ArticleNPJ precision oncology2020
Classification and mutation prediction based on histopathology H&E images in liver cancer using deep learning.
Article in NPJ precision oncology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 132 papers, 1 of them a synthesis that pooled it.
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Who cites it
132 citing papers in PubMed, 1 synthesis or guideline pooled it, 242 citations in OpenAlex.
- Transforming histologic assessment: artificial intelligence in cancer diagnosis and personalized treatment.British journal of cancer · 2025Pooled it
- Artificial Intelligence for Molecular Biomarker Identification in Gastrointestinal and Hepatobiliary Cancers.International journal of molecular sciences · 2026Review
- Development of an automated artificial intelligence-based tool for reticulin fibrosis assessment in bone marrow biopsies.Virchows Archiv : an international journal of pathology · 2026Article
- Bridging traditional and deep learning methods in H&E histological image normalization: a comprehensive review and introducing a novel framework for comparative analyses.Journal of advanced research · 2026Review
- A deep learning framework for histopathological analysis of pixel-level extracellular matrix variation in standard H&E-stained images.Scientific reports · 2026Article
- Prediction of OncotypeDX recurrence score using hematoxylin and eosin-stained whole slide images.NPJ breast cancer · 2026Article
- Deep learning on histopathological images to predict breast cancer recurrence risk and chemotherapy benefit: a multicentre, model development and validation study.The Lancet. Oncology · 2026Article
- His-MMDM: Multi-Domain and Multi-Omics Translation of Histopathological Images with Diffusion Models.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Artificial Intelligence in Hepatocellular Carcinoma: Current Applications, Clinical Performance, and Barriers to Implementation.Journal of clinical medicine · 2026Review
- WNT-β-catenin signalling in hepatocellular carcinoma: from bench to clinical trials.Nature reviews. Gastroenterology & hepatology · 2026Review
- Advances in predicting omics profiles from imaging data.Briefings in bioinformatics · 2026Review
- Artificial intelligence and digital transformation of gastroenterology and hepatology: A critical review of clinical applications and future challenges.World journal of hepatology · 2026Review
- Transcriptomic-guided whole-slide image classification for molecular subtype identification.PLoS computational biology · 2026Article
- Novel growth pattern-specific digital marker of TILs improves stratification of lung adenocarcinoma patients.The Journal of pathology · 2026Article
- Automatic clear cell renal cell carcinoma grading framework using histopathological images via artificial intelligence: a benchmarking study.BioData mining · 2026Article
- Insights into artificial intelligence-based digital pathology in hepatobiliary and pancreatic cancer.Frontiers in oncology · 2026Review
- Path2Omics Enhances Transcriptomic and Methylation Prediction Accuracy from Tumor Histopathology.Cancer research · 2025Article
- Integrated ultrasound radiomics and clinical data to predict PD-1 blockade efficacy in unresectable hepatocellular carcinoma.BMC gastroenterology · 2025Article
- Development of a PANoptosis-Related Pathomics Prognostic Model in Ovarian Cancer: A Multi-Omics Study.Journal of cellular and molecular medicine · 2025Article
- A pathomics-integrated multimodal model to evaluate chemoimmunotherapy efficacy in unresectable intrahepatic cholangiocarcinoma.JHEP reports : innovation in hepatology · 2025Article
72 more citing papers are in PubMed but not listed here.
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Authors and funding
9 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Hepatocellular carcinoma (HCC) is the most common subtype of liver cancer, and assessing its histopathological grade requires visual inspection by an experienced pathologist. In this study, the histopathological H&E images from the Genomic Data Commons Databases were used to train a neural network (inception V3) for automatic classification. According to the evaluation of our model by the Matthews correlation coefficient, the performance level was close to the ability of a 5-year experience pathologist, with 96.0% accuracy for benign and malignant classification, and 89.6% accuracy for well, moderate, and poor tumor differentiation. Furthermore, the model was trained to predict the ten most common and prognostic mutated genes in HCC. We found that four of them, including
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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.