ArticleNeuro-oncology advances
Direct image to subtype prediction for brain tumors using deep learning.
Article in Neuro-oncology advances. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it, 22 citations in OpenAlex.
- 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
- Hetairos is a histology-based artificial intelligence model for predicting central nervous system tumor methylation subtypes.Nature cancer · 2026Article
- AI-Based Methods in Neuropathology for Diagnosis and Treatment of Brain Tumors.European journal of neurology · 2026Review
- AI-driven WHO 2021 classification of gliomas based only on H&E-stained slides.Neuro-oncology · 2026Article
- A structure-aware and causal-invariant framework for glioma cell classification in pathological images.Frontiers in cell and developmental biology · 2026Article
- Similarity-guided swarm of models: enhancing semi-supervised learning in computational pathology.Scientific reports · 2025Article
- AI-Powered Histology for Molecular Profiling in Brain Tumors: Toward Smart Diagnostics from Tissue.Cancers · 2025Review
- Artificial intelligence-based tools for precision diagnosis and treatment of neurofibromatosis type 1 associated peripheral and central glial tumors.Orphanet journal of rare diseases · 2025Review
- Artificial Intelligence in the Diagnosis and Treatment of Brain Gliomas.Biomedicines · 2025Review
- From whole-slide image to biomarker prediction: end-to-end weakly supervised deep learning in computational pathology.Nature protocols · 2025Article
- Machine learning methods for histopathological image analysis: Updates in 2024.Computational and structural biotechnology journal · 2025Review
- Applications of artificial intelligence in the analysis of histopathology images of gliomas: a review.Npj imaging · 2024Review
- Review
- Interpretable artificial intelligence based determination of glioma IDH mutation status directly from histology slides.Neuro-oncology advancesArticle
- AI-based selection of tumor regions for genomic profiling in neuropathology.Neuro-oncology advancesArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
16 authors at 10 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Deep Learning (DL) can predict molecular alterations of solid tumors directly from routine histopathology slides. Since the 2021 update of the World Health Organization (WHO) diagnostic criteria, the classification of brain tumors integrates both histopathological and molecular information. We hypothesize that DL can predict molecular alterations as well as WHO subtyping of brain tumors from hematoxylin and eosin-stained histopathology slides. Methods: We used weakly supervised DL and applied it to three large cohorts of brain tumor samples, comprising Results: We found that the key molecular alterations for subtyping, Conclusions: In the future, such DL-based implementations could ease diagnostic workflows, particularly for situations in which advanced molecular testing is not readily available.
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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.