| 摘要: |
| 目的 探讨超声造影（CEUS）联合中国版甲状腺影像报告与数据系统（C-TIRADS）分类对甲状腺结节良恶性的鉴别诊断价值。方法 选取行甲状腺常规超声和超声造影检查的116位患者121个甲状腺结节，进行C-TIRADS分类和超声造影评估。以手术病理和细针穿刺（FNA）活检病理结果作为金标准。绘制受试者工作特征（ROC）曲线，比较C-TIRADS分类、CEUS评分法及两者联合对甲状腺结节的诊断效能。结果 C-TIRADS分类、CEUS评分和联合诊断ROC曲线的曲线下面积（AUC）分别为0.820、0.824、0.886，最佳分类阈值分别为4C类、2分、4C类。CEUS联合C-TIRADS分类的ROC曲线下面积（AUC）高于单独应用C-TIRADS分类和CEUS评分（Z=3.183，P<0.01；Z=2.070，P=0.038）。结论 超声造影对C-TIRADS 4类结节有校正价值。CEUS联合C-TIRADS分类有助于提高甲状腺结节良恶性诊断效能。 |
| 关键词: 甲状腺结节 超声造影 甲状腺影像学报告及数据系统 鉴别诊断 |
| DOI： |
| 投稿时间：2022-12-31修订日期：2023-07-04 |
| 基金项目: |
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| Value Of Contrast-Enhanced Ultrasound Combined With C-TIRADS Classification In The Differential Diagnosis Of Thyroid Nodules |
| FU QIANG |
| () |
| Abstract: |
| Objective To evaluate the value of contrast-enhanced ultrasound (CEUS) combined with C-TIRADS classification in the diagnosis of benign and malignant thyroid nodules. Methods A total of 116 patients with 121 thyroid nodules who underwent ultrasound examination and contrast-enhanced ultrasound were selected for C-TIRADS classification and contrast-enhanced ultrasound evaluation, using surgical pathology findings and fine-needle aspiration (FNA) biopsy pathology as the gold standard. Receiver operator characteristic (ROC) curve was used to compare the diagnostic performance of C-TIRADS, CEUS and a combination of both methods. Results ROC curve showed that AUC of C-TIRADS, CEUS and the combination of both methods were 0.820, 0.824 and 0.886 respectively. The optimal truncation values in differentiating benign and malignant nodules were 4C, 2points and 4C. The area under the ROC of CEUS combined with C-TIRADS classification was higher than those of C-TIRADS classification and CEUS scoring, the difference was statistically significant (Z=3.183，P<0.01；Z=2.070，P=0.038)．Conclusion CEUS has revised value for C-TIRADS 4 nodules. CEUS combined with C-TIRADS classification can significantly improve the diagnostic performance of benign and malignant thyroid nodules. |
| Key words: thyroid nodule contrast-enhanced ultrasound thyroid imaging report and data system differential diagnosis |
