1Assistant Professor, Department of Radio-Diagnosis, Kalinga Institute of Medical Sciences, KIIT University, Bhubaneswar, Odisha, India
2Senior Resident, Department of Radio-Diagnosis, Kalinga Institute of Medical Sciences, KIIT University, Bhubaneswar, Odisha, India
3Post Graduate, Department of Radio-Diagnosis, Kalinga Institute of Medical Sciences, KIIT University, Bhubaneswar, Odisha, India
4Associate Professor, Department of Radio-Diagnosis, Kalinga Institute of Medical Sciences, KIIT University, Bhubaneswar, Odisha, India
*Corresponding Author: Dr Sudhansu Sekhar Mohanty, Department of Radio-diagnosis, Kalinga Institute of Medical Sciences, e-mail: sudhansumohanty2009@gmail.com
Online published on 23 December, 2019.
Nodular thyroid disease are a very common clinical finding, with an estimated prevalence on the basis of palpation that ranges from 3% to 7%. High resolution Ultrasonography (USG) is the most sensitive test to detect thyroid lesions. USG can identify thyroid nodules that have been missed on physical examination, isotope scanning and other imaging techniques. Fine Needle Aspiration Cytology (FNAC) is the gold standard diagnostic investigation for the thyroid nodules. USG of thyroid gland was performed in 70 patients with clinically detected thyroid nodule, in our study, over a period of 18 months, using USG with Color Doppler in GE VOLUSON S6 PRO and PHILIPS Affinity 30 with linear array transducer of 7–9 megahertz frequency. Ultrasonographic findings relevant to benign and malignant thyroid nodules were recorded and compared with fine needle aspiration cytology reports. Out of total 70 cases 64 cases (91%) were diagnosed as benign and 6 cases (9%) as malignant on Ultrasonogram (USG). Whereas in FNAC, 63 cases (90%) were diagnosed as benign and 7 cases (10%) as malignant. In this study, it was found that sensitivity for detecting thyroid malignancy on USG is 85.7%. The positive predictive value for detecting thyroid malignancy on ultrasound is 100%.
Nodular thyroid, USG, FNAC, Sensitivity, Positive predictive value