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CC BY-NC-ND 4.0 · Indian J Radiol Imaging 2024; 34(02): 220-231
DOI: 10.1055/s-0043-1775862
Original Article

Determining the Best Thyroid Imaging Reporting and Data System: A Prospective Study Comparing the Diagnostic Performance of ACR, EU, and K TIRADS in the Evaluation of Thyroid Nodules

Supraja Laguduva Mohan
1   Department of Radiodiagnosis, Jawaharlal Institute of Postgraduate Medical Education and Research, Puducherry, India
,
Ramkumar Govindarajalou 
1   Department of Radiodiagnosis, Jawaharlal Institute of Postgraduate Medical Education and Research, Puducherry, India
,
Dukhabandhu Naik
2   Department of Endocrinology, Jawaharlal Institute of Postgraduate Medical Education and Research, Puducherry, India
,
Sunil Kumar Saxena
3   Department of Otorhinolaryngology, Jawaharlal Institute of Postgraduate Medical Education and Research, Puducherry, India
,
Pampa Ch. Toi
4   Department of Pathology, Jawaharlal Institute of Postgraduate Medical Education and Research, Puducherry, India
,
Gomathi Shankar V.
5   Department of Surgery, Jawaharlal Institute of Postgraduate Medical Education and Research, Puducherry, India
› Author Affiliations
Funding None.
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Abstract

Background Many different risk stratification systems have been formulated for thyroid nodules, differing in their fine-needle aspiration cytology (FNAC) indication, suggesting a lack of consensus around the world.

Purpose This prospective study was conducted to find the best guideline for risk stratification, for a better malignancy yield, and with reduced rates of negative FNACs among three Thyroid Imaging, Reporting, and Data System (TIRADS) guidelines.

Materials and Methods A total of 625 thyroid nodules with conclusive FNAC or histopathological diagnosis were included in the study. Various sonographic parameters were recorded. They were classified into categories as per the three guidelines and compared with FNAC diagnosis. The guidelines were evaluated in terms of sensitivity, specificity, predictive values, and diagnostic accuracy. Sensitivity and specificity were compared by McNemar's test.

Results American College of Radiology (ACR) TIRADS had the highest diagnostic accuracy (56.8%), specificity (50.75%), positive predictive value (23.92%), lowest rates of negative FNACs (76.08%), and high negative predictive value (97.84 %). Korean (K) TIRADS had the maximum sensitivity (97.75%), highest negative predictive value (98.44%), and gross malignancy yield. European TIRADS was between the two other guidelines in most parameters with specificity like K TIRADS.

Conclusion All the three guidelines are very good screening tools, with comparable high sensitivity. ACR TIRADS is better in terms of specificity and reduced rates of negative FNACs. Including the presence of a suspicious cervical lymph node as a criterion and more frequent follow-up might further improve the diagnostic performance of the guideline.

Keywords

thyroid nodules - Thyroid Imaging Reporting and Data System - TIRADS - thyroid ultrasound - American College of Radiology - Korean Society of Thyroid Radiology - European Thyroid Association - fine-needle aspiration cytology

Authors' Contributions

S.L.M.: Data acquisition and analysis, literature search, and manuscript preparation. R.G.: Design, data analysis, manuscript editing, and manuscript review. D.N.: Manuscript editing and review. S.K.S.: Manuscript editing and review. P.C.T.: Manuscript editing and review. G.S.V.: Manuscript editing and review.


Note

This work (before completion of the study) has been presented at the 72nd TN & PY state IRIA conference as an e-poster and at the SonoCon 2020 as a paper.




Publication History

Article published online:
13 October 2023

© 2023. Indian Radiological Association. This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/)

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