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Diffusion tensor imaging for preoperative glioma classification: Combined histogram analysis of various diffusivity maps allows more accurate prediction

A Jakab, Emri M., P Molnár. E Berényi
ECR - European Congress of Radiology
2010

Abstract

Purpose: Our aim was to assess the feasibility of combined histogram analysis of DTI-derived scalar maps in the prediction of glioma subtype. Methods and Materials: Pathologically proved malignant astrocytic tumors of 24 patients included 12 grade II oligoastrocytomas, 3 grade III oligoastrocytomas, 3 grade II oligodendrogliomas and 6 grade IV astrocytomas. Preoperatively obtained DTI data comprised: (i) fractional anisotropy-, (ii) directionally averaged diffusion-weighted scans (isotropic image), (iii) trace images, (iv) longitudinal and (v) perpendicular diffusivity maps. The voxel value distribution within the outlined tumorous volume was analyzed on histograms. Each histogram yielded 50 variables. Cases were labeled according to the 4 histological categories and were also divided into low and high grade classes. The relationship between histogram variables and the apportioning was put to discriminant analysis. Accuracy of the predictive model was defined by the success rate of the leave-one-out cross-validation. Results: The discriminant functions comprising fractional anisotropy histogram variables could not estimate the histological class and grade. By combining the calculated histograms for prediction, the 4 histological types could be properly separated in 83.3% of all cases and 100% success rate was achieved when delineating high and low grade tumors. Classification accuracies were the following when using histograms of individual scalar maps: trace: 75%, perpendicular diffusivity: 50%, longitudinal diffusivity: 37,5%, isotropic image: 45.8%. Conclusion: DTI is appropriate for preoperative prediction of histological class and grade of gliomas. Combination of histograms of diffusivity-related scalar maps is advantageous over


Link to publisher's page
@InProceedings{eth_biwi_00772,
  author = {A Jakab and Emri M. and P Molnár. E Berényi},
  title = {Diffusion tensor imaging for preoperative glioma classification: Combined histogram analysis of various diffusivity maps allows more accurate prediction},
  booktitle = {ECR - European Congress of Radiology},
  year = {2010},
  volume = {EPOS - Electronic Posters},
  keywords = {}
}