ArticleNature communications2023
Neuropathologist-level integrated classification of adult-type diffuse gliomas using deep learning from whole-slide pathological images.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers, 1 of them a synthesis that pooled it.
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Who cites it
42 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The Performance of Artificial Intelligence in Classifying Molecular Markers in Adult-Type Gliomas Using Histopathological Images: Systematic Review.Journal of medical Internet research · 2026Pooled it
- Integrating Multimodal MRI Habitat and Transformer-Based Pathomics to Predict High-Risk Molecular Subtypes and Explore Biological Mechanisms in Adult Diffuse Gliomas.CNS neuroscience & therapeutics · 2026Article
- Classification of histology and molecular subtypes of brain gliomas and glioneuronal and neuronal tumors using a deep learning approach.European radiology · 2026Article
- Accurate pathologic classification of aggressive myocarditis improves insights into patient precise medicine.Fundamental research · 2026Article
- Navigating the Artificial Intelligence Revolution in Clinical Neurology: A New Multidisciplinary Task Force Within the European Academy of Neurology.European journal of neurology · 2026Article
- Natural history study of adults with primary central nervous system tumors: Initial results for first 1000 participants.Neuro-oncology practice · 2026Article
- Multimodal integration of radiomics, pathomics, and clinical data enhances grading of adult diffuse gliomas using machine learning.Neuroradiology · 2026Article
- Decoding Glioblastoma Complexity Through Extracellular Vesicles, Organ-on-Chip Models, and Deep Learning.Cells · 2026Review
- A lightweight ResNet50V2-ECA model for renal cell carcinoma grading: efficiency, calibration, and state-of-the-art performance.Scientific reports · 2026Article
- Deep learning prediction of pathological complete response in breast cancer using Mamba architecture.NPJ digital medicine · 2026Article
- Hetairos is a histology-based artificial intelligence model for predicting central nervous system tumor methylation subtypes.Nature cancer · 2026Article
- Raman spectroscopy as a diagnostic tool for glioma: protocol for a systematic review.Diagnostic and prognostic research · 2026Review
- Image Feature Fusion of Hyperspectral Imaging and MRI for Automated Subtype Classification and Grading of Adult Diffuse Gliomas According to the 2021 WHO Criteria.Diagnostics (Basel, Switzerland) · 2026Article
- Artificial intelligence in digital pathology diagnosis and analysis: technologies, challenges, and future prospects.Military Medical Research · 2026Review
- A Deep Learning-Generated Mixed Tumor-Stroma Ratio for Prognostic Stratification and Multi-omics Profiling in Bladder Cancer.Research (Washington, D.C.) · 2026Article
- AI-driven WHO 2021 classification of gliomas based only on H&E-stained slides.Neuro-oncology · 2026Article
- Adult-type diffuse glioma prediction using MnasNet optimized by the advanced single candidate optimizer.Frontiers in oncology · 2026Article
- Recent applications of artificial intelligence in cancer radiotherapy and immunotherapy: current status and future directions.Frontiers in immunology · 2026Review
- AI-Powered Histology for Molecular Profiling in Brain Tumors: Toward Smart Diagnostics from Tissue.Cancers · 2025Review
- A foundation model for rock thin-section images analysis.Communications engineering · 2025Article
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Authors and funding
35 authors.
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Abstract
Current diagnosis of glioma types requires combining both histological features and molecular characteristics, which is an expensive and time-consuming procedure. Determining the tumor types directly from whole-slide images (WSIs) is of great value for glioma diagnosis. This study presents an integrated diagnosis model for automatic classification of diffuse gliomas from annotation-free standard WSIs. Our model is developed on a training cohort (n = 1362) and a validation cohort (n = 340), and tested on an internal testing cohort (n = 289) and two external cohorts (n = 305 and 328, respectively). The model can learn imaging features containing both pathological morphology and underlying biological clues to achieve the integrated diagnosis. Our model achieves high performance with area under receiver operator curve all above 0.90 in classifying major tumor types, in identifying tumor grades within type, and especially in distinguishing tumor genotypes with shared histological features. This integrated diagnosis model has the potential to be used in clinical scenarios for automated and unbiased classification of adult-type diffuse gliomas.
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