Performance Comparison of Diffusion Kurtosis Imaging (DKI), Neurite Orientation Dispersion and Density Imaging (NODDI), and Diffusion Microstructure Imaging (DMI) in Predicting Adult-Type Glioma Subtype-A Pilot Study.

Background/

Objectives: The purpose of this study was to evaluate the performance of diffusion kurtosis imaging (DKI), neurite orientation dispersion and density imaging (NODDI), and diffusion microstructure imaging (DMI) in differentiating molecular subtypes of adult-type gliomas.

Methods: Standardized MRI was performed and evaluated in 59 patients with adult-type glioma. DKI, NODDI, and DMI parameter values were quantitatively evaluated in ROIs in contrast-enhancing/solid tumor tissue and five concentric shells with peritumoral tissue. DKI, NODDI, and DMI parameters of (i) glioblastomas, Isocitrate dehydrogenase (IDH) wildtype; (ii) astrocytomas, IDH mutant; and (iii) oligodendrogliomas, IDH mutant were compared with analysis of variance (ANOVA). Receiver operating characteristic curve (ROC) curve analysis was conducted to discriminate firstly between IDH mutant and IDH wildtype gliomas and then between IDH mutant astrocytomas and oligodendrogliomas.

Results: Significant differences between the three aforementioned subtypes were found for the apparent diffusion coefficient (ADC) and mean kurtosis (MK) and again for the orientation dispersion index (ODI) and intra-axonal volume fraction (v-intra). The diagnostic accuracy depended on the distance to the contrast-enhancing/solid tumor tissue. Some NODDI and DMI parameters significantly predicted the IDH status and significantly discriminated between astrocytomas and oligodendrogliomas; however, ADC and MK showed the best prediction in both ROC analyses (maximum AUC 0.910 (CI 0.824-0.995)).

Conclusions: The evaluation of peritumoral tissue can be a valuable procedure, while NODDI and DMI appear to be promising but are currently inferior to DKI in predicting glioma subtypes categorized according to the WHO 2021 classification.