Generic placeholder image

Current Medical Imaging

Editor-in-Chief

ISSN (Print): 1573-4056
ISSN (Online): 1875-6603

Research Article

Dynamic Contrast-Enhanced MR Perfusion in Differentiation of Benign and Malignant Brain Lesions

Author(s): Ezra Cetinkaya, Ayse Aralasmak*, Bahar Atasoy, Sevil Tokdemir, Huseyin Toprak, Ali Toprak, Serpil Kurtcan and Alpay Alkan

Volume 18, Issue 10, 2022

Published on: 25 May, 2022

Article ID: e240322202575 Pages: 7

DOI: 10.2174/1573405618666220324112457

Price: $65

Abstract

Background: We aimed to differentiate Glioblastoma Multiforme (GBM) from benign lesions like Developmental Venous Anomaly (DVA) and Cavernous Malformation (CM) by Dynamic Contrast-Enhanced MR Perfusion (DCE-MRP) markers such as Ktrans, Ve, Kep, and IAUC.

Methods: We retrospectively evaluated 20 patients; 10 GBM as the malignant group, 5 CM and 5 DVA as the benign group. Ktrans, Kep, Ve, and IAUC parameters were measured by DCE-MRP, within the lesion, at perilesional nonenhancing white matter (PLWM) and contralateral normal appearing white matter (CLWM).

Results: All benign and malignant lesions exhibited significantly increased Ktrans, Ve, and IAUC values compared to PLWM and CLWM (p < 0.001, p=0.006 and p<0.001). Subtracted Kep values between lesion and PLWM were significantly different between the benign and malignant groups, as the malignant group exhibited higher subtracted Kep values (p 0.035). For the malignant group; Ktrans and IAUC values at the lesion were positively correlated (r 0.911), while Kep and Ve at CLWM were negatively and strongly correlated (r 0.798). For the benign group; Ktrans with Ve and Ktrans with IAUC at lesion (r 0.708 and r 0.816 respectively), Ktrans and IAUC at PLWM (r 0.809), Ktrans and IAUC at CLWM(r 0.798) were strongly and positively correlated. Ktrans, Ve, and IAUC values can be used to restrict the lesion in both groups.

Conclusion: Ktrans strongly correlates with IAUC and they can be used instead of each other in both benign and malignant lesions. Classical DCE-MRP parameters cannot be used in the differentiation of malignant lesions from benign vascular lesions. However, subtracted Kep values can be used to differentiate GBM from benign vascular lesions.

Keywords: DCE-MR perfusion, glioblastome multiforme, cavernous malformation, developmental venous anomaly, Ktrans, subtracted Kep.

