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

Purpose Diffuse 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. Despite their clinical urgency, non-invasive differentiation remains challenging due to overlapping conventional MRI features and the difficulty of obtaining tissue diagnosis from eloquent midline locations. Methods This 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 ^trans , V _p , V _e ) were computed and compared between the midline tumor types. Statistical analyses included Shapiro-Wilk test, t-test, and ROC curve analysis using perfusion parameters. Machine learning-based classification was also performed using four classifiers and 5-fold cross-validation, evaluating all possible feature combinations among the best features from the perfusion parameters. Results The 95th percentile values of perfusion parameters demonstrated superior discriminative capability between mGBM and DMG. DMG exhibited significantly lower perfusion parameter values compared to mGBM (p < 0.05). Individual perfusion parameters, particularly rCBF, rCBV, V _e showed discriminative performance achieving AUC values ranging from 70.62% to 75.31%, for differentiating mGBM vs DMG. Machine learning classifiers used these features for evaluating 7 combinations. Three parameter combination (rCBV + rCBF + V _e ) using RF achieved highest cross-validation accuracy (76.67 ± 7.16%) with consistent sensitivity (80.00%) across all models. Conclusions Quantitative 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.