ArticleNPJ precision oncology2024
Comparison of DNA methylation based classification models for precision diagnostics of central nervous system tumors.
Article in NPJ precision oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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Who cites it
8 citing papers in PubMed.
- Ependymoma-like tumor with mesenchymal differentiation harboring ZFTA::NCOA2 fusion: a case report.Brain tumor pathology · 2026Article
- Extracellular Vesicles in the Biology and Liquid Biopsy Diagnostics of Pediatric High-Grade Glioma - Emerging Findings and Opportunities.Journal of extracellular biology · 2026Review
- M-PACT leverages cell-free DNA methylomes to achieve robust classification of pediatric brain tumors.Nature cancer · 2026Article
- The contribution of methylation profiling in neuropathological diagnosis of central nervous system tumors in children, adolescent and young adults.Pathologica · 2025Article
- Validation of target-enriched enzymatic methylation sequencing for brain tumor classification from formalin-fixed paraffin embedded-derived DNA.Brain pathology (Zurich, Switzerland) · 2025Article
- Artificial intelligence for comprehensive DNA methylation analysis: overview, challenges, and future directions.Briefings in bioinformatics · 2025Review
- Subtypes detection of papillary thyroid cancer from methylation assay via Deep Neural Network.Computational and structural biotechnology journal · 2025Article
- Enhancing the accuracy of molecular classification of pediatric CNS tumors: a dual-classifier approach using DNA methylation profiling.Frontiers in oncology · 2025Article
Corrections and comments
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Authors and funding
18 authors.
Funding
Abstract
As part of the advancement in therapeutic decision-making for brain tumor patients at St. Jude Children's Research Hospital (SJCRH), we developed three robust classifiers, a deep learning neural network (NN), k-nearest neighbor (kNN), and random forest (RF), trained on a reference series DNA-methylation profiles to classify central nervous system (CNS) tumor types. The models' performance was rigorously validated against 2054 samples from two independent cohorts. In addition to classic metrics of model performance, we compared the robustness of the three models to reduced tumor purity, a critical consideration in the clinical utility of such classifiers. Our findings revealed that the NN model exhibited the highest accuracy and maintained a balance between precision and recall. The NN model was the most resistant to drops in performance associated with a reduction in tumor purity, showing good performance until the purity fell below 50%. Through rigorous validation, our study emphasizes the potential of DNA-methylation-based deep learning methods to improve precision medicine for brain tumor classification in the clinical setting.
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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.