Graphical Abstract

[1]
Choi HS, Kim AH, Ahn SS, Shin NY, Kim J, Lee SK. Glioma grading capability: Comparisons among parameters from dynamic contrast-enhanced MR I and ADC value on DWI. Korean J Radiol 2013; 14(3): 487-92.
[http://dx.doi.org/10.3348/kjr.2013.14.3.487] [PMID: 23690718]
[2]
Khanna A, Venteicher AS, Walcott BP, et al. Glioblastoma mimicking an arteriovenous malformation. Front Neurol 2013; 4: 144.
[http://dx.doi.org/10.3389/fneur.2013.00144] [PMID: 24137154]
[3]
Cha S. Update on brain tumor imaging: From anatomy to physiology. AJNR Am J Neuroradiol 2006; 27(3): 475-87.
[PMID: 16551981]
[4]
Zhao M, Guo LL, Huang N, et al. Quantitative analysis of permeability for glioma grading using dynamic contrast-enhanced magnetic reso-nance imaging. Oncol Lett 2017; 14(5): 5418-26.
[http://dx.doi.org/10.3892/ol.2017.6895] [PMID: 29113174]
[5]
Batra S, Crain B, Engelmann R, Camara-Quintana J, Rigamonti D. Pathology of cavernous malformations. In: Rigamonti D, Ed. Cavernous Malformations of the Nervous System. Cambridge: Cambridge University Press 2011; pp. 1-8.
[http://dx.doi.org/10.1017/CBO9781139003636.002]
[6]
Gökçe E, Acu B, Beyhan M, Celikyay F, Celikyay R. Magnetic resonance imaging findings of developmental venous anomalies. Clin Neuroradiol 2014; 24(2): 135-43.
[http://dx.doi.org/10.1007/s00062-013-0235-9] [PMID: 24240482]
[7]
Cuenod CA, Balvay D. Perfusion and vascular permeability: Basic concepts and measurement in DCE-CT and DCE-MRI. Diagn Interv Imaging 2013; 94(12): 1187-204.
[http://dx.doi.org/10.1016/j.diii.2013.10.010] [PMID: 24211260]
[8]
Castellano A, Falini A. Progress in neuro-imaging of brain tumors. Curr Opin Oncol 2016; 28(6): 484-93.
[http://dx.doi.org/10.1097/CCO.0000000000000328] [PMID: 27649026]
[9]
Little RA, Barjat H, Hare JI, et al. Evaluation of dynamic contrast-enhanced MRI biomarkers for stratified cancer medicine: How do perme-ability and perfusion vary between human tumours? Magn Reson Imaging 2018; 46: 98-105.
[http://dx.doi.org/10.1016/j.mri.2017.11.008] [PMID: 29154898]
[10]
d’Arcy JA, Collins DJ, Padhani AR, Walker-Samuel S, Suckling J, Leach MO. Informatics in Radiology (infoRAD): Magnetic Resonance Imaging Workbench: Analysis and visualization of dynamic contrast-enhanced MR imaging data. Radiographics 2006; 26(2): 621-32.
[http://dx.doi.org/10.1148/rg.262045187] [PMID: 16549620]
[11]
Heye AK, Culling RD, Valdés Hernández MC, Thrippleton MJ, Wardlaw JM. Assessment of blood-brain barrier disruption using dynamic contrast-enhanced MRI. A systematic review. Neuroimage Clin 2014; 6: 262-74.
[http://dx.doi.org/10.1016/j.nicl.2014.09.002] [PMID: 25379439]
[12]
Clatterbuck RE, Eberhart CG, Crain BJ, Rigamonti D. Ultrastructural and immunocytochemical evidence that an incompetent blood-brain barrier is related to the pathophysiology of cavernous malformations. J Neurol Neurosurg Psychiatry 2001; 71(2): 188-92.
[http://dx.doi.org/10.1136/jnnp.71.2.188] [PMID: 11459890]
[13]
Gokce E, Beyhan M, Acu B, Aktas F, Ozmen Z. Magnetic resonance imaging findings of cerebral cavernomas. Kocatepe Med J 2016; 17(1): 1-7.
[14]
Mikati AG, Tan H, Shenkar R, et al. Dynamic permeability and quantitative susceptibility: Related imaging biomarkers in cerebral cavern-ous malformations. Stroke 2014; 45(2): 598-601.
[http://dx.doi.org/10.1161/STROKEAHA.113.003548] [PMID: 24302484]
[15]
Mikati AG, Khanna O, Zhang L, et al. Vascular permeability in cerebral cavernous malformations. J Cereb Blood Flow Metab 2015; 35(10): 1632-9.
[http://dx.doi.org/10.1038/jcbfm.2015.98] [PMID: 25966944]
[16]
Kickingereder P, Sahm F, Wiestler B, et al. Evaluation of microvascular permeability with dynamic contrast-enhanced MRI for the differen-tiation of primary CNS lymphoma and glioblastoma: Radiologic-pathologic correlation. AJNR Am J Neuroradiol 2014; 35(8): 1503-8.
[http://dx.doi.org/10.3174/ajnr.A3915] [PMID: 24722313]
[17]
Koo HR, Cho N, Song IC, et al. Correlation of perfusion parameters on dynamic contrast-enhanced MRI with prognostic factors and sub-types of breast cancers. J Magn Reson Imaging 2012; 36(1): 145-51.
[http://dx.doi.org/10.1002/jmri.23635] [PMID: 22392859]
[18]
Choi YS, Ahn SS, Lee H-J, et al. The initial area under the curve derived from dynamic contrast-enhanced MRI improves prognosis predic-tion in glioblastoma with unmethylated MGMT promoter. AJNR Am J Neuroradiol 2017; 38(8): 1528-35.
[http://dx.doi.org/10.3174/ajnr.A5265] [PMID: 28642265]

Rights & Permissions Print Cite
© 2024 Bentham Science Publishers | Privacy Policy