What question did this study set out to answer?

This study aims to assess the effectiveness of an optimized time-dependent diffusion MRI for predicting molecular subtypes of adult diffuse gliomas preoperatively.

May 9, 2026Open Access

Optimized Time-Dependent Diffusion MRI for Preoperative Molecular Subtyping of Adult Diffuse Gliomas

Key Points

This study aims to assess the effectiveness of an optimized time-dependent diffusion MRI for predicting molecular subtypes of adult diffuse gliomas preoperatively.
Adults with suspected tumors underwent preoperative 3.0-T MRI with TDD-MRI using OGSE and PGSE acquisitions.
Bayesian IMPULSED mapping was employed to derive microstructural parameters and evaluate diagnostic performance.
Receiver operating characteristic analysis and logistic regression were used for performance evaluation.
TDD-MRI effectively differentiated IDH-wildtype glioblastoma and IDH-mutant oligodendroglioma with high AUCs (0.95, 95% CI: 0.91-0.99).
All microstructural parameters showed significant differences, with IDHwt demonstrating higher ADC_ratio and lower extracellular diffusivity.
Imaging-derived parameters correlated strongly with pathology, with r-values between 0.684 and 0.769, all P<0.001.

Abstract

BACKGROUND AND PURPOSE: Isocitrate dehydrogenase (IDH) mutation and 1p/19q codeletion guide preoperative management in adult diffuse gliomas but are usually unknown before tissue sampling, and conventional ADC-based surrogates lack specificity. We evaluated whether an optimized time-dependent diffusion MRI (TDD-MRI) protocol integrating oscillating-gradient spin-echo (OGSE) and pulsed-gradient spin-echo (PGSE) acquisitions with Bayesian IMPULSED mapping can predict these markers preoperatively and validated imaging indices against digital histopathology. MATERIALS AND METHODS: In this study (February 2023-December 2024), adults with suspected intracranial tumors underwent preoperative 3.0-T MRI with high-performance gradients. TDD-MRI included OGSE (20 and 40-Hz) and PGSE acquisitions. Bayesian IMPULSED generated voxelwise extracellular diffusivity (Dex), intracellular volume fraction (fin), cell diameter (Diameter), and Cellularity; multi-time ADC and ADCratio were also computed. Diagnostic performance was evaluated using logistic regression and receiver-operating-characteristic analysis. Imaging-pathology associations were evaluated using digital hematoxylin-and-eosin metrics. RESULTS: Among 119 adult diffuse gliomas (mean age, 51.9±9.1 years), tumors were classified as IDH-wildtype glioblastoma (IDHwt; n=53), IDH-mutant oligodendroglioma with 1p/19q codeletion (IDHmut-codel; n=33), or IDH-mutant astrocytoma with intact 1p/19q (IDHmut-intact; n=33). The intra- and inter-observer reproducibility of ROI measurements by the radiologists was excellent (intraclass correlation coefficients, 0.89-0.97). All microstructural parameters showed significant group differences on overall analysis (Pratio, fin, and Cellularity and lower Dex than other groups, whereas IDHmut-codel had the lowest Cellularity. Among single parameters, ADCratio and Cellularity performed best. Multivariable models achieved AUC 0.95 (95% CI: 0.91-0.99) for identifying IDHmut-codel and 0.95 (95% CI: 0.92-0.99) for IDHwt, each outperforming single-time ADC (Pfin, Diameter, and Cellularity correlated with pathology (r = 0.684-0.769, all PCONCLUSIONS: Optimized TDD-MRI with Bayesian IMPULSED provided quantitative microstructural mapping with strong histopathologic concordance and showed promise for the preoperative molecular subtyping of adult diffuse gliomas.

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