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

• Main Authors: Adarsh Ghosh, Soumya Ranjan Malla, Ashu Seith Bhalla, Smita Manchanda, Devasenathipathy Kandasamy, Rakesh Kumar
• Format: Article
• Language: English
• Published: Elsevier 2020-01-01
• Series: European Journal of Radiology Open
• Subjects: Paraganglioma; Head neck; Schwannoma; Nerve sheath tumour; Texture analysis; Radiomics
• Online Access: http://www.sciencedirect.com/science/article/pii/S235204772030037X

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.