ArticleNeuroradiology2026
Non-invasive differentiation of diffuse midline glioma and midline glioblastoma using DCE-MRI perfusion parameters and machine learning classification in pediatric and young adult patients.
Article in Neuroradiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
purposeDiffuse midline glioma (DMG) and midline glioblastoma (mGBM) are aggressive WHO grade 4 tumors with comparable median survival of 12-18 months but require fundamentally different therapeutic approaches. DMG is molecularly defined by H3 K27-alteration, whereas mGBM lacks this alteration, however, non-invasive differentiation remains challenging due to overlapping conventional MRI features and the difficulty of obtaining tissue diagnosis from eloquent midline locations.
methodsThis retrospective study included 62 patients with histologically confirmed midline gliomas (30 mGBM, 32 DMG) evaluated with 3T MRI. Quantitative DCE-MRI perfusion parameters (rCBV, rCBF, Slope-2, K
resultsAmong the statistical features extracted from each parametric map, the 95
conclusionsQuantitative DCE-MRI perfusion analysis provides significant diagnostic value for differentiating DMG from mGBM, offering a non-invasive alternative when tissue diagnosis is not obtained. Both individual parameters and optimized multi-parametric approaches demonstrate clinically useful performance for guiding treatment decisions.
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