摘要目的 建立Logistic回归模型并筛选鉴别乳腺结节良恶性的临床超声特征.方法 分析北京大学第一医院2009年7月至2010年5月手术病理证实的217例患者共219个乳腺结节(恶性结节133个、良性结节86个)灰阶、彩色多普勒超声特征及临床资料,选择单因素良恶性组间比较差异有统计学意义的临床及超声特征指标进入多变量二分类Logistic回归分析,建立Logistic回归模型,筛选鉴别乳腺结节良恶性的危险因素,计算良恶性结节概率并定性诊断(P＞0.5为恶性,≤0.5为良性),绘制ROC曲线,分析Logistic回归模型与手术病理相关性.结果 Logistic回归分析筛选了7个指标(危险因素)进入模型:乳腺结节纵横比、边缘特征、强回声晕、微钙化、阻力指数、腋窝淋巴结肿大及患者年龄,Logistic回归模型计算良恶性结节的概率及定性诊断的ROC曲线下面积分别为0.948和0.882,其灵敏性、特异性、准确性为91.7%、84.9%和89.0%.Logistic回归模型与手术病理相关指数r=0.768(P=0.000).结论 以乳腺结节的临床超声特征建立的Logistic回归模型对鉴别乳腺结节良恶性具有较高的诊断价值.

abstractsObjective To create a breast nodule estimation model based on grayscale and color Doppler ultrasonography using Logistic regression that can screen out the specific features for distinguishing breast malignancy from benignancy.Methods From July,2009 to May,2010,217 patients were enrolled in the study in peking university first hospital.Clinical data and ultrasonic features were evaluated in 219 breast nodules of 217 patients confirmed by surgical pathology.Logistic regression model was established to screen out significant ultrasonic indexes for differentiating breast malignancy from benignancy.A receiver operating characteristics curve was made to assess diagnostic value of the Logistic regression model.Correlation was analyzed between the Logistic regression model and surgical pathology.Results Logistic regression model:Logit(p) = - 16.884 + 0.037 × age + 3.228 × longitudinal-transverse axis ratio + 1.412 ×border + 2.663 × halo + 1.813 × microcalcicum + 1.157 × resistance index + 2.204 × enlarged axillary lymph node (x2 = 167.107,P =000).The areas of ROC curve for probability and identification of breast malignant and benign nodule were 0.948 and 0.882 respectively.Diagnostic sensitivity,specificity and accuracy were 91.6%,84.9% and 88.9%.Logistic regression model positively correlated with surgical pathology(r=0.768,P= 0.000).Conclusion Our Logistic regression model can effectively differentiate malignant breast nodules from benign and can identify the ultrasonic features associated with breast cancer.