mutations using unsupervised clustering.The journal of pathology. Clinical research · 2023Article
- Deep Learning-Based Prediction of Molecular Tumor Biomarkers from H&E: A Practical Review.Journal of personalized medicine · 2022Review
- Prediction of Epstein-Barr Virus Status in Gastric Cancer Biopsy Specimens Using a Deep Learning Algorithm.JAMA network open · 2022Article
- Deep learning-based prediction of molecular cancer biomarkers from tissue slides: A new tool for precision oncology.Clinical and molecular hepatology · 2022Review
- The evolving role of morphology in endometrial cancer diagnostics: From histopathology and molecular testing towards integrative data analysis by deep learning.Frontiers in oncology · 2022Review
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundStudies correlating specific genetic mutations and treatment response are ongoing to establish an effective treatment strategy for gastric cancer (GC). To facilitate this research, a cost- and time-effective method to analyze the mutational status is necessary. Deep learning (DL) has been successfully applied to analyze hematoxylin and eosin (H and E)-stained tissue slide images.
aimTo test the feasibility of DL-based classifiers for the frequently occurring mutations from the H and E-stained GC tissue whole slide images (WSIs).
methodsFrom the GC dataset of The Cancer Genome Atlas (TCGA-STAD), wild-type/mutation classifiers for CDH1, ERBB2, KRAS, PIK3CA, and TP53 genes were trained on 360 × 360-pixel patches of tissue images.
resultsThe area under the curve (AUC) for the receiver operating characteristic (ROC) curves ranged from 0.727 to 0.862 for the TCGA frozen WSIs and 0.661 to 0.858 for the TCGA formalin-fixed paraffin-embedded (FFPE) WSIs. The performance of the classifier can be improved by adding new FFPE WSI training dataset from our institute. The classifiers trained for mutation prediction in colorectal cancer completely failed to predict the mutational status in GC, indicating that DL-based mutation classifiers are incompatible between different cancers.
conclusionThis study concluded that DL could predict genetic mutations in H and E-stained tissue slides when they are trained with appropriate tissue data.
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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.