Histogram analysis of diffusion kurtosis imaging derived maps may distinguish between low and high grade gliomas before surgery

• Published in: European radiology Vol. 28; no. 4; pp. 1748 - 1755
• Main Authors: Qi, Xi-Xun, Shi, Da-Fa, Ren, Si-Xie, Zhang, Su-Ya, Li, Long, Li, Qing-Chang, Guan, Li-Ming
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.04.2018; Springer Nature B.V
• Subjects: Adult; Anisotropy; Brain Neoplasms - diagnostic imaging; Brain Neoplasms - pathology; Brain tumors; Correlation coefficient; Correlation coefficients; Diagnostic Radiology; Diagnostic systems; Diffusion Magnetic Resonance Imaging - methods; Diffusion Tensor Imaging - methods; Evaluation; Female; Glioma; Glioma - diagnostic imaging; Glioma - pathology; Histological Techniques; Humans; Imaging; Internal Medicine; Interventional Radiology; Kurtosis; Magnetic resonance imaging; Male; Medicine; Medicine & Public Health; Middle Aged; Neoplasm Grading; Neuro; Neuroradiology; NMR; Nuclear magnetic resonance; Parameter sensitivity; Preoperative Period; Quality; Radiology; ROC Curve; Sensitivity; Sensitivity and Specificity; Surgery; Ultrasound; Glioma; Magnetic resonance imaging; Pathological grade; Diffusion kurtosis imaging; Histogram analysis
• ISSN: 0938-7994; 1432-1084
• DOI: 10.1007/s00330-017-5108-1

Objective To investigate the value of histogram analysis of diffusion kurtosis imaging (DKI) maps in the evaluation of glioma grading. Methods A total of 39 glioma patients who underwent preoperative magnetic resonance imaging (MRI) were classified into low-grade (13 cases) and high-grade (26 cases) glioma groups. Parametric DKI maps were derived, and histogram metrics between low- and high-grade gliomas were analysed. The optimum diagnostic thresholds of the parameters, area under the receiver operating characteristic curve (AUC), sensitivity, and specificity were achieved using a receiver operating characteristic (ROC). Result Significant differences were observed not only in 12 metrics of histogram DKI parameters ( P <0.05), but also in mean diffusivity (MD) and mean kurtosis (MK) values, including age as a covariate (F=19.127, P <0.001 and F=20.894, P <0.001, respectively), between low- and high-grade gliomas. Mean MK was the best independent predictor of differentiating glioma grades (B=18.934, 22.237 adjusted for age, P <0.05). The partial correlation coefficient between fractional anisotropy (FA) and kurtosis fractional anisotropy (KFA) was 0.675 ( P <0.001). The AUC of the mean MK, sensitivity, and specificity were 0.925, 88.5% and 84.6%, respectively. Conclusions DKI parameters can effectively distinguish between low- and high-grade gliomas. Mean MK is the best independent predictor of differentiating glioma grades. Key points • DKI is a new and important method. • DKI can provide additional information on microstructural architecture. • Histogram analysis of DKI may be more effective in glioma grading.