Multiparametric Quantitative MRI With 2.5D Deep Learning for Molecular Classification and Overall Survival Assessment in Adult-Type Diffuse Gliomas.

Published in Journal of magnetic resonance imaging : JMRI. Abstract: Quantitative MRI may enable noninvasive glioma characterization, but optimal analytic strategies and prognostic utility remain uncertain. 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. Retrospective multicenter study. 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). 3 T; spin-echo echo-planar diffusion-weighted imaging, 3D pseudo-continuous arterial spin labeling, and multidynamic multiecho synthetic MRI. 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. 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. The ...