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Eur J Radiol. 2017 Oct;95:202-211. doi: 10.1016/j.ejrad.2017.08.008. Epub 2017 Aug 18.

Histogram analysis of diffusion kurtosis imaging estimates for in vivo assessment of 2016 WHO glioma grades: A cross-sectional observational study.

Author information

1
Department of Neuroradiology, Eberhard Karls University, Tübingen, Germany. Electronic address: johann-martin.hempel@uni-tuebingen.de.
2
Institute of Neuropathology, Department of Pathology and Neuropathology, Eberhard Karls University, Tübingen, Germany.
3
Department of Neuroradiology, Eberhard Karls University, Tübingen, Germany.
4
Department of Neurosurgery, Eberhard Karls University, Tübingen, Germany.
5
Centre of Neurooncology, Comprehensive Cancer Center Tübingen-Stuttgart, Eberhard Karls University, Tübingen, Germany.
6
Werner Siemens Imaging Center, Department of Preclinical Imaging and Radiopharmacy, Eberhard Karls University, Tübingen, Germany.

Abstract

PURPOSE:

To assess the diagnostic performance of histogram analysis of diffusion kurtosis imaging (DKI) maps for in vivo assessment of the 2016 World Health Organization Classification of Tumors of the Central Nervous System (2016 CNS WHO) integrated glioma grades.

MATERIALS AND METHODS:

Seventy-seven patients with histopathologically-confirmed glioma who provided written informed consent were retrospectively assessed between 01/2014 and 03/2017 from a prospective trial approved by the local institutional review board. Ten histogram parameters of mean kurtosis (MK) and mean diffusivity (MD) metrics from DKI were independently assessed by two blinded physicians from a volume of interest around the entire solid tumor. One-way ANOVA was used to compare MK and MD histogram parameter values between 2016 CNS WHO-based tumor grades. Receiver operating characteristic analysis was performed on MK and MD histogram parameters for significant results.

RESULTS:

The 25th, 50th, 75th, and 90th percentiles of MK and average MK showed significant differences between IDH1/2wild-type gliomas, IDH1/2mutated gliomas, and oligodendrogliomas with chromosome 1p/19q loss of heterozygosity and IDH1/2mutation (p<0.001). The 50th, 75th, and 90th percentiles showed a slightly higher diagnostic performance (area under the curve (AUC) range; 0.868-0.991) than average MK (AUC range; 0.855-0.988) in classifying glioma according to the integrated approach of 2016 CNS WHO.

CONCLUSIONS:

Histogram analysis of DKI can stratify gliomas according to the integrated approach of 2016 CNS WHO. The 50th (median), 75th, and the 90th percentiles showed the highest diagnostic performance. However, the average MK is also robust and feasible in routine clinical practice.

KEYWORDS:

2016 CNS WHO; Diffusion kurtosis imaging; Glioma; Grading; Histogram analysis; Integrated diagnosis; Non-Gaussian diffusion

PMID:
28987669
DOI:
10.1016/j.ejrad.2017.08.008
[Indexed for MEDLINE]

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