The research on risk prediction of thyroid cancer based on ultrasonic characteristic multivariate logistic regression coefficient β composite score method

陈俊慧; 张　曼; 刘水澎; 马　琳; 刘　洋; 李晓松; 孟　健; 李　劼; 张树华

doi:10.19401/j.cnki.1007-3639.2019.04.008

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The research on risk prediction of thyroid cancer based on ultrasonic characteristic multivariate logistic regression coefficient β composite score method

更新时间：2026-01-27

- The research on risk prediction of thyroid cancer based on ultrasonic characteristic multivariate logistic regression coefficient β composite score method
- China Oncology Vol. 29, Issue 4, Pages: 289-293(2019)
- 作者机构：
  
  华北理工大学附属医院超声科,河北,唐山,063009
- 作者简介：
- 基金信息：
- DOI：10.19401/j.cnki.1007-3639.2019.04.008
  CLC： R736.1;R445.1
- Published Online：17 May 2019，
  
  Published：17 May 2019
- 稿件说明：
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陈俊慧，张　曼，刘水澎，马　琳，刘　洋，李晓松，孟　健，李　劼，张树华. 基于超声征象多因素logistic回归β值积分法的甲状腺癌风险预测研究[J]. 中国癌症杂志, 2019, 29(4): 289-293.

陈俊慧, 张　曼, 刘水澎, et al. The research on risk prediction of thyroid cancer based on ultrasonic characteristic multivariate logistic regression coefficient β composite score method[J]. China Oncology, 2019, 29(4): 289-293.
陈俊慧，张　曼，刘水澎，马　琳，刘　洋，李晓松，孟　健，李　劼，张树华. 基于超声征象多因素logistic回归β值积分法的甲状腺癌风险预测研究[J]. 中国癌症杂志, 2019, 29(4): 289-293. DOI： 10.19401/j.cnki.1007-3639.2019.04.008.

陈俊慧, 张　曼, 刘水澎, et al. The research on risk prediction of thyroid cancer based on ultrasonic characteristic multivariate logistic regression coefficient β composite score method[J]. China Oncology, 2019, 29(4): 289-293. DOI： 10.19401/j.cnki.1007-3639.2019.04.008.

摘要

背景与目的：甲状腺结节的超声征象评分方法已有部分报道，但大多是直接为各征象赋值的方式，鲜有权重评分法的研究。该研究通过筛选超声征象中甲状腺癌的独立危险因素，以基于超声征象多因素logistic回归β值的权重评分法建立甲状腺癌风险预测模型，评估其应用价值并验证其工作效能。方法：选取2015年1月—2018年8月，于华北理工大学附属医院行甲状腺超声检查，并最终取得术后病理学检查结果的结节作为研究对象，1 749例患者的共计1 988个甲状腺结节纳入研究范畴。回顾分析其超声报告、影像及病理学资料，超声征象包括结节的组成成分、回声、形态、边界、纵横比、被膜侵犯、钙化情况，用单因素分析法筛选甲状腺癌的独立危险因素，将其纳入多因素logistic回归方程，以各危险征象的偏回归系数β值为其做权重评分，以结节的总积分建立甲状腺癌风险预测模型，采用受试者工作特征曲线（receiver operating characteristic curve，ROC）评价此模型在实际工作中的应用价值；以2018年9月—2018年12月经病理学检查证实的150例甲状腺结节作为验证数据，绘制ROC评价此模型的工作效能。结果：基于超声征象多因素logistic回归β值积分法的甲状腺癌风险预测模型在鉴别诊断甲状腺结节良恶性ROC下面积为0.953（95% CI：0.942~0.964），最佳诊断节点为24.2分，诊断的灵敏度、特异度、阳性预测值和阴性预测值分别为88.6%、93.3%、86.8%和94.4%，验证研究的准确率为88.3%。结论：基于超声征象多因素logistic回归β值积分法的甲状腺癌风险预测模型对于鉴别诊断甲状腺结节良恶性具有较高的效能。

Abstract

Background and purpose: The ultrasonic grading method of thyroid nodules has been partially reported. However

most of the reports are focused on assigning values directly to each characteristic

and few studies are focused on weight scoring. In this study

the independent risk factors of thyroid cancer in ultrasonic signs were screened

and the ultrasound characteristic diagnostic model was established by using the weighted scoring method based on multivariate logistic regression β value of ultrasonic signs. Then we evaluated its application value and verified its work efficiency. Methods: A total of 1 749 patients with 1 988 thyroid nodules underwent ultrasound diagnosis followed by pathological examination in Affiliated Hospital of North China University of Technology from Jan. 2015 to Aug. 2018. The conventional sonographic features and pathological results of these nodules were analyzed. Ultrasonic characteristics included composition

echogenicity

shape

margin

extension beyond the thyroid border

echogenic foci and S/L ratio≥1. Then we screened the ultrasound features with significant difference identified by the univariate analysis. The multivariate logistic regression analysis was performed to screen independent risk factors for thyroid cancer. The partial regression coefficient beta of each risk sign was used as the weighted score

and the total score of nodules was used to establish the risk prediction model of thyroid cancer. Finally

the conventional ultrasound characteristic diagnostic model of thyroid nodules based on the composite score was established

and we drew the receiver operating characteristic (ROC) curve to evaluate the model. A total of 150 thyroid nodules confirmed by pathological results in Affiliated Hospital of North China University of Technology from Sep. 2018 to Dec. 2018 were selected as the validation data. Results: The ultrasound characteristic diagnostic model of thyroid nodules based on the composite score method had high diagnostic accuracy of 95.3% (95% CI: 0.942-0.964). The best cut-off for the diagnosis of malignant lesions was 24.2 with a sensitivity of 88.6%

a specificity of 93.3%

a positive predictive value of 86.8% and a negative predictive value of 94.4%

and the accuracy of the verification study was 83.3%. Conclusion: The ultrasound characteristic diagnostic model of thyroid nodules based on ultrasound characteristic multivariate logistic regression coefficient β composite score method has relatively high efficiency in differentiating benign from malignant thyroid nodules.

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