Diagnostic Accuracy of Ultrasound U Classification System of Thyroid Ultrasound in Predicting Thyroid Malignancy by Using Histopathology as Gold Standard

  • Tayyiba Akhter Assistant Professor, Department of Radiology, FMH, Lahore
  • Khubaib Shahid Professor, Head department of Radiology, FMH, Lahore
  • Usman Afzal Medical Officer, Department of Radiology, FMH, Lahore

Abstract

Objective: The objective of this study is to determine the diagnostic accuracy of ultrasound U classification system of thyroid ultrasound in predicting thyroid malignancy by using histopathology as gold standard.
Methodology: This was a cross sectional study conducted in the Fatima memorial Hospital, Lahore in a duration of one-year January 2017 to January 2018. All the patients irrespective of age and gender were taken. Patients were segregated with the presence of thyroid nodules. Lateron the patients were subjected to US and ultrasound guided FNAC. Correlation of the histopathology reports was made with the u classification system. Sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, predictive value, negative predictive value and accuracy were calculated in a conservative and non-conservative method.
Results: It was observed that out of 100 nodules examined 11 were malignant. The sensitivity of the ultrasonography was 80% and specificity was 34%. Moreover, positive predictive value was 100% and negative predictive value was found to be 90%.
Conclusion: The u classification system is a reliable tool for the detection of the thyroid nodules and predicting malignancy which is proved by histopathology. More research however is necessary for widespread acceptance and application of this tool.

Published
2018-11-29
How to Cite
AKHTER, Tayyiba; SHAHID, Khubaib; AFZAL, Usman. Diagnostic Accuracy of Ultrasound U Classification System of Thyroid Ultrasound in Predicting Thyroid Malignancy by Using Histopathology as Gold Standard. Annals of PIMS-Shaheed Zulfiqar Ali Bhutto Medical University, [S.l.], v. 14, n. 3, p. 222-226, nov. 2018. ISSN 1815-2287. Available at: <https://www.apims.net/index.php/apims/article/view/170>. Date accessed: 13 dec. 2018.
Section
Original Articles