ArticleNature medicine2024
Prediction of DNA methylation-based tumor types from histopathology in central nervous system tumors with deep learning.
Article in Nature medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers, 1 of them a synthesis that pooled it.
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Who cites it
34 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine learning applications in predicting pharmacological treatment outcomes and responses in esophageal cancer.Frontiers in pharmacology · 2026Pooled it
- Modern molecular profiling recontextualizes the NRG/RTOG 0539 trial and reveals hidden high-risk and radiotherapy-resistant meningiomas.Neuro-oncology · 2026Trial
- Time-dependent prognostic value of automated Ki67 assessment and its integration with molecular risk profiling in WHO grade 2 meningioma.Acta neuropathologica communications · 2026Article
- Machine learning-driven cancer diagnostics with improved robustness and interpretability.Chemical science · 2026Review
- A unified vision-language model for precision oncology and biomarker prediction in neuroblastoma.Nature communications · 2026Article
- Multidisciplinary management of meningiomas in the era of precision oncology.Nature reviews. Clinical oncology · 2026Review
- Hetairos is a histology-based artificial intelligence model for predicting central nervous system tumor methylation subtypes.Nature cancer · 2026Article
- Systematically decoding pathological morphologies and molecular profiles with unified multimodal embedding.Nature methods · 2026Article
- Deep learning inference of cell type-specific gene expression from breast tumor histopathology.NPJ precision oncology · 2026Article
- Prognostic significance and immune correlation of STING expression and promoter methylation in renal cell carcinoma.Scientific reports · 2026Article
- DNA methylation profiling in central nervous system tumors: Where do we draw the line of clinical utility?World journal of experimental medicine · 2026Article
- AI-Based Methods in Neuropathology for Diagnosis and Treatment of Brain Tumors.European journal of neurology · 2026Review
- Discovery of predictive biomarkers for cancer therapy through computational approaches.Nature reviews. Clinical oncology · 2026Review
- AI-Powered Histology for Molecular Profiling in Brain Tumors: Toward Smart Diagnostics from Tissue.Cancers · 2025Review
- Path2Omics Enhances Transcriptomic and Methylation Prediction Accuracy from Tumor Histopathology.Cancer research · 2025Article
- Association of DRD4 promoter-associated methylation with mild cognitive impairment in Han and Uygur populations.European journal of medical research · 2025Article
- AI-augmented intraoperative decision-making workflows in diffuse midline glioma biopsy using cryosection pathology.Nature communications · 2025Article
- 3D spatial sampling to quantify morphologic heterogeneity in isocitrate dehydrogenase-wildtype glioblastoma.Journal of neuropathology and experimental neurology · 2025Article
- Artificial intelligence in cancer: applications, challenges, and future perspectives.Molecular cancer · 2025Review
- Pathologist-interpretable breast cancer subtyping and stratification from AI-inferred nuclear features.bioRxiv : the preprint server for biology · 2025Article
Corrections and comments
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Authors and funding
11 authors.
Funding
Abstract
Precision in the diagnosis of diverse central nervous system (CNS) tumor types is crucial for optimal treatment. DNA methylation profiles, which capture the methylation status of thousands of individual CpG sites, are state-of-the-art data-driven means to enhance diagnostic accuracy but are also time consuming and not widely available. Here, to address these limitations, we developed Deep lEarning from histoPathoLOgy and methYlation (DEPLOY), a deep learning model that classifies CNS tumors to ten major categories from histopathology. DEPLOY integrates three distinct components: the first classifies CNS tumors directly from slide images ('direct model'), the second initially generates predictions for DNA methylation beta values, which are subsequently used for tumor classification ('indirect model'), and the third classifies tumor types directly from routinely available patient demographics. First, we find that DEPLOY accurately predicts beta values from histopathology images. Second, using a ten-class model trained on an internal dataset of 1,796 patients, we predict the tumor categories in three independent external test datasets including 2,156 patients, achieving an overall accuracy of 95% and balanced accuracy of 91% on samples that are predicted with high confidence. These results showcase the potential future use of DEPLOY to assist pathologists in diagnosing CNS tumors within a clinically relevant short time frame.
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Registered trials
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.