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Advanced MRI increases the diagnostic accuracy of recurrent glioblastoma: Single institution thresholds and validation of MR spectroscopy and diffusion weighted MR imaging

Authors :
Tomas Kazda
Martin Bulik
Petr Pospisil
Radek Lakomy
Martin Smrcka
Pavel Slampa
Radim Jancalek
Source :
NeuroImage: Clinical, Vol 11, Iss C, Pp 316-321 (2016)
Publication Year :
2016
Publisher :
Elsevier, 2016.

Abstract

The accurate identification of glioblastoma progression remains an unmet clinical need. The aim of this prospective single-institutional study is to determine and validate thresholds for the main metabolite concentrations obtained by MR spectroscopy (MRS) and the values of the apparent diffusion coefficient (ADC) to enable distinguishing tumor recurrence from pseudoprogression. Thirty-nine patients after the standard treatment of a glioblastoma underwent advanced imaging by MRS and ADC at the time of suspected recurrence — median time to progression was 6.7 months. The highest significant sensitivity and specificity to call the glioblastoma recurrence was observed for the total choline (tCho) to total N-acetylaspartate (tNAA) concentration ratio with the threshold ≥1.3 (sensitivity 100.0% and specificity 94.7%). The ADCmean value higher than 1313 × 10−6 mm2/s was associated with the pseudoprogression (sensitivity 98.3%, specificity 100.0%). The combination of MRS focused on the tCho/tNAA concentration ratio and the ADCmean value represents imaging methods applicable to early non-invasive differentiation between a glioblastoma recurrence and a pseudoprogression. However, the institutional definition and validation of thresholds for differential diagnostics is needed for the elimination of setup errors before implementation of these multimodal imaging techniques into clinical practice, as well as into clinical trials.

Details

Language :
English
ISSN :
22131582
Volume :
11
Issue :
C
Database :
Directory of Open Access Journals
Journal :
NeuroImage: Clinical
Publication Type :
Academic Journal
Accession number :
edsdoj.0cfaa1ad0bb44cecaf8c2d7eea3dfde2
Document Type :
article
Full Text :
https://doi.org/10.1016/j.nicl.2016.02.016