1 Department of Radiology, Chung-Ang University Hospital, Chung-Ang University College of Medicine , Seoul, Korea.
2 Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine , Asan Medical Center, Seoul, Korea.
Thyroid. 2017 Dec;27(12):1550-1557. doi: 10.1089/thy.2017.0363.
To minimize potential harm from overuse of fine-needle aspiration, Thyroid Imaging Reporting and Data Systems (TIRADSs) were developed for thyroid nodule risk stratification. The purpose of this study was to perform validation of three scoring risk-stratification models for thyroid nodules using ultrasonography features, a web-based malignancy risk-stratification system, and a model developed by the Korean Society of Thyroid Radiology and the American College of Radiology.
Using ultrasonography images, radiologists assessed thyroid nodules according to the following criteria: internal content, echogenicity of the solid portion, shape, margin, and calcifications. A total of 954 patients (M = 50.8 years; range 13-86 years) with 1112 nodules were evaluated at the authors' institute from January 2013 to December 2014. The discrimination ability of the three models was assessed by estimating the area under the receiver operating characteristic curve. Additionally, Hosmer-Lemeshow goodness-of-fit statistics (calibration ability) were used to evaluate the agreement between the observed and expected number of nodules that were benign or malignant.
Thyroid malignancy was present in 37.2% (414/1112) of nodules. According to the 14-point web-based scoring risk-stratification system, malignancy risk ranged from 4.5% to 100.0% and was positively associated with an increase in risk scores. The areas under the receiver operating characteristic curve of the validation set were 0.884 in the web-based model, 0.891 in the Korean Society of Thyroid Radiology model, and 0.875 in the American College of Radiology model. The Hosmer-Lemeshow goodness-of-fit test indicated that the web-based scoring system showed the best-calibrated result, with a p-value of 0.078.
The three scoring risk-stratification models using the ultrasonography features of thyroid nodules to stratify malignancy risk showed acceptable predictive accuracy and similar areas under the curve. The web-based scoring system demonstrated the strongest agreement in calibration ability analysis. The easily accessible automated web-based scoring risk-stratification system may overcome the complexity of the various Thyroid Imaging Reporting and Data System guidelines and provide simplified guidance on personalized and optimal management in real practice.
为了最大限度地减少细针抽吸过度使用造成的潜在危害，甲状腺成像报告和数据系统（TIRADS）被开发用于甲状腺结节风险分层。本研究的目的是使用超声特征、基于网络的恶性风险分层系统以及韩国甲状腺放射学会和美国放射学院开发的模型，对三种评分风险分层模型对甲状腺结节的预测能力进行验证。
使用超声图像，放射科医生根据以下标准评估甲状腺结节：内部内容、实性部分的回声特性、形状、边缘和钙化。作者所在机构于 2013 年 1 月至 2014 年 12 月对 954 例患者（M=50.8 岁；年龄 13-86 岁）的 1112 个结节进行了评估。通过估计受试者工作特征曲线下的面积来评估三种模型的区分能力。此外，Hosmer-Lemeshow 拟合优度统计量（校准能力）用于评估观察到的和预期的良性或恶性结节数量之间的一致性。
1112 个结节中，甲状腺恶性肿瘤占 37.2%（414/1112）。根据 14 分的基于网络的评分风险分层系统，恶性风险范围为 4.5%至 100.0%，并与风险评分的增加呈正相关。验证集的受试者工作特征曲线下面积在基于网络的模型中为 0.884，在韩国甲状腺放射学会模型中为 0.891，在美国放射学院模型中为 0.875。Hosmer-Lemeshow 拟合优度检验表明，基于网络的评分系统具有最佳的校准结果，p 值为 0.078。
使用甲状腺结节超声特征分层恶性风险的三种评分风险分层模型显示出可接受的预测准确性和相似的曲线下面积。基于网络的评分系统在校准能力分析中表现出最强的一致性。易于访问的自动化基于网络的评分风险分层系统可能克服各种甲状腺成像报告和数据系统指南的复杂性，并在实际实践中提供简化的个性化和最佳管理指导。
