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Journal of Neuroradiology
Sous presse. Epreuves corrigées par l'auteur. Disponible en ligne depuis le vendredi 23 décembre 2011
Doi : 10.1016/j.neurad.2011.11.002
Multiparametric magnetic resonance imaging to differentiate high-grade gliomas and brain metastases
Imagerie par résonance magnétique multiparamétrique pour différencier gliomes de haut grade et métastases cérébrales
 

Nathalie Mouthuy ⁎ , Guy Cosnard, Jorge Abarca-Quinones, Nicolas Michoux
Radiodiagnostic Unit, université catholique de Louvain, Saint-Luc University Hospital, avenue Hippocrate 10, 1200 Brussels, Belgium 

Corresponding author. Tel.: +32 2 7642551; fax: +32 2 7705574.
Summary
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/T1post-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.


Keywords : Brain tumor, Perfusion MR imaging, Texture analysis


OutlineMasquer le plan
Introduction
Patients and methods  (+)
Results
Discussion
Conclusion
Disclosure of interest


© 2011  Published by Elsevier Masson SAS.
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