Research paperMR diffusion kurtosis imaging predicts malignant potential and the histological type of meningioma

- DKI metrics were found to be significantly different between benign and malignant meningioma.
- MK had the relatively robust specificity and sensibility.
- DKI metrics showed great potential to identical different subtypes of meningioma.
- MK and RK may potentially assess the cellular proliferation.

PurposeTo explore the value of Diffusion kurtosis imaging (DKI) metrics in the differential diagnosis of meningioma.MethodsFor this study, we retrospectively enrolled 35 patients of cerebral meningioma with DKI which included axial diffusion coefficient (AD), radial diffusion coefficient (RD), mean diffusion coefficient (MD), fractional anisotropy (FA), axial kurtosis (AK), radial kurtosis (RK) and mean kurtosis (MK). All of these metrics were normalized according to contralateral normal-appearing white matter (NAWMc). Patients were divided into two groups (benign and malignant meningioma) and were further analyzed using the independent sample t-test and receiver operating characteristic (ROC) curve. A one-way ANOVA analysis was used to analyze four groups divided according to pathological subtypes.ResultsThe metrics of AD, normalized AD, normalized MD, MK and normalized MK showed a significant difference between benign and malignant group, and MK showed relatively higher diagnostic ability with its cut-off value, area under the curve (AUC), sensitivity and specificity of 0.875, 0.780, 70% and 89%, respectively. The metrics of normalized MD, RD and normalized RD, FA and normalized FA, AK and normalized AK, and RK showed significant difference among four subtypes. MK and RK in meningioma were found to correlate positively with the Ki-67 labeling index (Ki-67 LI).ConclusionsDKI metrics may be used to differentiate benign from malignant meningioma, and also to distinguish some histological subtypes of meningioma. Moreover, DKI metrics may potentially reflect cellular proliferation.