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Evaluating the Intermediate Suspicion Category in Thyroid Nodules With Artificial Intelligence Assistance: A
Xin-Xin Lin1, Ji-Hang Chen1, Jian-Yang Huang1
1Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
Objective:
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.
Methods:
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.
Results:
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%).
Discussion:
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.
