In vivo molecular profiling of human glioma using diffusion kurtosis imaging


The purpose of this study is to assess the diagnostic performance of diffusion kurtosis imaging (DKI) for in vivo molecular profiling of human glioma. Normalized mean kurtosis (MKn) and mean diffusivity (MDn) metrics from DKI were assessed in 50 patients with histopathologically confirmed glioma. The results were compared in regard to the WHO-based histological findings and molecular characteristics leading to integrated diagnosis (Haarlem Consensus): isocitrate-dehydrogenase (IDH1/2) mutation status, alpha-thalassemia/mental retardation syndrome X-linked (ATRX) expression, chromosome 1p/19q loss of heterozygosity (LOH), and O6-methylguanine DNA methyltransferase (MGMT) promoter methylation status. MKn was significantly lower in tumors with IDH1/2 mutation (0.43 ± 0.09) and ATRX loss of expression (0.41 ± 0.11) than in those with IDH1/2 wild type (0.57 ± 0.09, p < 0.001) and ATRX maintained expression (0.51 ± 0.10, p = 0.004), respectively. Regarding the integrated molecular diagnosis, MKn was significantly higher in primary glioblastoma (0.57 ± 0.10) than in astrocytoma (0.39 ± 0.11, p < 0.001) and oligodendroglioma (0.47 ± 0.05, p = 0.003). MK may be used to provide insight into the human glioma molecular profile regarding IDH1/2 mutation status and ATRX expression. Considering the diagnostic and prognostic significance of these molecular markers, MK appears to be a promising in vivo biomarker for glioma. The diagnostic performance of MK seems to fit more with the integrated molecular approach than the conventional histological findings of the current WHO 2007 classification.

This is a preview of subscription content, log in to check access.

Fig. 1
Fig. 2


  1. 1.

    Louis DN, Ohgaki H, Wiestler OD et al (2007) The 2007 WHO classification of tumours of the central nervous system. Acta Neuropathol 114(2):97–109. doi:10.1007/s00401-007-0243-4

    Article  PubMed  PubMed Central  Google Scholar 

  2. 2.

    van Cauter S, Veraart J, Sijbers J et al (2012) Gliomas: diffusion kurtosis MR imaging in grading. Radiology 263(2):492–501. doi:10.1148/radiol.12110927

    Article  PubMed  Google Scholar 

  3. 3.

    Scott JN, Brasher PMA, Sevick RJ et al (2002) How often are nonenhancing supratentorial gliomas malignant? A population study. Neurology 59(6):947–949

    CAS  Article  PubMed  Google Scholar 

  4. 4.

    Watanabe M, Tanaka R, Takeda N (1992) Magnetic resonance imaging and histopathology of cerebral gliomas. Neuroradiology 34(6):463–469

    CAS  Article  PubMed  Google Scholar 

  5. 5.

    Dean BL, Drayer BP, Bird CR et al (1990) Gliomas: classification with MR imaging. Radiology 174(2):411–415. doi:10.1148/radiology.174.2.2153310

    CAS  Article  PubMed  Google Scholar 

  6. 6.

    van Cauter S, Keyzer F de, 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 Oncol 16(7):1010–1021. doi:10.1093/neuonc/not304

    Article  PubMed  PubMed Central  Google Scholar 

  7. 7.

    Kulkarni AV, Guha A, Lozano A et al (1998) Incidence of silent hemorrhage and delayed deterioration after stereotactic brain biopsy. J Neurosurg 89(1):31–35. doi:10.3171/jns.1998.89.1.0031

    CAS  Article  PubMed  Google Scholar 

  8. 8.

    Kärger J (1985) NMR self-diffusion studies in heterogeneous systems. Adv Colloid Interface Sci 23:129–148. doi:10.1016/0001-8686(85)80018-X

    Article  Google Scholar 

  9. 9.

    Jensen JH, Helpern JA, Ramani A et al (2005) Diffusional kurtosis imaging: the quantification of non-gaussian water diffusion by means of magnetic resonance imaging. Magn Reson Med 53(6):1432–1440. doi:10.1002/mrm.20508

    Article  PubMed  Google Scholar 

  10. 10.

    Jensen JH, Helpern JA (2010) MRI quantification of non-Gaussian water diffusion by kurtosis analysis. NMR biomed 23(7): 698–710. doi:10.1002/nbm.1518

    Article  PubMed  PubMed Central  Google Scholar 

  11. 11.

