The role of MR diffusion kurtosis and neurite orientation dispersion and density imaging in evaluating gliomas

Conventional MRI sequences in neuro-oncology are insufficient for glioma grading. However, newly developed diffusion-weighted imaging techniques have been shown to have a great potential for glioma grading. This study examined the diagnostic performance of diffusion tensor imaging (DTI), diffusion kurtosis imaging (DKI), neurite orientation dispersion and density imaging (NODDI), and their combinations in glioma grading.Multishell diffusion tensor images were obtained with 3T MRI in 38 glioma patients (22 high-grade glioma [HGG], 16 low-grade glioma [LGG]). DTI (fractional anisotropy [FA], mean diffusivity [MD], axial diffusivity [AD], radial diffusivity [RD]), DKI (Axial kurtosis [AK], mean kurtosis [MK], radial kurtosis [RK]), and NODDI (intracellular volume fraction [ICVF], orientation distribution index, isotropic water fraction [ISO]) images were obtained after preprocessing. The average value of these parameters was calculated in the solid components of the tumors. The receiver operating characteristic curve analyses were performed to investigate the diagnostic performance and the curves were compared with the Delong test.FA shows an increase in HGG, while MD, RD, and AD exhibit a decrease. AK, MK, and RK were higher in HGG than LGG. ICVF increased in HGG, while ISO decreased. AK demonstrated the best diagnostic performance among all parameters, and kurtosis outperformed NODDI but not DTI. Combining these parameters did not yield a statistically significant improvement in diagnostic performance.DTI, DKI, and NODDI approaches can differentiate between HGG and LGG; however, kurtosis parameters perform better and adding NODDI parameters does not improve diagnostic performance. Using multishell b-value has not led to an increase in diagnostic performance.