Glioma grade assessment by using histogram analysis of diffusion tensor imaging-derived maps
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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.
KeywordsGlioma Diffusion magnetic resonance imaging Classification Diffusion tensor imaging
Apparent diffusion coefficient
Central nervous system
Digital Imaging and Communications in Medicine
Diffusion tensor imaging
Mean diffusivity (trace/3)
Multivariate discriminant analysis
Magnetic resonance imaging
Regional cerebral blood volume
Region of interest
Surgical Planning Laboratory
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.
- 1.Louis DH, Ohgaki H, Wiestler OD, Cavanee WK (2007) WHO classification of tumours of the central nervous system. WHO/IARC, LyonGoogle Scholar
- 25.Pieper S, Lorensen B, Schroeder W, Kikinis R (2006) The NA-MIC Kit: ITK, VTK, pipelines, grids and 3D slicer as an open platform for the medical image computing community. Proceedings of the 3rd IEEE International Symposium on Biomedical Imaging: From Nano to Macro 1, pp 698–701Google Scholar
- 27.Abramoff MD, Magelhaes PJ, Ram SJ (2004) Image processing with image. J Biophotonics Int 11:36–42Google Scholar
- 29.Ripley BD (1997) Pattern recognition and neural networks. Cambridge University Press, Cambridge, p 91Google Scholar
- 30.Webb A (1999) Statistical pattern recognition. Oxford University Press, New York, p 6Google Scholar