Cited by 340Advanced Neuroimaging Techniques and ApplicationsGlioma Diagnosis and TreatmentMRI in cancer diagnosis

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Gliomas: Diffusion Kurtosis MR Imaging in Grading

Van Cauter, Sofie, Veraart, Jelle, Sijbers, Jan, Peeters, Ronald R., Himmelreich, Uwe, De Keyzer, Frederik, Van Gool, Stefaan W., Van Calenbergh, Frank, De Vleeschouwer, Steven, Van Hecke, Wim, Sunaert, Stefan·2012·Radiology

DOI 10.1148/radiol.12110927

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Abstract

Purpose To assess the diagnostic accuracy of diffusion kurtosis magnetic resonance imaging parameters in grading gliomas. Materials and methods The institutional review board approved this prospective study, and informed consent was obtained from all patients. Diffusion parameters-mean diffusivity (MD), fractional anisotropy (FA), mean kurtosis, and radial and axial kurtosis-were compared in the solid parts of 17 high-grade gliomas and 11 low-grade gliomas (P Results Mean, radial, and axial kurtosis were significantly higher in high-grade gliomas than in low-grade gliomas (P = .02, P = .015, and P = .01, respectively). FA and MD did not significantly differ between glioma grades. All values, except for axial kurtosis, that were normalized to the values in the contralateral NAWM were significantly different between high-grade and low-grade gliomas (mean kurtosis, P = .02; radial kurtosis, P = .03; FA, P = .025; and MD, P = .03). When values were normalized to those in the contralateral PLIC, none of the considered parameters showed significant differences between high-grade and low-grade gliomas. The highest sensitivity and specificity for discriminating between high-grade and low-grade gliomas were found for mean kurtosis (71% and 82%, respectively) and mean kurtosis normalized to the value in the contralateral NAWM (100% and 73%, respectively). Optimal thresholds for mean kurtosis and mean kurtosis normalized to the value in the contralateral NAWM for differentiating high-gr…

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Authors: Van Cauter, S., Veraart, J., Sijbers, J., Peeters, R. R., Himmelreich, U., De Keyzer, F., Van Gool, S. W., Van Calenbergh, F., De Vleeschouwer, S., Van Hecke, W., & Sunaert, S.
Affiliations: University of Antwerp (BE)
KU Leuven (BE)
ORCID: Veraart, Jelle
Sijbers, Jan
Peeters, Ronald R.
Himmelreich, Uwe
Sunaert, Stefan
Venue: Radiology
Publisher: Radiological Society of North America (RSNA)
Published: 2012-05
Volume: 263
Issue: 2
Pages: 492-501
ISSN: 0033-8419 (ISSN-L)
1527-1315
DOI: 10.1148/radiol.12110927
OpenAlex: W2166244453
Cited by: 340
References: 34
Citations by year: 2026: 2 · 2025: 11 · 2024: 8 · 2023: 21 · 2022: 21 · 2021: 36 · 2020: 29 · 2019: 29 · 2018: 32 · 2017: 36 · 2016: 29 · 2015: 36 · 2014: 30 · 2013: 17 · 2012: 3
FWCI: 20.76
Type: journal article
Language: en
Open access: closed
Locations: Radiology (publishedVersion)
PubMed (publishedVersion)
Topics: Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
Health Sciences › Medicine › Oncology › Glioma Diagnosis and Treatment
Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › MRI in cancer diagnosis
Keywords: glioma grading, diffusion kurtosis imaging, mean kurtosis, fractional anisotropy, mean diffusivity, normal-appearing white matter, posterior limb of internal capsule, diagnostic accuracy
MeSH terms: Adult, Aged, Brain Neoplasms, Contrast Media, Female, Glioma, Humans, Image Interpretation, Computer-Assisted, Male, Meglumine, Middle Aged, Organometallic Compounds, Prospective Studies, ROC Curve, Sensitivity and Specificity, Anisotropy, Statistics, Nonparametric, Area Under Curve, Imaging, Three-Dimensional, Diffusion Magnetic Resonance Imaging, Neoplasm Grading

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Gliomas: Diffusion Kurtosis MR Imaging in Grading

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