Department of Radio-diagnosis, Zagazig University, Zagazig, Egypt.
Department of Radio-diagnosis, Ain Shams University, Cairo, Egypt.
Eur J Radiol. 2019 Aug;117:184-192. doi: 10.1016/j.ejrad.2019.06.015. Epub 2019 Jun 18.
To assess diagnostic validity and reproducibility of Thyroid Imaging Reporting and Data System (TI-RADS) for interpretation of thyroid nodules by thyroid ultrasonography (US).
A prospective multicentre study initially included 557 patients with clinically suspected thyroid nodules. After exclusion, a final cohort of 380 patients with 948 thyroid nodules detected by US were enrolled. Based on American College of Radiology (ACR) TI-RADS, three radiologists analysed all US examinations independently and assigned a TI-RADS category to each thyroid nodule. The final diagnosis was based on cytology which was used as reference standard for calculating diagnostic performance of TI-RADS for predicting malignant thyroid nodules. The Fleiss and weighted kappa (κ) statistics were applied to assess inter-observer agreement of morphological features and TI-RADS scoring results for thyroid nodules. Additionally, we made a simple screening among referring clinicians to assess the clinical response to application of TI-RADS.
A total of 948 thyroid nodules were evaluated; 136 (14.3%) were malignant, and 812 (85.7%) were benign. The papillary carcinoma was the most common malignant thyroid nodules (81.6%). The best cut-off value for predicting malignant thyroid nodules was > TR3. On a lesion-based analysis, the TI-RADS had a sensitivity, specificity, and an accuracy of 98.3%, 90.9%, and 92.1%, respectively when regarding those thyroid nodules classified as > TR3 for predicting malignancy. The inter-observer agreement of the TI-RADS category was good (κ = 0.636). Ninety percent of referring clinicians accept TI-RADS.
TI-RADS improves diagnostic performance of US for predicting malignant thyroid nodules with high validity and high reproducibility.
评估甲状腺影像报告和数据系统（TI-RADS）在甲状腺超声检查（US）诊断甲状腺结节中的诊断准确性和可重复性。
一项前瞻性多中心研究最初纳入了 557 例临床怀疑患有甲状腺结节的患者。排除后，最终纳入了 380 例经 US 检测到 948 个甲状腺结节的患者。基于美国放射学院（ACR）TI-RADS，三位放射科医生独立分析了所有 US 检查，并为每个甲状腺结节分配了 TI-RADS 类别。最终诊断基于细胞学，细胞学被用作计算 TI-RADS 预测恶性甲状腺结节的诊断性能的参考标准。采用 Fleiss 和加权κ（κ）统计评估了观察者间对甲状腺结节形态特征和 TI-RADS 评分结果的一致性。此外，我们对参考医生进行了简单的筛查，以评估 TI-RADS 应用的临床反应。
共评估了 948 个甲状腺结节，其中 136 个（14.3%）为恶性，812 个（85.7%）为良性。甲状腺乳头状癌是最常见的恶性甲状腺结节（81.6%）。预测恶性甲状腺结节的最佳截断值为>TR3。在基于病变的分析中，当将 TI-RADS 分类为>TR3 的甲状腺结节用于预测恶性时，TI-RADS 的敏感性、特异性和准确性分别为 98.3%、90.9%和 92.1%。TI-RADS 类别的观察者间一致性较好（κ=0.636）。90%的参考医生接受 TI-RADS。
TI-RADS 提高了 US 预测恶性甲状腺结节的诊断性能，具有较高的有效性和可重复性。
