Diagnostic Performance of Microstructural Parameters Derived From Time-dependent Diffusion MRI for Grading Meningiomas: A Prospective Study.

RATIONALE AND

OBJECTIVES

Preoperative meningioma grading is crucial for therapeutic planning and prognosis. This study aimed to prospectively evaluate microstructural parameters derived from time-dependent diffusion MRI (td-dMRI) for preoperative grading of low-grade meningiomas (LGMs) and high-grade meningiomas (HGMs), and to assess meningioma proliferative activity.

MATERIALS AND

METHODS

In this prospective study, 214 meningioma patients underwent td-dMRI using pulsed and oscillating gradient diffusion sequences. Diffusion data were fitted using an optimized model to obtain intracellular volume fraction (fin), cell diameter, cellularity, and extracellular diffusivity (Dex). Patients were divided into two cohorts based on time: a derivation cohort (N = 159) and an independent validation cohort (N = 55). Receiver operating characteristic (ROC) analysis and logistic regression assessed diagnostic performance and defined optimal cut-offs in the derivation cohort. Pre-specified cut-offs were tested in the validation cohort. Spearman's rank correlations were calculated between td-dMRI-derived parameters, Ki-67 index, and histology-based measurements.

RESULTS

HGMs showed significantly higher fin and cellularity, and lower diameter and Dex than LGMs in tumor solid regions (all P < 0.001). In the derivation cohort, fin showed the highest AUC for grading (0.936). The combined model achieved a higher AUC (0.951). Both fin and cellularity showed strong positive correlations with the Ki-67 index (r=0.711-0.721), whereas diameter and Dex showed negative correlations (r=-0.663 to -0626). In the validation cohort, pre-specified cutoffs achieved similar AUCs for grading meningiomas. The td-dMRI-derived parameters correlated strongly with the histology-based measurements (r=0.641-0.773).

CONCLUSION

Td-dMRI-derived microstructural parameters provide promising, non-invasive biomarkers for preoperative grading of LGMs and HGMs, with consistent diagnostic performance in both derivation and validation cohorts.