    Lu H, Jensen JH, Ramani A et al. (2006) Three-dimensional characterization of non-Gaussian water diffusion in humans using diffusion kurtosis imaging. NMR biomed 19(2): 236–247. doi:10.1002/nbm.1020

    Article  PubMed  Google Scholar 

  12. 12.

    Poot DHJ, den Dekker AJ, Achten E et al (2010) Optimal experimental design for diffusion kurtosis imaging. IEEE Trans Med Imaging 29(3):819–829. doi:10.1109/TMI.2009.2037915

    Article  PubMed  Google Scholar 

  13. 13.

    Raab P, Hattingen E, Franz K et al (2010) Cerebral gliomas: diffusional kurtosis imaging analysis of microstructural differences. Radiology 254(3):876–881. doi:10.1148/radiol.09090819

    Article  PubMed  Google Scholar 

  14. 14.

    Jiang R, Jiang J, Zhao L et al (2015) Diffusion kurtosis imaging can efficiently assess the glioma grade and cellular proliferation. Oncotarget 6(39):42380–42393

    PubMed  PubMed Central  Google Scholar 

  15. 15.

    Tan Y, Zhang H, Zhao R-F et al (2016) Comparison of the values of MRI diffusion kurtosis imaging and diffusion tensor imaging in cerebral astrocytoma grading and their association with aquaporin-4. Neurol India 64(2):265. doi:10.4103/0028-3886.177621

    Article  PubMed  Google Scholar 

  16. 16.

    Louis DN, Perry A, Burger P et al (2014) International Society Of Neuropathology—Haarlem consensus guidelines for nervous system tumor classification and grading. Brain Pathol 24(5):429–435. doi:10.1111/bpa.12171

    Article  PubMed  Google Scholar 

  17. 17.

    Reuss DE, Mamatjan Y, Schrimpf D et al (2015) IDH mutant diffuse and anaplastic astrocytomas have similar age at presentation and little difference in survival: a grading problem for WHO. Acta Neuropathol 129(6):867–873. doi:10.1007/s00401-015-1438-8

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  18. 18.

    Reuss DE, Sahm F, Schrimpf D et al (2015) ATRX and IDH1-R132H immunohistochemistry with subsequent copy number analysis and IDH sequencing as a basis for an “integrated” diagnostic approach for adult astrocytoma, oligodendroglioma and glioblastoma. Acta Neuropathol 129(1):133–146. doi:10.1007/s00401-014-1370-3

    CAS  Article  PubMed  Google Scholar 

  19. 19.

    Gerson SL (2004) MGMT: its role in cancer aetiology and cancer therapeutics. Nat Rev Cancer 4(4):296–307. doi:10.1038/nrc1319

    CAS  Article  PubMed  Google Scholar 

  20. 20.

    Sahm F, Reuss D, Koelsche C et al (2014) Farewell to oligoastrocytoma: in situ molecular genetics favor classification as either oligodendroglioma or astrocytoma. Acta Neuropathol 128(4):551–559. doi:10.1007/s00401-014-1326-7

    CAS  Article  PubMed  Google Scholar 

  21. 21.

    Capper D, Weissert S, Balss J et al (2010) Characterization of R132H mutation-specific IDH1 antibody binding in brain tumors. Brain Pathol 20(1):245–254. doi:10.1111/j.1750-3639.2009.00352.x

    CAS  Article  PubMed  Google Scholar 

  22. 22.

    Schittenhelm J, Mittelbronn M, Meyermann R et al (2011) Confirmation of R132H mutation of isocitrate dehydrogenase 1 as an independent prognostic factor in anaplastic astrocytoma. Acta Neuropathol 122(5):651–652. doi:10.1007/s00401-011-0885-0

    Article  PubMed  Google Scholar 

  23. 23.

    Hartmann C, Meyer J, Balss J et al (2009) Type and frequency of IDH1 and IDH2 mutations are related to astrocytic and oligodendroglial differentiation and age: a study of 1010 diffuse gliomas. Acta Neuropathol 118(4):469–474. doi:10.1007/s00401-009-0561-9

    Article  PubMed  Google Scholar 

  24. 24.

