Histogram Analysis of Advanced Diffusion-weighted MRI Models for Evaluating the Grade and Proliferative Activity of Meningiomas.

RATIONALE AND OBJECTIVES

To explore the value of diffusion tensor imaging (DTI), diffusion kurtosis imaging (DKI), neurite orientation dispersion and density imaging (NODDI), and mean apparent propagator (MAP) magnetic resonance imaging histogram analysis in evaluating the grade and proliferative activity of meningiomas.

MATERIALS AND METHODS

A total of 134 meningioma patients were prospectively included and underwent magnetic resonance diffusion imaging. The whole-tumor histogram parameters were extracted from multiple functional maps. Mann-Whitney U test was used to compare the histogram parameters of high- and low-grade meningiomas. The receiver operating characteristic (ROC) curve and multiple logistic regression analysis were used to evaluate the diagnostic efficacy. The correlation between histogram parameters and the Ki-67 index was analyzed. The diffusion model was further validated with an independently validation set (n = 33).

RESULTS

Among single histogram parameters, the variance of NODDI-ISOVF (isotropic volume fraction) showed the highest AUC of 0.829 in grading meningiomas. For the combined models, the DKI model had the best performance in the diagnosis (AUC=0.925). Delong test showed the DKI combined model showed superior diagnostic performance to those of DTI, NODDI and MAP models (P < 0.05 for all). Moreover, moderate to weak correlations were found between various diffusion parameters and the Ki-67 labeling index (rho=0.20-0.45, P < 0.05 for all). In the validation set, the DKI model still showed higher performance (AUC, 0.85) than other diffusion models, thus demonstrating robustness.

CONCLUSIONS

Whole-tumor histogram analyses of DTI, DKI, NODDI, and MAP are useful for evaluating the grade and cellular proliferation of meningiomas. DKI combined model has higher diagnostic accuracy than DTI, NODDI and MAP in meningioma grading.