| 乳腺小肿物超声特征的Logistic回归分析 |
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| 引用本文： | 李裕生,林肖彬,卓冠航,魏秀霞. 乳腺小肿物超声特征的Logistic回归分析[J]. 临床超声医学杂志, 2020, 22(4): 307-310 |
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| 作者姓名： | 李裕生 林肖彬 卓冠航 魏秀霞 |
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| 作者单位： | 352100 福建省宁德市,福建医科大学附属宁德市医院超声科 |
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| 摘 要： | 目的探讨乳腺小肿物的超声征象,应用Logistic回归分析评价其应用价值。方法回顾性分析经手术病理证实的497个乳腺小肿物的超声征象,根据病理结果分为良性组466个和恶性组31个,比较两组超声特征的差异。应用多因素二元Logistic回归分析筛选出鉴别诊断乳腺小肿物良恶性的独立影响因素,建立回归方程,绘制受试者工作特征(ROC)曲线分析Logistic回归模型的预测价值。结果两组超声特征中形态、纵横比、边缘、回声类型、肿物内钙化、周围组织相关征象及肿物内血流信号比较差异均有统计学意义(均P<0.05),两组肿物后方回声特征比较差异无统计学意义(P=0.26)。多因素二元Logistic回归分析显示纵横比≥1、内部血流信号、边缘血流信号均是鉴别乳腺小肿物良恶性的独立影响因素(OR=9.56、9.68、4.29,P=0.02、0.00、0.04);Logistic回归方程为:Logistic(P)=-3.86+2.23×纵横比≥1+2.29×内部血流信号+1.46×边缘血流信号。Logistic回归模型以预测概率P=0.50作为阈值,鉴别小肿物良恶性的准确率95.2%,敏感性83.9%,特异性89.1%,ROC曲线下面积0.89。结论以纵横比和血流信号建立的Logistic回归模型有助于乳腺小肿物良恶性的鉴别诊断。
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| 关 键 词： | 超声检查,多普勒,彩色 乳腺,小肿物,良恶性 多因素二元Logistic回归 |
Logistic regression analysis of sonographic features of subcentimeter breast mass |
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| LI Yusheng,LIN Xiaobin,ZHUO Guanghang,WEI Xiuxia. Logistic regression analysis of sonographic features of subcentimeter breast mass[J]. Journal of Ultrasound in Clinical Medicine, 2020, 22(4): 307-310 |
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| Authors: | LI Yusheng LIN Xiaobin ZHUO Guanghang WEI Xiuxia |
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| Affiliation: | (Depatartment of Ultrasound,Ningde Municipal Hospital,Fujian Medical University,Fujian 352100,China) |
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| Abstract: | Objective To explore the sonographic features of subcentimeter breast masses,and to assess the application value by the multi-factor binary Logistic regression.Methods The sonographic features of 497 breast masses confirmed by surgical pathology were analyzed retrospectively.The breast masses were divided into benign group(n=466) and malignant group(n=31) according to the results of pathology.The differences of sonographic features were compared.The multifactor binary Logistic regression analysis was used to screen out the independent influencing factors for the differential diagnosis of benign and malignant subcentimeter breast masses.The regression equation was established and the ROC curve was depicted to analyze the predictive value of Logistic regression model.Results There were statistically differences in morphology,aspect ratio,edge,echo type,in-tumor calcification,peripheral tissue signs and blood flow signal in tumor between the two groups(all P<0.05),while there was no statistically difference in posterior echo between the two groups(P=0.26).The multi-factor binary Logistic regression analysis showed that aspect ratio≥1,internal blood flow signal and marginal blood flow signal were independent influencing factors for differentiating benign and malignant breast masses(OR=9.56,9.68,4.29,P=0.02,0.00,0.04).Logistic regression equation was Logistic(P)=-3.86+2.23×aspect ratio≥1+2.29×internal blood flow signal+1.46×marginal blood flow signal.The Logistic regression model demonstrated that with prediction probablity P=0.05 as cut-off value,the diagnostic accuracy was 95.2%,sensitivity was 83.9%,specificity was 89.1%,and area under ROC curve was 0.89.Conclusion The Logistic regression model based on aspect ratio and blood flow signal can efficiently differentiate malignant subcentimeter breast mass from benign one. |
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| Keywords: | Ultrasonography,Doppler,color Breast,subcentimeter mass,benign and malignant Multi-factor binary Logistic regression analysis |
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