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Web-Based Malignancy Risk Estimation for Thyroid Nodules Using Ultrasonography Characteristics: Development and Validation of a Predictive Model

机译:基于超声特征的基于网络的甲状腺结节恶性肿瘤风险评估:预测模型的开发和验证

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Background: 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. Methods: 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). Results: 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. Conclusion: A simple and reliable web-based predictive model was designed using ultrasonography characteristics to stratify thyroid nodules according to the probability of malignancy.
机译:背景:为了建立一种实用且简化的临床环境中甲状腺结节分析方法,需要开发一种新的实用预测模型。这项研究旨在利用甲状腺结节的超声特征来构建和验证简单可靠的基于网络的预测模型,以对恶性肿瘤的风险进行分层。方法:为了分析超声图像,要求放射科医生根据以下标准评估甲状腺结节:内部含量,固体部分的回声性,形状,边缘和钙化。进行多因素logistic回归以预测结节被诊断为恶性还是良性。发展数据集包括849个结节(2003年1月至6月)。验证集包含不同的数据(n = 453,2008年6月至2009年2月)。结果:超声检查的特征包括固体含量,高出宽大的形状,尖刻的边缘,边缘不清晰,低回声性,明显的低回声性,微钙化和边缘钙化,被选为发展集中恶性结节的预测因子。开发了一个14点风险评分系统。恶性肿瘤的风险介于3.8%至97.4%之间,恶性肿瘤的风险与风险评分的增加呈正相关。开发和验证集的接收器工作特性曲线下的面积分别为0.903和0.897。结论:设计了一个简单可靠的基于网络的预测模型,利用超声特征根据恶性肿瘤的可能性对甲状腺结节进行分层。

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