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Diagnostic Accuracy of Dynamic Contrast-enhanced Perfusion MRI in Stratifying Gliomas: A Systematic Review and Meta-analysis

Overview
Journal Cancer Med
Specialty Oncology
Date 2019 Aug 8
PMID 31389669
Citations 21
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Abstract

Background: T1-weighted dynamic contrast-enhanced (DCE) perfusion magnetic resonance imaging (MRI) has been broadly utilized in the evaluation of brain tumors. We aimed at assessing the diagnostic accuracy of DCE-MRI in discriminating between low-grade gliomas (LGGs) and high-grade gliomas (HGGs), between tumor recurrence and treatment-related changes, and between primary central nervous system lymphomas (PCNSLs) and HGGs.

Methods: We performed this study based on the Preferred Reporting Items for Systematic Reviews and Meta-Analysis of Diagnostic Test Accuracy Studies criteria. We systematically surveyed studies evaluating the diagnostic accuracy of DCE-MRI for the aforementioned entities. Meta-analysis was conducted with the use of a random effects model.

Results: Twenty-seven studies were included after screening of 2945 possible entries. We categorized the eligible studies into three groups: those utilizing DCE-MRI to differentiate between HGGs and LGGs (14 studies, 546 patients), between recurrence and treatment-related changes (9 studies, 298 patients) and between PCNSLs and HGGs (5 studies, 224 patients). The pooled sensitivity, specificity, and area under the curve for differentiating HGGs from LGGs were 0.93, 0.90, and 0.96, for differentiating tumor relapse from treatment-related changes were 0.88, 0.86, and 0.89, and for differentiating PCNSLs from HGGs were 0.78, 0.81, and 0.86, respectively.

Conclusions: Dynamic contrast-enhanced-Magnetic resonance imaging is a promising noninvasive imaging method that has moderate or high accuracy in stratifying gliomas. DCE-MRI shows high diagnostic accuracy in discriminating between HGGs and their low-grade counterparts, and moderate diagnostic accuracy in discriminating recurrent lesions and treatment-related changes as well as PCNSLs and HGGs.

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References
1.
Arevalo-Perez J, Kebede A, Peck K, Diamond E, Holodny A, Rosenblum M . Dynamic Contrast-Enhanced MRI in Low-Grade Versus Anaplastic Oligodendrogliomas. J Neuroimaging. 2015; 26(3):366-71. PMC: 5510484. DOI: 10.1111/jon.12320. View

2.
Jia Z, Geng D, Xie T, Zhang J, Liu Y . Quantitative analysis of neovascular permeability in glioma by dynamic contrast-enhanced MR imaging. J Clin Neurosci. 2012; 19(6):820-3. DOI: 10.1016/j.jocn.2011.08.030. View

3.
Santarosa C, Castellano A, Conte G, Cadioli M, Iadanza A, Terreni M . Dynamic contrast-enhanced and dynamic susceptibility contrast perfusion MR imaging for glioma grading: Preliminary comparison of vessel compartment and permeability parameters using hotspot and histogram analysis. Eur J Radiol. 2016; 85(6):1147-56. DOI: 10.1016/j.ejrad.2016.03.020. View

4.
Lin X, Lee M, Buck O, Woo K, Zhang Z, Hatzoglou V . Diagnostic Accuracy of T1-Weighted Dynamic Contrast-Enhanced-MRI and DWI-ADC for Differentiation of Glioblastoma and Primary CNS Lymphoma. AJNR Am J Neuroradiol. 2016; 38(3):485-491. PMC: 5352508. DOI: 10.3174/ajnr.A5023. View

5.
Roberts C, Issa B, Stone A, Jackson A, Waterton J, Parker G . Comparative study into the robustness of compartmental modeling and model-free analysis in DCE-MRI studies. J Magn Reson Imaging. 2006; 23(4):554-63. DOI: 10.1002/jmri.20529. View