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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
Neuro

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.

Keywords

Glioma Magnetic resonance imaging Diffusion kurtosis imaging Histogram analysis Pathological grade 

Abbreviations

AD

axial diffusivity

AK

axial kurtosis

AUC

area under the receiver operating characteristic curve

CBTRUS

Central Brain Tumor Registry of the United States

CNS

central nervous system

DKE

Diffusion Kurtosis Estimator

DKI

diffusion kurtosis imaging

DTI

diffusion tensor imaging

DWI

diffusion weighted imaging

FA

fractional anisotropy

FDT

FMRIB's Diffusion Toolbox

FWHM

full width at half maximum

KFA

kurtosis fractional anisotropy

MD

mean diffusivity

MK

mean kurtosis

MRI

magnetic resonance imaging

NPV

negative predictive value

RD

radial diffusivity

RK

radial kurtosis

WHO

World Health Organization

Notes

Funding

This study has received funding by National Natural Science Foundation of China (No. 81101035).

Compliance with ethical standards

Guarantor

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.

Methodology

• retrospective

• diagnostic or prognostic study

• performed at one institution

References

  1. 1.
    Ostrom QT, Gittleman H, Liao P et al (2014) CBTRUS statistical report: primary brain and central nervous system tumors diagnosed in the United States in 2007-2011. Neuro Oncol 16:iv1–iv63CrossRefPubMedPubMedCentralGoogle Scholar
  2. 2.
    Louis DN, Perry A, Reifenberger G et al (2016) The 2016 world health organization classification of tumors of the central nervous system: a summary. Acta Neuropathol 131:803–820CrossRefPubMedGoogle Scholar
  3. 3.
    Alexiou GA, Zikou A, Tsiouris S et al (2014) Correlation of diffusion tensor, dynamic susceptibility contrast MRI and (99m)Tc-Tetrofosmin brain SPECT with tumour grade and Ki-67 immunohistochemistry in glioma. Clin Neurol Neurosurg 116:41–45CrossRefPubMedGoogle Scholar
  4. 4.
    Jakab A, Molnár P, Emri M, Berényi E (2011) Glioma grade assessment by using histogram analysis of diffusion tensor imaging-derived maps. Neuroradiology 53:483–491CrossRefPubMedGoogle Scholar
  5. 5.
    Jiang R, Jiang J, Zhao L et al (2015) Diffusion kurtosis imaging can efficiently assess the glioma grade and cellular proliferation. Oncotarget 6:42380–42393PubMedPubMedCentralGoogle Scholar
  6. 6.
    Xiao HF, Chen ZY, Lou X et al (2015) Astrocytic tumour grading: a comparative study of three-dimensional pseudocontinuous arterial spin labelling, dynamic susceptibility contrast-enhanced perfusion-weighted imaging, and diffusion-weighted imaging. Eur Radiol 25:3423–3430CrossRefPubMedPubMedCentralGoogle Scholar
  7. 7.
    Liu X, Tian W, Kolar B et al (2011) MR diffusion tensor and perfusion-weighted imaging in preoperative grading of supratentorial nonenhancing gliomas. Neuro Oncol 13:447–455CrossRefPubMedPubMedCentralGoogle Scholar
  8. 8.
    Jensen JH, Helpern JA (2010) MRI quantification of non-Gaussian water diffusion by kurtosis analysis. Biomedicine 23:698–710Google Scholar
  9. 9.
    Tabesh A, Jensen JH, Ardekani BA, Helpern JA (2011) Estimation of tensors and tensor-derived measures in diffusional kurtosis imaging. Magn Reson Med 65:823–836CrossRefPubMedGoogle Scholar
  10. 10.
    Raab P, Hattingen E, Franz K, Zanella FE, Lanfermann H (2010) Cerebral gliomas: diffusional kurtosis imaging analysis of microstructural differences. Radiology 254:876–881CrossRefPubMedGoogle Scholar
  11. 11.
    Hui ES, Cheung MM, Qi L et al (2008) Towards better MR characterization of neural tissues using directional diffusion kurtosis analysis. NeuroImage 42:122–134CrossRefPubMedGoogle Scholar
  12. 12.
    van Cauter S, Veraart J, Sijbers J et al (2012) Gliomas: diffusion kurtosis MR imaging in grading. Radiology 263:492–501CrossRefPubMedGoogle Scholar
  13. 13.
    Just N (2014) Improving tumour heterogeneity MRI assessment with histograms. Br J Cancer 111:2205–2213CrossRefPubMedPubMedCentralGoogle Scholar
  14. 14.
    Kyriazi S, Collins DJ, Messiou C et al (2011) Metastatic ovarian and primary peritoneal cancer: assessing chemotherapy response with diffusion-weighted MR imaging--value of histogram analysis of apparent diffusion coefficients. Radiology 261:182–192CrossRefPubMedGoogle Scholar
