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目的应用Logistic回归模型探讨2013版超声乳腺影像报告数据系统(BI-RADS-US)中列举的超声特征及相关临床资料在乳腺肿块良恶性鉴别中的应用价值。方法对367例患者共430个乳腺肿块行灰阶超声、彩色多普勒超声、弹性成像等检查,根据2013版BI-RADS-US记录其超声特征和临床资料,结合手术或穿刺活检病理,先行单因素分析,具有统计学意义的指标再运用Binary Logistic回归分析Enter法进行分析,建立预测模型,并分析其诊断意义。结果恶性肿块170个,良性肿块260个。共有7个特征变量进入回归方程:年龄≥40岁、方位(不平行)、边缘成角、边缘微小分叶状、边缘毛刺状、肿块内细钙化、腋窝淋巴结肿大。结论乳腺肿块良恶性鉴别Logistic回归分析能筛选出对有意义的特征变量,BI-RADS-US在乳腺肿块良恶性鉴别中具有较高的实用价值。
Objective To investigate the clinical value of ultrasound features and related clinical data in the 2013 edition of the Breast Imaging Report Data System (BI-RADS-US) in the diagnosis of benign and malignant breast lesions by using Logistic regression model. Methods A total of 430 breast masses from 367 patients underwent gray-scale ultrasound, color Doppler ultrasound and elastography. According to the ultrasound features and clinical data of 2013 BI-RADS-US, combined with the surgical or biopsy pathology, Univariate analysis, statistically significant indicators and then use Binary Logistic regression analysis of Enter method for analysis, the establishment of prediction models, and analyze the diagnostic significance. Results 170 malignant tumors, 260 benign tumors. There are seven characteristic variables into the regression equation: age ≥ 40 years old, azimuth (not parallel), the edge of the angle, the edge of the small lobular, edge burr-like, calcified within the tumor, axillary lymph nodes. Conclusions Logistic regression analysis of benign and malignant breast lesions can screen out significant characteristic variables. BI-RADS-US has high practical value in benign and malignant breast lesions.