Dynamic contrast-enhanced magnetic resonance imaging for differentiating head and neck paraganglioma and schwannoma.

BACKGROUND AND PURPOSE: Morphological assessment with conventional magnetic resonance imaging (MRI) sequences has limited specificity to distinguish between paragangliomas and schwannomas. Assessing the differences in microvascular properties through pharmacokinetic parameters of dynamic contrast-enhanced (DCE)-MRI can provide additional information to aid in this differentiation. MATERIALS AND METHODS: A prospective study on MR characterization of neck masses was performed between January 2017 and March 2019 in our department, out of which 40 patients with head and neck paragangliomas (HNPGLs) (33 lesions) and schwannomas (15 lesions) were included in this analysis. MR perfusion using dynamic axial T1WI fat suppressed fast spoiled gradient recalled sequence with parallel imaging was performed in all the patients, in addition to single-shot turbo spin-echo axial diffusion weighted imaging (DWI) and routine MRI. ROI-based method was used to obtain signal-time curves, permeability measurements, and mean apparent diffusion coefficient (ADC) to differentiate paragangliomas from schwannomas. Statistical analysis was done to assess the significance and establish a cutoff to distinguish between the two entities. The available images of DOTANOC PET/CT (34 lesions) were analyzed retrospectively. Correlations between the perfusion, diffusion, and molecular PET/CT parameters were done. RESULTS: Paragangliomas had a higher wash-in rate, wash-out rate, Ktrans, Kep , and Vp (p < 0.001); while schwannomas had a higher relative enhancement (p < 0.012), time to peak, time of onset, brevity of enhancement, and Ve (p < 0.001). Among the perfusion parameters, Kep (area under curve (AUC) 0.994) and Vp (AUC 0.992) were found to have the highest diagnostic value. In diffusion-weighted imaging, paragangliomas had a lower mean ADC compared to schwannomas (p < 0.001). The SUVmax and SUVmean were significantly associated with Ktrans , Kep , and Vp in paragangliomas. CONCLUSION: DCE-MRI in addition to DWI-MRI can accurately distinguish HNPGL from schwannoma and may replace the need for any additional imaging and preoperative biopsy in most cases.

Texture analysis of routine T2 weighted fat-saturated images can identify head and neck paragangliomas - A pilot study.

PURPOSE: To evaluate the role of the first and second-order texture parameters obtained from T2-weighted fat-saturated DIXON images in differentiating paragangliomas from other neck masses, and to develop a statistical model to classify them. METHOD: We retrospectively evaluated 38 paragangliomas, 18 nerve-sheath tumours and 14 other miscellaneous neck lesions obtained from an IRB approved study conducted between January 2016 and June 2019; using a composite gold standard of histopathology, cytology and DOTANOC PET CT (A total of 70 lesions in 63 patients). Fat-suppressed T2weighted-DIXON axial images were used. First and second-order texture-parameters were calculated from the original and filtered images. Feature selection using F-statistics and collinearity analysis provided 14 texture parameters for further analysis. Mann-Whitney-U test was used to compare between the groups and p-values were adjusted for multiple comparisons. ROC curve analysis was used to obtain optimal cut-offs. RESULTS: A total of ten texture features were found to be significantly different between paragangliomas and non-paraganglioma lesions. Minimum from the histogram of grey levels was lower in paragangliomas with a cut off of ≤113.462 obtaining 62.9 % sensitivity and 77.27 % specificity in differentiating paragangliomas from non-paragangliomas. Logistic regression model was trained (n-49) using forward feature selection, which when evaluated on the validation set(n-21)- obtained an AUC of 0.855(95 %CI, 0.633 to 0.968) with a positive likelihood ratio of 4.545 (95 %CI, 1.298-15.923) in differentiating paragangliomas from non-paragangliomas. CONCLUSION: Texture analysis of a routine imaging sequence can identify paragangliomas with high accuracy. Further development of texture analysis would enable better imaging workflow, resource utilisation and imaging cost reductions.