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European Radiology

Erschienen in:

16.11.2017 | Neuro

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

verfasst von: Xi-Xun Qi, Da-Fa Shi, Si-Xie Ren, Su-Ya Zhang, Long Li, Qing-Chang Li, Li-Ming Guan

Erschienen in: European Radiology | Ausgabe 4/2018

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Abstract

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.

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Titel: Histogram analysis of diffusion kurtosis imaging derived maps may distinguish between low and high grade gliomas before surgery
verfasst von: Xi-Xun Qi
Da-Fa Shi
Si-Xie Ren
Su-Ya Zhang
Long Li
Qing-Chang Li
Li-Ming Guan
Publikationsdatum: 16.11.2017
Verlag: Springer Berlin Heidelberg
Erschienen in: European Radiology / Ausgabe 4/2018
Print ISSN: 0938-7994
Elektronische ISSN: 1432-1084
DOI: https://doi.org/10.1007/s00330-017-5108-1

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

Abstract

Objective

Methods

Result

Conclusions

Key points

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Springer Medizin

Abstract

Objective

Methods

Result

Conclusions

Key points

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