Computed Tomography Angiography in the Evaluation of Carotid Paragangliomas

Objective: to investigate the informative value of computed tomography (CT) angiography for the evaluation of carotid paragangliomas.

Material and methods. Twelve patients with carotid paragangliomas were examined in the Department of X-ray Computed Tomography and Magnetic Resonance Imaging, Vladimirskiy Moscow Regional Research and Clinical Institute, in 2014-2019. Before surgery, we performed ultrasound imaging and CT angiography (Philips iCT 256 slices): non-contrast study, subsequent contrast enhancement (arterial and venous phases) using the intravenous bolus injection of 60 ml of non-ionic iso- or low-osmolar contrast media. The Shamblin criteria were used to evaluate paragangliomas. The extent of changes and the spread of mass tissue were estimated along the carotid artery walls. Surgery was performed in 11 out of 12 patients.

Results. Unilateral and bilateral masses were identified in 10 and 2 cases, respectively. Two neoplasms that were characterized by small sizes and minimal involvement of carotid artery walls in the process were assigned to type I in accordance with the Shamblin classification. Type II masses (n = 5) were represented by a partial encasement of the internal and external carotid arteries by paraganglioma. Type III tumors (n = 7) were characterized by large sizes and encasement of all walls of the common, internal and external carotid arteries over a considerable length. No calcifications or necrotic areas were found in the structure of the neoplasms. Multiple small tortuous arterial vessels around the tumor were determined in 12 out of 14 cases. These changes did not match the overall length of the tumor and the degree of carotid artery encasement by the latter. Histological examination confirmed the diagnosis of paragangliomas in all cases. A pathologically altered lymph node was found in 1 case during the subsequent 5-month follow-up.

Conclusion. CT angiography can assume the diagnosis of carotid paraganglioma based on its characteristic features and determine the type of tumor according to the Shamblin classification, which allows to plan the optimal surgical intervention.

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