AUTHOR=Wei An , Tang Yu-Long , Tang Shi-Chu , Zhang Xian-Ya , Ren Jia-Yu , Shi Long , Cui Xin-Wu , Zhang Chao-Xue TITLE=A model based on C-TIRADS combined with SWE for predicting Bethesda I thyroid nodules JOURNAL=Frontiers in Oncology VOLUME=14 YEAR=2024 URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2024.1421088 DOI=10.3389/fonc.2024.1421088 ISSN=2234-943X ABSTRACT=Objectives

This study aimed to explore the performance of a model based on Chinese Thyroid Imaging Reporting and Data Systems (C-TIRADS), clinical characteristics, and shear wave elastography (SWE) for the prediction of Bethesda I thyroid nodules before fine needle aspiration (FNA).

Materials and methods

A total of 267 thyroid nodules from 267 patients were enrolled. Ultrasound and SWE were performed for all nodules before FNA. The nodules were scored according to the 2020 C-TIRADS, and the ultrasound and SWE characteristics of Bethesda I and non-I thyroid nodules were compared. The independent predictors were determined by univariate analysis and multivariate logistic regression analysis. A predictive model was established based on independent predictors, and the sensitivity, specificity, and area under the curve (AUC) of the independent predictors were compared with that of the model.

Results

Our study found that the maximum diameter of nodules that ranged from 15 to 20 mm, the C-TIRADS category <4C, and Emax <52.5 kPa were independent predictors for Bethesda I thyroid nodules. Based on multiple logistic regression, a predictive model was established: Logit (p) = -3.491 + 1.630 × maximum diameter + 1.719 × C-TIRADS category + 1.046 × Emax (kPa). The AUC of the model was 0.769 (95% CI: 0.700–0.838), which was significantly higher than that of the independent predictors alone.

Conclusion

We developed a predictive model for predicting Bethesda I thyroid nodules. It might be beneficial to the clinical optimization of FNA strategy in advance and to improve the accurate diagnostic rate of the first FNA, reducing repeated FNA.