Histogram analysis of diffusion kurtosis imaging derived maps may distinguish between low and high grade gliomas before surgery
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To investigate the value of histogram analysis of diffusion kurtosis imaging (DKI) maps in the evaluation of glioma grading.
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).
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.
DKI parameters can effectively distinguish between low- and high-grade gliomas. Mean MK is the best independent predictor of differentiating glioma grades.
• 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.
KeywordsGlioma Magnetic resonance imaging Diffusion kurtosis imaging Histogram analysis Pathological grade
area under the receiver operating characteristic curve
Central Brain Tumor Registry of the United States
central nervous system
Diffusion Kurtosis Estimator
diffusion kurtosis imaging
diffusion tensor imaging
diffusion weighted imaging
FMRIB's Diffusion Toolbox
full width at half maximum
kurtosis fractional anisotropy
magnetic resonance imaging
negative predictive value
World Health Organization
This study has received funding by National Natural Science Foundation of China (No. 81101035).
Compliance with ethical standards
The scientific guarantor of this publication is Xu Ke.
Conflict of interest
The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article.
Statistics and biometry
No complex statistical methods were necessary for this paper.
Written informed consent was not required for this study because all patients had signed the hospitalised informed consents.
Institutional Review Board approval was not required because the study does not involve ethical issues.
• diagnostic or prognostic study
• performed at one institution
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