Gliomas: Diffusion Kurtosis MR Imaging in Grading

Abstract

Purpose:To assess the diagnostic accuracy of diffusion kurtosis magnetic resonance imaging parameters in grading gliomas. Materials and Methods:The institutional review board approved this prospective study, and informed consent was obtained from all patients. Diffusion parameters-mean diffusivity (MD), fractional anisotropy (FA), mean kurtosis, and radial and axial kurtosis-were compared in the solid parts of 17 high-grade gliomas and 11 low-grade gliomas (P , .05 significance level, Mann-Whitney-Wilcoxon test, Bonferroni correction). MD, FA, mean kurtosis, radial kurtosis, and axial kurtosis in solid tumors were also normalized to the corresponding values in contralateral normal-appearing white matter (NAWM) and the contralateral posterior limb of the internal capsule (PLIC) after age correction and were compared among tumor grades. Results:Mean, radial, and axial kurtosis were significantly higher in high-grade gliomas than in low-grade gliomas (P = .02, P = .015, and P = .01, respectively). FA and MD did not significantly differ between glioma grades. All values, except for axial kurtosis, that were normalized to the values in the contralateral NAWM were significantly different between high-grade and low-grade gliomas (mean kurtosis, P = .02; radial kurtosis, P = .03; FA, P = .025; and MD, P = .03). When values were normalized to those in the contralateral PLIC, none of the considered parameters showed significant differences between high-grade and low-grade gliomas. The highest sensitivity and specificity for discriminating between high-grade and low-grade gliomas were found for mean kurtosis (71% and 82%, respectively) and mean kurtosis normalized to the value in the contralateral NAWM (100% and 73%, respectively). Optimal thresholds for mean kurtosis and mean kurtosis normalized to the value in the contralateral NAWM for differentiating high-grade from low-grade gliomas were 0.52 and 0.51, respectively. Conclusion:There were significant differences in kurtosis parameters between high-grade and low-grade gliomas; hence, better separation was achieved with these parameters than with conventional diffusion imaging parameters.q RSNA, 2012 1

Visualizing Non-Gaussian Diffusion: Clinical Application of q-Space Imaging and Diffusional Kurtosis Imaging of the Brain and Spine

“…65 In addition, Van Cauter and associates reported signiˆcant diŠerences in kurtosis parameters between high-and low-grade gliomas using mean, radial, and axial kurtosis values on a 3T MR imaging scanner. 66 In this report, only kurtosis MR imaging parameters showed signiˆcant diŠerences between diŠerent grades of gliomas; FA and mean diŠusivi-ty did not diŠer signiˆcantly between them.…”

Mean Diffusional Kurtosis in Patients with Glioma: Initial Results with a Fast Imaging Method in a Clinical Setting

“…Van Cauter et al 7 examined 28 patients (17 with an HGG and 11 with an LGG, both oligodendroglial and astrocytic types) with a conventional DKI sequence taking 17 minutes 29 seconds and showed a significant difference in MK, nMK, and nMD values between HGGs and LGGs, with areas under the curve of Ͼ0.8 for all 3 values. Median values in the tumor core, the perifocal hyperintensity on T2 FLAIR images, and the contralateral NAGM were similar to ours, but the median value for the contralateral NAWM was considerably lower than ours.…”

“…This heterogeneity is likely to be reflected in MK alterations, and promising first studies have reported MK as an imaging-based means of presurgical glioma grading. 6,7 Larger studies and the more widespread use of DKI, however, have been held back by relatively long acquisition times that result from a model requirement for data to be obtained along several directions at each of many b-values. 8 Such protocols are clearly incompatible with the daily routines in busy clinical imaging departments and are often not tolerable by critically ill patients.…”

Meta-Analysis of Diffusion Metrics for the Prediction of Tumor Grade in Gliomas

“…Unfortunately, promising metrics such as diffusional kurtosis, p:q diffusion tensor decomposition, and maximum SD of FA were provided in too few studies to be accessible by meta-analysis. 19,36,37 Considerable heterogeneity was observed in the cohort of studies for some of the metrics we tested. One source of heterogeneity was sampling error in pathologic specimens used for histologic grading.…”