SWE, C-TIRADS and AI-assisted diagnostic systems in distinguishing thyroid nodules of different lesion size: Which has better diagnostic performance?

Objective: To compare the diagnostic performance of shear wave elastography (SWE), Chinese Thyroid Imaging Reporting and Data System (C-TIRADS), and an artificial intelligence (AI)-assisted diagnostic system in differentiating thyroid nodules of different sizes. Methods: A total of 103 thyroid nodules in 90 patients were prospectively analyzed and divided into two groups based on the maximum diameter: <10 mm and ≥10 mm. Each thyroid nodule was evaluated using three methods: conventional ultrasound for C-TIRADS scoring, shear wave elastography (SWE), and AI-assisted diagnosis. The diagnostic performance of individual methods and their combinations was assessed within each nodule size group using sensitivity, specificity, and area under the receiver operating characteristic (ROC) curve. For combined assessments, a nodule was considered positive if any constituent method indicated malignancy. Intergroup comparisons of AUC values were performed using DeLong's test to evaluate the effect of nodule size on diagnostic performance. Results: In nodules ≥1 cm, AI demonstrated excellent performance (AUC = 0.875, sensitivity = 96.43%, specificity = 77.78%), and C-TIRADS also performed well (AUC = 0.834, sensitivity = 96.55%, specificity = 70.37%). Among SWE parameters, Emax achieved the highest AUC (0.895). The diagnostic efficacy of AI combined with C-TIRADS (AUC = 0.852) was comparable to that of AI + C-TIRADS + Emax. In subcentimeter nodules, diagnostic performance decreased, with AI achieving an AUC of 0.654 and C-TIRADS an AUC of 0.524, whereas Emean retained moderate discriminative ability (AUC = 0.821). Conclusion: AI combined with C-TIRADS provides an efficient and practical strategy for diagnosing thyroid nodules ≥1 cm. For subcentimeter nodules, Emean retains discriminative ability, indicating potential clinical value in assessment of small lesions.