New similarity search based glioma grading

Haegler K, Wiesmann M, Boehm C, Freiherr J, Schnell O, Brueckmann H, Tonn JC, Linn J (2012)

Publication Status: Published

Publication Type: Journal article

Publication year: 2012

Journal

Neuroradiology Springer Verlag (Germany)

Publisher: SPRINGER

Book Volume: 54

Pages Range: 829-837

Journal Issue: 8

DOI: 10.1007/s00234-011-0988-2

Abstract

MR-based differentiation between low- and high-grade gliomas is predominately based on contrast-enhanced T1-weighted images (CE-T1w). However, functional MR sequences as perfusion- and diffusion-weighted sequences can provide additional information on tumor grade. Here, we tested the potential of a recently developed similarity search based method that integrates information of CE-T1w and perfusion maps for non-invasive MR-based glioma grading.We prospectively included 37 untreated glioma patients (23 grade I/II, 14 grade III gliomas), in whom 3T MRI with FLAIR, pre- and post-contrast T1-weighted, and perfusion sequences was performed. Cerebral blood volume, cerebral blood flow, and mean transit time maps as well as CE-T1w images were used as input for the similarity search. Data sets were preprocessed and converted to four-dimensional Gaussian Mixture Models that considered correlations between the different MR sequences. For each patient, a so-called tumor feature vector (= probability-based classifier) was defined and used for grading. Biopsy was used as gold standard, and similarity based grading was compared to grading solely based on CE-T1w.Accuracy, sensitivity, and specificity of pure CE-T1w based glioma grading were 64.9%, 78.6%, and 56.5%, respectively. Similarity search based tumor grading allowed differentiation between low-grade (I or II) and high-grade (III) gliomas with an accuracy, sensitivity, and specificity of 83.8%, 78.6%, and 87.0%.Our findings indicate that integration of perfusion parameters and CE-T1w information in a semi-automatic similarity search based analysis improves the potential of MR-based glioma grading compared to CE-T1w data alone.

Authors with CRIS profile

Jessica Freiherr
Oliver Schnell

Involved external institutions

Ludwig-Maximilians-Universität (LMU) DE Germany (DE) Rheinisch-Westfälische Technische Hochschule (RWTH) Aachen DE Germany (DE)

How to cite

APA:

Haegler, K., Wiesmann, M., Boehm, C., Freiherr, J., Schnell, O., Brueckmann, H.,... Linn, J. (2012). New similarity search based glioma grading. Neuroradiology, 54(8), 829-837. https://doi.org/10.1007/s00234-011-0988-2

MLA:

Haegler, Katrin, et al. "New similarity search based glioma grading." Neuroradiology 54.8 (2012): 829-837.

BibTeX: Download