Evaluating the Intermediate Suspicion Category in Thyroid Nodules With Artificial Intelligence Assistance: A Comparative Analysis of C-TIRADS, ACR-TIRADS, and American Thyroid Association Guidelines.

Abstract

To compare diagnostic performance and fine-needle aspiration (FNA) decision making for thyroid nodules classified as "intermediate suspicion" across three major ultrasound-based risk stratification systems (2017 ACR Thyroid Imaging Reporting and Data System [ACR-TIRADS], 2015 American Thyroid Association (ATA) guidelines, and 2020 Chinese Thyroid Imaging Reporting and Data System [C-TIRADS]), with and without artificial intelligence (AI) assistance. This retrospective study analyzed 1,911 ultrasound images of thyroid nodules from 1,040 patients (mean age, 46.1 ± 13.1 years) collected between 2021 and 2022. The intermediate suspicion category was defined as ACR-TIRADS level TR4 ("moderately suspicious"), ATA level 4, and "moderate suspicion" in C-TIRADS (level 4B). Seven radiologists independently categorized nodules according to each guideline, and an AI model independently evaluated all nodules. AI-assisted diagnostic and FNA strategies were implemented and compared across the three guidelines. Diagnostic performance and 95% confidence intervals were assessed using patient-level clustered logistic generalized estimating equations. In the intermediate suspicion category, diagnostic accuracy was lower for both radiologists and the AI model than in the overall cohort. Radiologists' overall accuracy increased from 66.4% to 71.4% without AI to 74.1% to 79.4% with AI across all three guidelines (P < .001). With AI, C-TIRADS achieved the highest accuracy (79.4%), specificity (66.7%), and positive predictive value (77.4%) (all P < .05), with no significant differences between ACR-TIRADS and ATA guidelines. Without AI, FNA rates were lowest with ACR-TIRADS (37.7%) and highest with C-TIRADS (52.2%). AI assistance reduced FNA rates by 20.3% to 34.7%, increased malignant detection rate by 15.0% to 23.4%, and decreased missed malignancy rate by 23.7% to 36.8% (all P < .001). With AI assistance, ATA level 4 had the lowest FNA rate (14.7%), whereas moderate suspicion in C-TIRADS (level 4B) had the lowest missed malignancy rate (29.8%) and the numerically highest malignant detection rate (70.0%). AI-assisted interpretation improves diagnostic accuracy and FNA decision making for intermediate suspicion nodules. C-TIRADS combined with AI shows superior performance among the three systems.