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基于中国甲状腺影像报告和数据系统的预测贝塞斯达III/IV类甲状腺结节的模型。

A model based on Chinese thyroid imaging reporting and data systems for predicting Bethesda III/IV thyroid nodules.

作者信息

Wei An, Tang Yu-Long, Tang Shi-Chu, Cui Xin-Wu, Zhang Chao-Xue

机构信息

Department of Ultrasound, Hunan Provincial People's Hospital/The First Affiliated Hospital of Hunan Normal University, Changsha, Hunan, China.

Department of Ultrasound, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China.

出版信息

Front Endocrinol (Lausanne). 2025 Mar 3;16:1442575. doi: 10.3389/fendo.2025.1442575. eCollection 2025.

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 other ultrasound characteristics for the prediction of Bethesda III/IV thyroid nodules before fine needle aspiration (FNA).

MATERIALS AND METHODS

A total of 855 thyroid nodules from 810 patients were included. All nodules underwent ultrasound examination before FNA. All nodules were categorized according to the C-TIRADS criteria and classified into two groups, Bethesda III/IV and non-III/IV thyroid nodules, using cytologic diagnosis as the gold standard. The clinical and ultrasonographic characteristics of the nodules in the two groups were compared, and independent predictors of Bethesda III/IV nodules were determined by univariate and multivariate logistic regression analyses, based on which a prediction model was constructed. The predictive efficacy of the model was compared with that of C-TIRADS alone by sensitivity, specificity, and area under the curve (AUC).

RESULTS

Our study found that the C-TIRADS category, homogeneous echotexture, blood flow signal present, and posterior echo unchanged were independent predictors for Bethesda III/IV thyroid nodules. Based on multiple logistic regression, a predictive model was established: Logit (p)= - 4.213 + 0.965 × homogeneous echotexture+ 1.050 × blood flow signal present + 0.473 × posterior echo unchanged+ 2.859 × C-TIRADS 3 + 2.804 × C-TIRADS 4A + 1.824 × C-TIRADS 4B + 0.919 × C-TIRADS 4C. The AUC of the model among all nodules was 0.746 (95%CI: 0.710-0.782), 0.779 (95%CI: 0.730-0.829) among nodules with a diameter (D) > 10mm, and 0.718 (95%CI: 0.667-0.769) among nodules with D ≤ 10mm, which were significantly higher than that of the C-TIRADS alone.

CONCLUSION

We developed a predictive model for Bethesda III/IV thyroid nodules that is better for nodules with D > 10mm FNA operators can choose the optimal puncture strategy based on the prediction results to improve the rate of definitive diagnosis of the first FNA of Bethesda III/IV nodules and thus reduce repeat FNA.

摘要

目的

本研究旨在探索基于中国甲状腺影像报告和数据系统(C-TIRADS)、临床特征及其他超声特征的模型在细针穿刺活检(FNA)前预测甲状腺Bethesda III/IV类结节的性能。

材料与方法

纳入810例患者的855个甲状腺结节。所有结节在FNA前均接受超声检查。所有结节根据C-TIRADS标准进行分类,并以细胞诊断为金标准分为Bethesda III/IV类和非III/IV类甲状腺结节两组。比较两组结节的临床和超声特征,通过单因素和多因素逻辑回归分析确定Bethesda III/IV类结节的独立预测因素,并据此构建预测模型。通过敏感性、特异性和曲线下面积(AUC)比较该模型与单独使用C-TIRADS的预测效能。

结果

本研究发现,C-TIRADS分类、内部回声均匀、存在血流信号及后方回声无改变是甲状腺Bethesda III/IV类结节的独立预测因素。基于多因素逻辑回归建立预测模型:Logit (p)= - 4.213 + 0.965×内部回声均匀 + 1.050×存在血流信号 + 0.473×后方回声无改变 + 2.859×C-TIRADS 3 + 2.804×C-TIRADS 4A + 1.824×C-TIRADS 4B + 0.919×C-TIRADS 4C。该模型在所有结节中的AUC为0.746(95%CI:0.710 - 0.782),直径(D)> 10mm结节中的AUC为0.779(95%CI:0.730 - 0.829),D≤10mm结节中的AUC为0.718(95%CI:0.667 - 0.769),均显著高于单独使用C-TIRADS。

结论

我们建立了甲状腺Bethesda III/IV类结节的预测模型,对直径D > 10mm的结节效果更佳。FNA操作者可根据预测结果选择最佳穿刺策略,以提高Bethesda III/IV类结节首次FNA的确诊率,从而减少重复FNA。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/92fe/11911163/54fe74471764/fendo-16-1442575-g001.jpg

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