Department of Endocrinology and Nutrition, Puerto Real University Hospital, Cádiz, Spain.
Department of Obstetrics and Gynecology, Puerto Real University Hospital, Cádiz, Spain.
Endocr Pract. 2020 Oct;26(10):1077-1084. doi: 10.4158/EP-2020-0004.
The objective of this study was to develop and validate a predictive model for the assessment of the individual risk of malignancy of thyroid nodules based on clinical, ultrasound, and analytic variables.
A retrospective case-control study was carried out with 542 patients whose thyroid nodules were analyzed at our endocrinology department between 2013 and 2018 while undergoing treatment for thyroidectomy. Starting with a multivariate logistic regression analysis, which included clinical, analytic, and ultrasound variables, a predictive model for thyroid cancer (TC) risk was devised. This was then subjected to a cross-validation process, using resampling techniques.
In the final model, the independent predictors of the risk of malignancy were: being male, age of the extremes, family history of TC, thyroid-stimulating hormone level >4.7 μU/L, presence of autoimmune thyroiditis, solid consistency, hypoechogenicity, irregular or microlobed borders, nodules that are taller than they are wide, microcalcifications, and suspicious adenopathy. With a cut-off point of 50% probability of thyroid cancer, the predictive model had an area under the receiver operating characteristic curve of 0.925 (95% confidence interval 0.898 to 0.952). Finally, using the 10-fold cross-validation method, the accuracy of the model was found to be 88.46%, with a kappa correlation coefficient of 0.62.
A predictive model for the individual risk of malignancy of thyroid nodules was developed and validated using clinical, analytic, and ultrasound variables. An online calculator was developed from this model to be used by clinicians to improve decision-making in patients with thyroid nodules.
本研究旨在建立并验证一种基于临床、超声和分析变量评估甲状腺结节恶性风险的预测模型。
这是一项回顾性病例对照研究，共纳入 542 例于 2013 年至 2018 年期间在我院内分泌科接受甲状腺切除术治疗的甲状腺结节患者。首先进行多变量逻辑回归分析，纳入临床、分析和超声变量，建立甲状腺癌（TC）风险预测模型。然后使用重采样技术进行交叉验证。
在最终模型中，恶性风险的独立预测因素包括：男性、年龄较大或较小、TC 家族史、促甲状腺激素水平>4.7 μU/L、自身免疫性甲状腺炎、实性质地、低回声、不规则或微分叶状边界、纵横比>1、微钙化和可疑淋巴结肿大。当恶性肿瘤概率的截断点为 50%时，预测模型的受试者工作特征曲线下面积为 0.925（95%置信区间 0.898 至 0.952）。最后，使用 10 倍交叉验证法，模型的准确率为 88.46%，kappa 相关系数为 0.62。
本研究利用临床、分析和超声变量建立并验证了一种用于评估甲状腺结节恶性风险的预测模型。我们基于该模型开发了一个在线计算器，供临床医生在评估甲状腺结节患者时使用，以提高决策的准确性。
