ArticleBrain pathology (Zurich, Switzerland)2024
Morphology-based molecular classification of spinal cord ependymomas using deep neural networks.
Article in Brain pathology (Zurich, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- AOSNP-ADAPTR resource level-based recommendations on practical diagnostic strategies for ependymomas.Brain pathology (Zurich, Switzerland) · 2026Article
- Hetairos is a histology-based artificial intelligence model for predicting central nervous system tumor methylation subtypes.Nature cancer · 2026Article
- Advancements in artificial intelligence for cancer diagnosis and prognosis prediction: current applications and emerging opportunities.Frontiers in cell and developmental biology · 2026Review
- 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
- Morphology-based molecular classification of spinal cord ependymomas using deep neural networks.Brain pathology (Zurich, Switzerland) · 2024Article
- AI-based selection of tumor regions for genomic profiling in neuropathology.Neuro-oncology advancesArticle
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8 authors.
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Abstract
Based on DNA-methylation, ependymomas growing in the spinal cord comprise two major molecular types termed spinal (SP-EPN) and myxopapillary ependymomas (MPE(-A/B)), which differ with respect to their clinical features and prognosis. Due to the existing discrepancy between histomorphogical diagnoses and classification using methylation data, we asked whether deep neural networks can predict the DNA methylation class of spinal cord ependymomas from hematoxylin and eosin stained whole-slide images. Using explainable AI, we further aimed to prospectively improve the consistency of histology-based diagnoses with DNA methylation profiling by identifying and quantifying distinct morphological patterns of these molecular ependymoma types. We assembled a case series of 139 molecularly characterized spinal cord ependymomas (n
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