  15. 15.
    Suo S, Zhang K, Cao M et al (2016) Characterization of breast masses as benign or malignant at 3.0T MRI with whole-lesion histogram analysis of the apparent diffusion coefficient. J Magn Reson Imaging 43:894–902CrossRefPubMedGoogle Scholar
  16. 16.
    Wang S, Kim S, Zhang Y et al (2012) Determination of grade and subtype of meningiomas by using histogram analysis of diffusion-tensor imaging metrics. Radiology 262:584–592CrossRefPubMedGoogle Scholar
  17. 17.
    Xu XQ, Hu H, Su GY et al (2016) Utility of histogram analysis of ADC maps for differentiating orbital tumors. Diagn Interv Radiol 22:161–167CrossRefPubMedPubMedCentralGoogle Scholar
  18. 18.
    Jensen JH, Falangola MF, Hu C et al (2011) Preliminary observations of increased diffusional kurtosis in human brain following recent cerebral infarction. Biomedicine 24:452–457Google Scholar
  19. 19.
    Lee J, Choi SH, Kim JH et al (2014) Glioma grading using apparent diffusion coefficient map: application of histogram analysis based on automatic segmentation. NMR Biomed 27:1046–105220CrossRefPubMedGoogle Scholar
  20. 20.
    Arevalo-Perez J, Peck KK, Young RJ et al (2015) Dynamic contrast-enhanced perfusion MRI and diffusion-weighted imaging in grading of gliomas. J Neuroimaging 25:792–798CrossRefPubMedGoogle Scholar
  21. 21.
    van Cauter S, de Keyzer F, Sima DM et al (2014) Integrating diffusion kurtosis imaging, dynamic susceptibility-weighted contrast-enhanced MRI, and short echo time chemical shift imaging for grading gliomas. Neuro-Oncology 16:1010–1021CrossRefPubMedPubMedCentralGoogle Scholar
  22. 22.
    Chen SD, Hou PF, Lou L, Jin X, Wang TH, Xu JL (2014) The correlation between MR diffusion-weighted imaging and pathological grades on glioma. Eur Rev Med Pharmacol Sci 18:1904–1909PubMedGoogle Scholar
  23. 23.
    Kang Y, Choi SH, Kim YJ et al (2011) Gliomas: Histogram analysis of apparent diffusion coefficient maps with standard- or high-b-value diffusion-weighted MR imaging--correlation with tumor grade. Radiology 261:882–890CrossRefPubMedGoogle Scholar
  24. 24.
    Beppu T, Inoue T, Shibata Y et al (2003) Measurement of fractional anisotropy using diffusion tensor MRI in supratentorial astrocytic tumors. J Neuro-Oncol 63:109–116CrossRefGoogle Scholar
  25. 25.
    Wu EX, Cheung MM (2010) MR diffusion kurtosis imaging for neural tissue characterization. NMR Biomed 23:836–848CrossRefPubMedGoogle Scholar
  26. 26.
    Tietze A, Hansen MB, Østergaard L et al (2015) Mean diffusional kurtosis in patients with glioma: initial results with a fast imaging method in a clinical setting. Am J Neuroradiol 36:1472–1478CrossRefPubMedGoogle Scholar
  27. 27.
    Bai Y, Lin Y, Tian J et al (2016) Grading of gliomas by using monoexponential, biexponential, and stretched exponential diffusion-weighted MR imaging and diffusion kurtosis MR imaging. Radiology 278:496–504CrossRefPubMedGoogle Scholar
  28. 28.
    Qi C, Yang S, Meng L et al (2017) Evaluation of cerebral glioma using 3T diffusion kurtosis tensor imaging and the relationship between diffusion kurtosis metrics and tumor cellularity. J Int Med Res 45:1347–1358CrossRefPubMedPubMedCentralGoogle Scholar
  29. 29.
    Falangola MF, Jensen JH, Babb JS et al (2008) Age-related non-Gaussian diffusion patterns in the prefrontal brain. J Magn Reson Imaging 28:1345–1350CrossRefPubMedPubMedCentralGoogle Scholar
  30. 30.
    Glenn GR, Helpern JA, Tabesh A, Jensen JH (2015) Quantitative assessment of diffusional kurtosis anisotropy. Biomedicine 28:448–459Google Scholar
  31. 31.
    Hansen B, Jespersen SN (2016) Kurtosis fractional anisotropy, its contrast and estimation by proxy. Sci Rep 6:23999CrossRefPubMedPubMedCentralGoogle Scholar
  32. 32.
    Stadlbauer A, Ganslandt O, Buslei R et al (2006) Gliomas: histopathologic evaluation of changes in directionality and magnitude of water diffusion at diffusion-tensor MR imaging. Radiology 240:803–810CrossRefPubMedGoogle Scholar

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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