European Radiology

, Volume 28, Issue 4, pp 1748–1755 | Cite as

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

  • Xi-Xun Qi
  • Da-Fa Shi
  • Si-Xie Ren
  • Su-Ya Zhang
  • Long Li
  • Qing-Chang Li
  • Li-Ming GuanEmail author



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.

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.


Glioma Magnetic resonance imaging Diffusion kurtosis imaging Histogram analysis Pathological grade 



axial diffusivity


axial kurtosis


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


fractional anisotropy


FMRIB's Diffusion Toolbox


full width at half maximum


kurtosis fractional anisotropy


mean diffusivity


mean kurtosis


magnetic resonance imaging


negative predictive value


radial diffusivity


radial kurtosis


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.

Informed consent

Written informed consent was not required for this study because all patients had signed the hospitalised informed consents.

Ethical approval

Institutional Review Board approval was not required because the study does not involve ethical issues.


• retrospective

• diagnostic or prognostic study

• performed at one institution


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Copyright information

© European Society of Radiology 2017

Authors and Affiliations

  1. 1.Department of RadiologyFirst Affiliated Hospital of China Medical UniversityShenyangChina
  2. 2.Department of RadiologyFirst Affiliated Hospital of Yangtze UniversityJingzhouChina
  3. 3.Department of RadiologyChengdu Second People’s HospitalChengduChina
  4. 4.Department of NeurosurgeryFirst Affiliated Hospital of China Medical UniversityShenyangChina
  5. 5.Department of PathologyFirst Affiliated Hospital of China Medical UniversityShenyangChina

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