Clinico-pathological Correlation of Thyroid Nodule Ultrasound and Cytology Using the TIRADS and Bethesda Classifications.

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Clinico-pathological Correlation of Thyroid Nodule Ultrasound and Cytology Using the TIRADS and Bethesda Classifications.

World J Surg. 2017 Mar 01;:

Authors: Singaporewalla RM, Hwee J, Lang TU, Desai V

Abstract
BACKGROUND: Clinico-pathological correlation of thyroid nodules is not routinely performed as until recently there was no objective classification system for reporting thyroid nodules on ultrasound. We compared the Thyroid Imaging Reporting and Data System (TIRADS) of classifying thyroid nodules on ultrasound with the findings on fine-needle aspiration cytology (FNAC) reported using the Bethesda System.
METHODS: A retrospective analysis of 100 consecutive cases over 1 year (Jan-Dec 2015) was performed comparing single-surgeon-performed bedside thyroid nodule ultrasound findings based on the TIRADS classification to the FNAC report based on the Bethesda Classification. TIRADS 1 (normal thyroid gland) and biopsy-proven malignancy referred by other clinicians were excluded. Benign-appearing nodules were reported as TIRADS 2 and 3. Indeterminate or suspected follicular lesions were reported as TIRADS 4, and malignant-appearing nodules were classified as TIRADS 5 during surgeon-performed bedside ultrasound. All the nodules were subjected to ultrasound-guided FNAC, and TIRADS findings were compared to Bethesda FNAC Classification.
RESULTS: Of the 100 cases, 74 were considered benign or probably benign, 20 were suspicious for malignancy, and 6 were indeterminate on ultrasound. Overall concordance rate with FNAC was 83% with sensitivity and specificity of 70.6 and 90.4%, respectively. The negative predictive value was 93.8%.
CONCLUSION: It is essential for clinicians performing bedside ultrasound thyroid and guided FNAC to document their sonographic impression of the nodule in an objective fashion using the TIRADS classification and correlate with the gold standard cytology to improve their learning curve and audit their results.

PMID: 28251273 [PubMed - as supplied by publisher]

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