Diagnostic accuracy and ability to reduce unnecessary FNAC: A comparison between four Thyroid Imaging Reporting Data System (TI-RADS) versions

Background: Thyroid Imaging Reporting Data System (TI-RADS) is used to characterize thyroid nodules while reducing unnecessary FNAC. Over the years, several versions of TI-BADS have been developed but there is no consensus on which TI-BADS is the best system. This study aimed to compare the diagnostic accuracy and ability of ACR TI-RADS, EU TI-RADS, K TI-RADS, AI TI-BADS to eliminate unnecessary FNAC. Methods: In this prospective study, thyroid nodules were characterized by using the four TI-RADS systems and US-guided FNAC was done for nodule with the highest ACR TI-BADS score. Correlation between TI-RADS and FNAC results were analyzed. Results: Out of 244 thyroid nodules, 100 nodules with either size <1 cm (43 nodules) non-diagnostic or inconclusive FNAC results (57 nodules) were excluded. Seven nodules (4.9%) were confirmed to be malignant on FNAC. K TI-RADS showed 100% sensitivity and NPV but the lowest specificity (40.2%). EU TI-BADS had the highest specificity (83.2%) but the lowest sensitivity (57.1%) and NPV (97.4%). ACR TI-RADS had an average sensitivity (85.7%) and NPV (98.6%). The specificity of ACR TI-BADS (51.1%) was lower than EU TI-BADS but higher than K TI-BADS. AI TI-BADS showed higher specificity (61.8% vs 51.1%, p < 0.05) but comparable NPV and sensitivity to ACR TI-BADS. AI TI-RADS was able to avoid the highest number of unnecessary FNAC (62.5%) followed by ACR TI-RADS(54.2%), EU TI-RADS(37.5%) and K TI-RADS(11.8%). Conclusion: AI TI-BADS is a more simple scoring system with better overall diagnostic performance and ability to exclude unnecessary FNAC with high NPV. Advances in knowledge: Highest number of unnecessary FNAC thyroid could be prevented by applying AI TIRADS.