Multiparametric Quantitative MRI With 2. 5D Deep Learning for Molecular Classification and Overall Survival Assessment in Adult‐Type Diffuse Gliomas

ABSTRACT Background Quantitative MRI may enable noninvasive glioma characterization, but optimal analytic strategies and prognostic utility remain uncertain. Purpose To compare volume‐of‐interest (VOI)‐mean, radiomics, and 2D, 2.5D, and 3D deep learning (DL) for preoperative molecular classification and assess the prognostic value of a 2.5D‐DL–derived isocitrate dehydrogenase wild‐type score (2.5D‐IDHwt score) in a postoperative clinicopathologic survival model. Study Type Retrospective multicenter study. Population One thousand four hundred and eight adults with diffuse glioma: training ( n = 409), temporal validation ( n = 300), and three external test cohorts ( n = 119, 310, and 270). Field Strength/Sequence 3 T; spin‐echo echo‐planar diffusion‐weighted imaging, 3D pseudo‐continuous arterial spin labeling, and multidynamic multiecho synthetic MRI. Assessment Apparent diffusion coefficient, cerebral blood flow, T1, T2, proton‐density, and contrast‐enhanced T1 maps were generated. Two neuroradiologists (5 and 20 years' experience) delineated whole‐tumor VOIs. Six map‐specific models were stacked separately for VOI‐mean, radiomics, 2D‐DL, 2.5D‐DL, and 3D‐DL. 2.5D‐qMR‐Clin combined the 2.5D‐IDHwt score with age, O 6 ‐methylguanine‐DNA methyltransferase promoter methylation, and extent of resection and was compared with PathoClin incorporating pathological IDH status. Statistical Tests Area under the curve (AUC), DeLong tests, Harrell C‐index, bootstrap resampling, calibration, decision‐curve analysis, Kaplan–Meier analysis, and Cox regression. p < 0.05 indicated significance. Results The 2.5D‐DL stacking model achieved the highest external AUCs for IDH mutation (0.89–0.93) and 1p/19q codeletion (0.92–0.95), significantly outperforming the other four stacking models. Reverse Kaplan–Meier median follow‐up was 1344 days (95% CI, 1316–1383); 27.5% died. External C‐indices for 2.5D‐qMR‐Clin were 0.750, 0.778, and 0.762 versus 0.704, 0.722, and 0.723 for PathoClin, with significant differences in test cohorts 2 and 3. Fixed‐cutoff risk stratification was significant across all external cohorts (hazard ratios, 5.30–5.80). A web‐based 2.5D‐qMR‐Clin explorer provided 1‐, 2‐, and 3‐year OS estimates. Data Conclusion Among evaluated approaches, 2.5D‐DL stacking performed best for molecular classification, and the 2.5D‐IDHwt score improved OS assessment in the postoperative clinicopathologic model. Evidence Level 3. Technical Efficacy Stage 2.