Format

Send to

Choose Destination
J Neuroradiol. 2012 Dec;39(5):301-7. doi: 10.1016/j.neurad.2011.11.002. Epub 2011 Dec 22.

Multiparametric magnetic resonance imaging to differentiate high-grade gliomas and brain metastases.

Author information

1
Radiodiagnostic Unit, université catholique de Louvain, Saint-Luc University Hospital, avenue Hippocrate 10, 1200 Brussels, Belgium. nmouthuy@hotmail.com

Abstract

PURPOSE:

To assess the performance of parameters used in conventional magnetic resonance imaging (MRI), perfusion-weighted MR imaging (PWI) and visual texture analysis, alone and in combination, to differentiate a single brain metastasis (MET) from glioblastoma multiforme (GBM).

PATIENTS AND METHODS:

In a retrospective study of 50 patients (41 GBM and 14 MET) who underwent T2/FLAIR/T1(post-contrast) imaging and PWI, morphological (circularity, surface area), perfusion (rCBV in the ring-like tumor area, rCBV in the peritumoral area, percentage of signal intensity recovery at the end of first pass) and texture parameters in the peritumoral area were estimated. Statistical differences and performances were assessed using Wilcoxon's test and receiver operating characteristic curves, respectively. Multiparametric classification of tumors was performed using k-means clustering.

RESULTS:

Significant statistical differences in circularity, surface area, rCBVs, percentage of signal intensity recovery and texture parameters (energy, entropy, homogeneity, correlation, inverse differential moment, sum average) were observed between MET and GBM (P<0.05). Moderate-to-good classification performances were found with these parameters. Clustering based on rCBV and texture parameters (contrast, sum average) differentiated MET from GBM with a sensitivity of 92% and a specificity of 71%.

CONCLUSION:

Combining perfusion and visual texture parameters within a statistical classifier significantly improved the differentiation of a single brain MET and GBM.

PMID:
22197404
DOI:
10.1016/j.neurad.2011.11.002
[Indexed for MEDLINE]

Supplemental Content

Full text links

Icon for Elsevier Science
Loading ...
Support Center