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目的应用Logistic回归模型评价弹性成像,结合甲状腺自身抗体在甲状腺微小癌(TMC)诊断中的价值。方法对74例患者的甲状腺微小结节(≤1cm)进行常规超声及超声弹性成像检查,同时结合甲状腺自身抗体,建立以超声特征及甲状腺自身抗体为变量的Logistic回归模型。结果运用前进法二分类Logistic回归分析,筛选出在TMC诊断中有统计学意义的特征变量,包括甲状腺球蛋白抗体(TgAb)及弹性应变率(SR比值)。其中SR比值的似然比明显高于其他变量。所建立的回归模型对TMC预报准确度为94.6%。结论弹性成像结合甲状腺自身抗体在TMC的诊断中具有较高的价值,所建立的Logistic回归模型具有较高的诊断准确度。
Objective To evaluate the value of elastography in combination with thyroid autoantibodies in the diagnosis of thyroid microcarcinoma (TMC) using Logistic regression model. Methods Thirty-six patients with small thyroid nodules (≤1cm) underwent routine ultrasound and ultrasound elastography combined with thyroid autoantibodies to establish a Logistic regression model with ultrasound features and thyroid autoantibodies as variables. Results Logistic regression analysis was used to identify the characteristic variables with statistical significance in the diagnosis of TMC, including the TgAb and the elastic strain rate (SR). The SR ratio of the likelihood ratio was significantly higher than other variables. The established regression model predicts the accuracy of TMC to be 94.6%. Conclusion Elastography combined with thyroid autoantibodies have high value in the diagnosis of TMC. The established Logistic regression model has high diagnostic accuracy.