Ultrasonographic Evaluation of Thyroid Nodules with Pathologic Correlation
Journal Title: International Journal of Anatomy Radiology and Surgery - Year 2017, Vol 6, Issue 2
Abstract
Introduction: Thyroid nodules are very common and can occur in upto 50% of the adult population. Ultrasonography is often the initial investigative modality used in the detection and characterisation of various thyroid nodules. Aim: To evaluate the diagnostic accuracy of ultrasonography in characterising benign and malignant thyroid nodules by correlating the sonographic findings with pathological diagnosis as reference. Materials and Methods: In this prospective study, a total of 138 thyroid nodules detected on ultrasonography were further evaluated with Fine Needle Aspiration Cytology (FNAC) and/or Histopathological Examination (HPE). The sonographic features such as internal composition, echotexture, shape, margins, presence or absence of peripheral halo, calcification and internal vascularity were correlated with the final diagnosis. Results: The incidence of malignancy in this study was 10.1% (14/138). Malignant nodules tended to show solid or predominantly solid composition (sensitivity 100%, specificity 43.5%, accuracy 49.2%), hypoechoic pattern (sensitivity 85.7%, specificity 67.7%, accuracy 69.5%), taller-than-wider shape (sensitivity 64.2%, specificity 87%, accuracy 84.7%), irregular margins (sensitivity 78.5%, specificity 82.2%, accuracy 81.8%), calcifications (sensitivity 78.5%, specificity 77.4%, accuracy 77.5%), absence of peripheral halo (sensitivity 64.2%, specificity 53.2%, accuracy 54.3%) and internal vascularity (sensitivity 85.7%, specificity 64.5%, accuracy 66.6%). Conclusion: Ultrasonography is a sensitive and specific modality in the assessment of thyroid nodules with good overall accuracy. The most useful sonographic feature that helped to predict malignancy were a solid composition, hypoechogenicity, taller-than-wider shape, irregular margins and presence of calcification.
Authors and Affiliations
Gururaj Sharma, Ganesh H. Keshava, Veeresh Hanchinal
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