ArticleEuropean radiology2024
Diffusion MRI is valuable in brainstem glioma genotyping with quantitative measurements of white matter tracts.
Article in European radiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 2 of them syntheses that pooled it.
What it found
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Who cites it
4 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Machine learning in neuroimaging for predicting H3K27M mutations in diffuse midline gliomas: a systematic review and meta-analysis.Frontiers in medicine · 2026Pooled it
- Machine learning radiomics for H3K27M mutation prediction in gliomas: A systematic review and meta-analysis.Neuroradiology · 2025Pooled it
- Integrating clinical features withBMC medical imaging · 2025Article
- Novel Imaging Approaches for Glioma Classification in the Era of the World Health Organization 2021 Update: A Scoping Review.Cancers · 2024Article
Corrections and comments
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Authors and funding
11 authors.
Funding
Abstract
objectivesTo investigate the value of diffusion MRI (dMRI) in H3K27M genotyping of brainstem glioma (BSG).
methodsA primary cohort of BSG patients with dMRI data (b = 0, 1000 and 2000 s/mm
resultsAt total of 117 patients (80 H3K27M-mutant) were included in the primary cohort. In total, 29 tumor histogram features and 41 WM tract measurements were selected for subsequent genotyping model construction. Incorporating WM tract measurements significantly improved diagnostic performances (p < 0.05). The model incorporating tumor and WM features from both DKI and DTI metrics showed the best performance (AUC = 0.9311). The nomogram combining this dMRI model and clinical variables achieved AUCs of 0.9321 and 0.8951 in the primary and validation cohort respectively.
conclusionsdMRI is valuable in BSG genotyping. Tumor diffusion histogram features are useful in genotyping, and WM tract measurements are more valuable in improving genotyping performance. CLINICAL RELEVANCE STATEMENT: This study found that diffusion MRI is valuable in predicting H3K27M mutation in brainstem gliomas, which is helpful to realize the noninvasive detection of brainstem glioma genotypes and improve the diagnosis of brainstem glioma. KEY POINTS: • Diffusion MRI has significant value in brainstem glioma H3K27M genotyping, and models with satisfactory performances were built. • Whole-tumor diffusion histogram features are useful in H3K27M genotyping, and quantitative measurements of white matter tracts are valuable as they have the potential to improve model performance. • The model combining the most discriminative diffusion MRI model and clinical variables can help make clinical decision.
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Registered trials
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