, Volume 53, Issue 7, pp 483–491 | Cite as

Glioma grade assessment by using histogram analysis of diffusion tensor imaging-derived maps

  • András JakabEmail author
  • Péter Molnár
  • Miklós Emri
  • Ervin Berényi
Diagnostic Neuroradiology



Current endeavors in neuro-oncology include morphological validation of imaging methods by histology, including molecular and immunohistochemical techniques. Diffusion tensor imaging (DTI) is an up-to-date methodology of intracranial diagnostics that has gained importance in studies of neoplasia. Our aim was to assess the feasibility of discriminant analysis applied to histograms of preoperative diffusion tensor imaging-derived images for the prediction of glioma grade validated by histomorphology.


Tumors of 40 consecutive patients included 13 grade II astrocytomas, seven oligoastrocytomas, six grade II oligodendrogliomas, three grade III oligoastrocytomas, and 11 glioblastoma multiformes. Preoperative DTI data comprised: unweighted (B 0) images, fractional anisotropy, longitudinal and radial diffusivity maps, directionally averaged diffusion-weighted imaging, and trace images. Sampling consisted of generating histograms for gross tumor volumes; 25 histogram bins per scalar map were calculated. The histogram bins that allowed the most precise determination of low-grade (LG) or high-grade (HG) classification were selected by multivariate discriminant analysis. Accuracy of the model was defined by the success rate of the leave-one-out cross-validation.


Statistical descriptors of voxel value distribution did not differ between LG and HG tumors and did not allow classification. The histogram model had 88.5% specificity and 85.7% sensitivity in the separation of LG and HG gliomas; specificity was improved when cases with oligodendroglial components were omitted.


Constructing histograms of preoperative radiological images over the tumor volume allows representation of the grade and enables discrimination of LG and HG gliomas which has been confirmed by histopathology.


Glioma Diffusion magnetic resonance imaging Classification Diffusion tensor imaging 



Apparent diffusion coefficient


Central nervous system


Digital Imaging and Communications in Medicine


Diffusion tensor imaging


Diffusion-weighted imaging


Fractional anisotropy


Glioblastoma multiforme


High grade


Low grade


Mean diffusivity (trace/3)


Multivariate discriminant analysis


Magnetic resonance imaging


Regional cerebral blood volume


Region of interest


Surgical Planning Laboratory


Echo time


Repetition time


World Health Organization



We gratefully acknowledge the financial support of 164/2006 grant of Medical Research Council of Ministry of Health, Hungary. P. Molnár is generously supported by a research grant of VFK Krebsforschung GmbH, Germany.

Conflict of interest statement

We declare that we have no conflict of interest.


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Copyright information

© Springer-Verlag 2010

Authors and Affiliations

  • András Jakab
    • 1
    Email author
  • Péter Molnár
    • 2
  • Miklós Emri
    • 3
  • Ervin Berényi
    • 1
  1. 1.Department of Biomedical Laboratory and Imaging Science, Faculty of MedicineUniversity of Debrecen Medical and Health Science CenterDebrecenHungary
  2. 2.Institute of Pathology, Faculty of MedicineUniversity of Debrecen Medical and Health Science CenterDebrecenHungary
  3. 3.Institute of Nuclear Medicine, Faculty of MedicineUniversity of Debrecen Medical and Health Science CenterDebrecenHungary

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