    Thon N, Eigenbrod S, Grasbon-Frodl EM et al (2009) Novel molecular stereotactic biopsy procedures reveal intratumoral homogeneity of loss of heterozygosity of 1p/19q and TP53 mutations in World Health Organization grade II gliomas. J Neuropathol Exp Neurol 68(11):1219–1228. doi:10.1097/NEN.0b013e3181bee1f1

    Article  PubMed  Google Scholar 

  25. 25.

    Hegi ME, Diserens A-C, Gorlia T et al (2005) MGMT gene silencing and benefit from temozolomide in glioblastoma. N Engl J Med 352(10):997–1003. doi:10.1056/NEJMoa043331

    CAS  Article  PubMed  Google Scholar 

  26. 26.

    Tozer DJ, Jäger HR, Danchaivijitr N et al (2007) Apparent diffusion coefficient histograms may predict low-grade glioma subtype. NMR Biomed 20(1):49–57. doi:10.1002/nbm.1091

    Article  PubMed  Google Scholar 

  27. 27.

    Kang Y, Choi SH, Kim Y-J 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(3):882–890. doi:10.1148/radiol.11110686

    Article  PubMed  Google Scholar 

  28. 28.

    Falangola MF, Jensen JH, Babb JS et al (2008) Age-related non-Gaussian diffusion patterns in the prefrontal brain. J Magn Reson Imaging 28(6):1345–1350. doi:10.1002/jmri.21604

    Article  PubMed  PubMed Central  Google Scholar 

  29. 29.

    Coutu JP, Chen JJ, Rosas HD et al (2014) Non-Gaussian water diffusion in aging white matter. Neurobiol Aging 35(6):1412–1421. doi:10.1016/j.neurobiolaging.2013.12.001

    Article  PubMed  Google Scholar 

  30. 30.

    Kleihues P, Soylemezoglu F, Schauble B et al (1995) Histopathology, classification, and grading of gliomas. Glia 15(3):211–221. doi:10.1002/glia.440150303

    CAS  Article  PubMed  Google Scholar 

  31. 31.

    Soeda A, Hara A, Kunisada T et al. (2015) The evidence of glioblastoma heterogeneity. Sci Rep 5: 7979. doi:10.1038/srep07979

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  32. 32.

    DeAngelis LM (2001) Brain tumors. N Engl J Med 344(2):114–123. doi:10.1056/NEJM200101113440207

    CAS  Article  PubMed  Google Scholar 

  33. 33.

    Cha S (2006) Update on Brain Tumor Imaging: From Anatomy to Physiology. AJNR Am J Neuroradiol 27(3):475–487

    CAS  PubMed  Google Scholar 

  34. 34.

    Popov S, Jury A, Laxton R et al (2013) IDH1-associated primary glioblastoma in young adults displays differential patterns of tumour and vascular morphology. PLoS One 8(2):e56328. doi:10.1371/journal.pone.0056328

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  35. 35.

    Lee S, Choi SH, Ryoo I et al (2015) Evaluation of the microenvironmental heterogeneity in high-grade gliomas with IDH1/2 gene mutation using histogram analysis of diffusion-weighted imaging and dynamic-susceptibility contrast perfusion imaging. J Neurooncol 121(1):141–150. doi:10.1007/s11060-014-1614-z

    CAS  Article  PubMed  Google Scholar 

  36. 36.

    Kim SH, Kim H, Kim TS (2005) Clinical, histological, and immunohistochemical features predicting 1p/19q loss of heterozygosity in oligodendroglial tumors. Acta Neuropathol 110(1):27–38. doi:10.1007/s00401-005-1020-x

    CAS  Article  PubMed  Google Scholar 

Download references


We thank Robert Grimm from Siemens (Erlangen, Germany) for support in image post-processing.

Author information



Corresponding author

Correspondence to Johann-Martin Hempel.

Additional information

The original version of this article was revised: The second author’s family name was incorrect. The name has been updated in this version.

An erratum to this article is available at

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Hempel, J., Bisdas, S., Schittenhelm, J. et al. In vivo molecular profiling of human glioma using diffusion kurtosis imaging. J Neurooncol 131, 93–101 (2017).

Download citation


  • Diffusion kurtosis imaging
  • Glioma
  • Isocitrate dehydrogenase
  • IDH1/2
  • ATRX
  • 1p/19q LOH
  • MGMT
  • Integrated diagnosis
  • Haarlem consensus