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RT Journal Article
SR Electronic
T1 Differentiation of Jugular Foramen Paragangliomas versus Schwannomas Using Golden-Angle Radial Sparse Parallel Dynamic Contrast-Enhanced MRI
JF American Journal of Neuroradiology
JO Am. J. Neuroradiol.
FD American Society of Neuroradiology
SP 1847
OP 1852
DO 10.3174/ajnr.A7243
VO 42
IS 10
A1 Pires, A.
A1 Nayak, G.
A1 Zan, E.
A1 Hagiwara, M.
A1 Gonen, O.
A1 Fatterpekar, G.
YR 2021
UL http://www.ajnr.org/content/42/10/1847.abstract
AB BACKGROUND AND PURPOSE: Accurate differentiation of paragangliomas and schwannomas in the jugular foramen has important clinical implications because treatment strategies may vary but differentiation is not always straightforward with conventional imaging. Our aim was to evaluate the accuracy of both qualitative and quantitative metrics derived from dynamic contrast-enhanced MR imaging using golden-angle radial sparse parallel MR imaging to differentiate paragangliomas and schwannomas in the jugular foramen.MATERIALS AND METHODS: A retrospective study of imaging data was performed on patients (n = 30) undergoing MR imaging for jugular foramen masses with the golden-angle radial sparse parallel MR imaging technique. Imaging data were postprocessed to obtain time-intensity curves and quantitative parameters. Data were normalized to the dural venous sinus for relevant parameters and analyzed for statistical significance using a Student t test. A univariate logistic model was created with a binary output, paraganglioma or schwannoma, using a wash-in rate as a variable. Additionally, lesions were clustered on the basis of the wash-in rate and washout rate using a 3-nearest neighbors method.RESULTS: There were 22 paragangliomas and 8 schwannomas. All paragangliomas demonstrated a type 3 time-intensity curve, and all schwannomas demonstrated a type 1 time-intensity curve. There was a statistically significant difference between paragangliomas and schwannomas when comparing their values for area under the curve, peak enhancement, wash-in rate, and washout rate. A univariate logistic model with a binary output (paraganglioma or schwannoma) using wash-in rate as a variable was able to correctly predict all observed lesions (P &lt; .001). All 30 lesions were classified correctly by using a 3-nearest neighbors method.CONCLUSIONS: Paragangliomas at the jugular foramen can be reliably differentiated from schwannomas using golden-angle radial sparse parallel MR imaging–dynamic contrast-enhanced imaging when imaging characteristics cannot suffice.AUCarea under the curveDCEdynamic contrast-enhancedEESextravascular extracellular spaceGRASPgolden-angle radial sparse parallelKeprate transfer constantKtransvolume transfer constantSERsignal-enhancement ratio3-NN3-nearest neighborsTICtime-intensity curveTMEtime-to-maximum enhancementVeextravascular extracellular space plasma volume fractionVpplasma volume fraction