LAUSR

Background The aim of this study was to explore the diagnostic and therapeutic performances of the artificial intelligence (AI), American College of Radiology (ACR), and Kwak Thyroid Imaging Reporting and Data Systems (TIRADSs) using the size thresholds for fine needle aspiration (FNA) and follow-up defined in the ACR TIRADS. Methods This retrospective study included 3,833 consecutive thyroid nodules identified in 2,590 patients from January 2010 to August 2017. Ultrasound (US) features were reviewed using the 2017 white paper of the ACR TIRADS. US categories were assigned according to the ACR/AI and Kwak TIRADS. We applied the thresholds for FNA and follow-up defined in the ACR TIRADS to the Kwak TIRADS. The diagnostic and therapeutic performances were calculated and compared using the McNemar or DeLong methods. Results The AI TIRADS had higher specificity, accuracy, and area under the curve (AUC) than did the ACR and Kwak TIRADS (specificity: 64.6% vs. 57.4% and 52.69%; accuracy: 78.5% vs. 75.4% and 73.0%; AUC: 88.2% vs. 86.6% and 86.0%; all P values <0.05). Meanwhile, the AI TIRADS had a lower FNA rate (FNAR), unnecessary FNA rate (UFR), and follow-up rate (FUR) than did the ACR and Kwak TIRADS using the size thresholds of the ACR TIRADS (specificity: 30.9% vs. 34.4% and 36.9%; accuracy: 41.1% vs. 47.8% and 48.7%; AUC: 34.2% vs. 37.7% and 41.0%; all P values <0.05). In addition, the Kwak TIRADS incorporating the size thresholds of the ACR TIRADS was almost similar to the ACR TIRADS in diagnostic and therapeutical performance. Conclusions The ACR TIRADS can be simplified, which potentially enhances its diagnostic and therapeutic performance. The method of score-based TIRADS (counting in the Kwak TIRADS and weighting in the ACR and AI TIRADS) might not determine the diagnostic and therapeutic performances of the TIRADS. Thus, we propose choosing a straightforward and practical TIRADS in daily practice.