Comparison of a model combining conventional ultrasonography and strain elastography with ACR TI-RADS for reducing unnecessary fine-needle aspiration in solid-predominant partially cystic thyroid nodules: a multicenter study
Year: 2026
Type: article
Study design: Observational Study
Abstract: Background: Solid-predominant partially cystic thyroid nodules (PCTNs) present challenges for malignancy risk stratification and fine-needle aspiration (FNA) decision-making. This study aimed to compare a multiparametric ultrasound model integrating conventional ultrasound and strain elastography with the American College of Radiology Thyroid Imaging Reporting and Data System (ACR TI-RADS) management strategy for reducing unnecessary FNA in solid-predominant PCTNs. Methods: This multicenter retrospective study included solid-predominant PCTNs evaluated at three tertiary referral hospitals between October 2018 and May 2024. A total of 375 nodules were included, with 264 assigned to the training cohort and 111 to the validation cohort. Two predictive models were constructed: Model 1 incorporated maximum nodule diameter and conventional ultrasound features, and Model 2 additionally included elastography. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis. Results: 0.74; paired DeLong P=0.02). Elastographic stiffness was the strongest predictor of malignancy in Model 2 [odds ratio (OR), 10.53; 95% CI: 3.65-30.38; P<0.001]. At a threshold probability of 0.62 and relative to the hypothetical strategy of performing FNA for all nodules, Model 2 could potentially avoid 56% of FNAs while missing 27% of malignant nodules, compared with 41% and 27%, respectively, under the ACR TI-RADS management strategy. Conclusions: The combination of conventional ultrasound features and strain elastography outperformed the ACR TI-RADS system for FNA triage in solid-predominant PCTNs and may help reduce unnecessary FNA procedures.
Source: Gland Surgery
Language: English
Subfield: Endocrinology, Diabetes and Metabolism
Field: Medicine
Domain: Health Sciences
Open Access status: diamond
OpenAlex ID: W7223889927