Grading of Gliomas by Using Monoexponential, Biexponential, and Stretched Exponential Diffusion-weighted MR Imaging and Diffusion Kurtosis MR Imaging

Abstract

Purpose: To quantitatively compare the potential of various diffusion parameters obtained from monoexponential, biexponential, and stretched exponential diffusion-weighted imaging models and diffusion kurtosis imaging in the grading of gliomas.

Materials and methods: This study was approved by the local ethics committee, and written informed consent was obtained from all subjects. Both diffusion-weighted imaging and diffusion kurtosis imaging were performed in 69 patients with pathologically proven gliomas by using a 3-T magnetic resonance (MR) imaging unit. An isotropic apparent diffusion coefficient (ADC), true ADC, pseudo-ADC, and perfusion fraction were calculated from diffusion-weighted images by using a biexponential model. A water molecular diffusion heterogeneity index and distributed diffusion coefficient were calculated from diffusion-weighted images by using a stretched exponential model. Mean diffusivity, fractional anisotropy, and mean kurtosis were calculated from diffusion kurtosis images. All values were compared between high-grade and low-grade gliomas by using a Mann-Whitney U test. Receiver operating characteristic and Spearman rank correlation analysis were used for statistical evaluations.

Results: ADC, true ADC, perfusion fraction, water molecular diffusion heterogeneity index, distributed diffusion coefficient, and mean diffusivity values were significantly lower in high-grade gliomas than in low-grade gliomas (U = 109, 56, 129, 6, 206, and 229, respectively; P < .05). Pseudo-ADC and mean kurtosis values were significantly higher in high-grade gliomas than in low-grade gliomas (U = 98 and 8, respectively; P < .05). Both water molecular diffusion heterogeneity index (area under the receiver operating characteristic curve [AUC] = 0.993) and mean kurtosis (AUC = 0.991) had significantly greater AUC values than ADC (AUC = 0.866), mean diffusivity (AUC = 0.722), and fractional anisotropy (AUC = 0.500) in the differentiation of low-grade and high-grade gliomas (P < .05).

Conclusion: Water molecular diffusion heterogeneity index and mean kurtosis values may provide additional information and improve the grading of gliomas compared with conventional diffusion parameters.

Figure 1:

High-grade glioblastoma (WHO grade IV) in the left temporal lobe (arrows) in a 61-year-old woman. A, The curves of different fits were derived from DWI by using monoexponential, biexponential, and stretched exponential models and DKI. B, T1-weighted MR image shows that the tumor is hypointense. C, T2-weighted MR image shows that the tumor is hyperintense. D, Gadolinium-based contrast material–enhanced T1-weighted MR image shows that the tumor has irregular enhancement. In the solid tumor component that enhances with gadolinium-based contrast material, E, the ADC map and, F, the ADC_slow map show decreased values. G, The ADC_fast map shows increased values, and, H, the f map, I, α map, J, DDC map, and, K, mean diffusivity map show decreased values. The, L, FA and, M, MK maps show increased values.

Figure 2:

Low-grade astrocytoma (WHO grade II) in the right temporal lobe (arrows) in a 43-year-old woman. A, The curves of different fits were derived from DWI by using monoexponential, biexponential, and stretched exponential models and DKI. B, T1-weighted MR image shows that the tumor is hypointense. C, T2-weighted MR image shows that the tumor is hyperintense. D, Gadolinium-based contrast-enhanced T1-weighted MR image shows that the tumor has no enhancement. In the tumor, E, the ADC map and, F, the ADC_slow map show increased values. G, The ADC_fast map shows decreased values. The, H, f map, I, α map, J, DDC map, and, K, mean diffusivity map show increased values. The, L, FA and, M, MK map show decreased values.

Figure 3:

Bar graphs of ADC, ADC_fast, ADC_slow, f, α, DDC, FA, mean diffusivity (MD), and MK values averaged across high-grade (n = 34) and low-grade (n = 28) gliomas. Error bars = standard deviations across subjects. ADC, ADC_fast, ADC_slow, DDC, and MD are in units of × 10⁻³ mm²/sec. Parameters not marked with asterisks are not significant. ** = P < .01, *** = P < .001.

Figure 4a:

(a) Receiver operating characteristic curves for ADC, ADC_slow, α, DDC, and mean diffusivity (MD) in distinguishing high- from low-grade gliomas. (b) Receiver operating characteristic curves for ADC_fast, MK, and FA in distinguishing high- from low-grade gliomas.

Figure 4b:

(a) Receiver operating characteristic curves for ADC, ADC_slow, α, DDC, and mean diffusivity (MD) in distinguishing high- from low-grade gliomas. (b) Receiver operating characteristic curves for ADC_fast, MK, and FA in distinguishing high- from low-grade gliomas.