Value of diffusion kurtosis imaging in assessing low‐grade gliomas

• Published in: Journal of magnetic resonance imaging Vol. 48; no. 6; pp. 1551 - 1558
• Main Authors: Goryawala, Mohammed Z., Heros, Deborah O., Komotar, Ricardo J., Sheriff, Sulaiman, Saraf‐Lavi, Efrat, Maudsley, Andrew A.
• Format: Journal Article
• Language: English
• Published: United States Wiley Subscription Services, Inc 01.12.2018
• Subjects: Anisotropy; Attention deficit hyperactivity disorder; Diffusion; diffusion kurtosis imaging; diffusion tensor imaging; Field strength; Glioma; Kurtosis; low‐grade glioma; Magnetic resonance imaging; Mathematical models; perilesional white matter; Population (statistical); Population studies; Sensitivity; Sensitivity analysis; Statistical analysis; Substantia alba; Tensors; perilesional white matter; diffusion kurtosis imaging; diffusion tensor imaging; low-grade glioma
• ISSN: 1053-1807; 1522-2586; 1522-2586
• DOI: 10.1002/jmri.26012

Background Diffusion kurtosis imaging (DKI) measures have been shown to provide increased sensitivity relative to diffusion tensor imaging (DTI) in detecting pathologies. Purpose To compare the sensitivity of DKI‐derived kurtosis and diffusion maps for assessment of low‐grade gliomas (LGG). Study Type Prospective study. Population In all, 19 LGG patients and 26 healthy control subjects were recruited. Field Strength/Sequence Echo‐planar‐imaging diffusion‐weighted MR images (b‐values = 0, 1000, and 2000 with 30 diffusion gradient directions) were acquired on a 3T scanner. Assessment Maps for mean, axial, and radial diffusivity (MD, AD, and RD) and kurtosis (MK, AK, and RK), and fractional anisotropy (FA) were evaluated in the tumor, perilesional white matter, and contralateral normal‐appearing white matter regions. Statistical Testing General linear models (GLM), Cohen's d for effect size estimates, false discovery rate (FDR) for multiple corrections, Cochran Q‐test. Results Pairwise differences were observed for all diffusion and kurtosis measures between the studied regions (FDR P < 0.001), except an FA map that failed to show significant differences between the lesion and perilesional white matter (FDR P = 0.373). Effect size analysis showed that kurtosis metrics were found to be 18.8% (RK, P = 0.144) to 29.1% (AK, P < 0.05) more sensitive in discriminating perilesional regions from the lesion than corresponding diffusion metrics, whereas AK provided a 25.0% (P < 0.05) increase in sensitivity in discriminating perilesional and contralateral white matter. RK was found to be the most sensitive to contralateral white matter differences between low‐grade gliomas and controls, with MK and RK providing a significantly greater sensitivity of 587.2% (P < 0.001) and 320.7% (P < 0.001) than MD and RD, respectively. Data Conclusion Kurtosis maps showed increased sensitivity, as compared to counterpart diffusion maps, for evaluation of microstructural changes in gliomas with a 3–6‐fold increment in assessing changes in contralateral white matter. Level of Evidence: 2 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2018;48:1551–1558