Choi Young Jun, Baek Jung Hwan, Baek Seung Hee, Shim Woo Hyun, Lee Kang Dae, Lee Hyoung Shin, Shong Young Kee, Ha Eun Ju, Lee Jeong Hyun
1 Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine , Seoul, Korea.
2 Department of Clinical Epidemiology and Biostatistics, Asan Medical Center, University of Ulsan College of Medicine , Seoul, Korea.
Thyroid. 2015 Dec;25(12):1306-12. doi: 10.1089/thy.2015.0188. Epub 2015 Oct 26.
To establish a practical and simplified method for analyzing thyroid nodules in a clinical setting, the development of a new practical prediction model was required. This study aimed to construct and validate a simple and reliable web-based predictive model using the ultrasonography characteristics of thyroid nodules to stratify the risk of malignancy.
To analyze ultrasonography images, radiologists were asked to assess thyroid nodules according to the following criteria: internal content, echogenicity of the solid portion, shape, margin, and calcifications. Multivariate logistic regression was performed to predict whether nodules were diagnosed as malignant or benign. The developmental data set included 849 nodules (January-June 2003). The validation set included different data (n = 453, June 2008-February 2009).
Ultrasonography features, including solid content, taller-than-wide shape, spiculated margin, ill-defined margin, hypoechogenicity, marked hypoechogenicity, microcalicifications, and rim calcifications, were selected as predictors for malignant nodules in the development set. A 14-point risk scoring system was developed. Malignancy risk ranged from 3.8% to 97.4%, and the risk of malignancy was positively associated with increases in risk scores. The areas under the receiver operating characteristic curve of the development and validation sets were 0.903 and 0.897, respectively.
A simple and reliable web-based predictive model was designed using ultrasonography characteristics to stratify thyroid nodules according to the probability of malignancy.
为在临床环境中建立一种实用且简化的甲状腺结节分析方法，需要开发一种新的实用预测模型。本研究旨在构建并验证一个基于网络的简单可靠预测模型，该模型利用甲状腺结节的超声特征对恶性风险进行分层。
为分析超声图像，要求放射科医生根据以下标准评估甲状腺结节：内部成分、实性部分的回声、形状、边缘和钙化情况。进行多因素逻辑回归以预测结节被诊断为恶性或良性。开发数据集包括849个结节（2003年1月至6月）。验证集包括不同的数据（n = 453，2008年6月至2009年2月）。
超声特征，包括实性成分、纵横比大于1、边缘呈毛刺状、边缘不清、低回声、显著低回声、微钙化和边缘钙化，被选为开发集中恶性结节的预测指标。开发了一个14分的风险评分系统。恶性风险范围为3.8%至97.4%，恶性风险与风险评分的增加呈正相关。开发集和验证集的受试者操作特征曲线下面积分别为0.903和0.897。
设计了一个基于网络的简单可靠预测模型，利用超声特征根据恶性概率对甲状腺结节进行分层。
