Enhancing Thyroid Nodule Characterization: Integrating TI-RADS With Ultrasound Viscosity Imaging for Improved Diagnostic Accuracy

To investigate and validate the clinical utility of the Thyroid Imaging Reporting and Data System (TI-RADS) combined with ultrasound viscosity imaging for differentiating benign and malignant thyroid nodules. This prospective diagnostic study enrolled 437 consecutive patients with 437 thyroid nodules referred to our institution between February 2022 and August 2024. Participants were stratified into a development cohort (DC, n = 306) and a validation cohort (VC, n = 131) based on enrollment chronology. Nodules were further categorized into large (>1 cm), small (≤1 cm) and overall cohorts for subgroup analysis. Using histopathologic results from surgical excision or core needle biopsy as the reference standard, we identified the optimal viscosity-related diagnostic parameter by comparing receiver operating characteristic (ROC) curve-derived area under the curve (AUC) values. The diagnostic performance of TI-RADS alone versus TI-RADS combined with the optimal parameter was systematically evaluated and validated. Demographic characteristics (gender, mean age) and nodule size distributions showed no significant differences between DC and VC groups (all p > 0.05). In the DC, S1-Vmax (The maximum viscosity value of shell1mm around the nodule) emerged as the optimal parameter for malignancy differentiation, achieving an AUC of 0.831 (95% CI: 0.783-0.885) with an optimal cutoff value of 3.52 Pa·s. The integrated model combining S1-Vmax and TI-RADS significantly outperformed TI-RADS alone in diagnostic accuracy across all cohorts (overall, large and small nodules) within the DC (all p < 0.05), with consistent improvement validated in the VC. S1-Vmax demonstrates superior diagnostic performance as a viscosity-based parameter for thyroid nodule characterization. The integration of TI-RADS with USVI significantly enhances the differentiation accuracy between benign and malignant thyroid nodules, providing a clinically actionable tool for risk stratification.