BI-RADS影像特征的预测模型在乳腺不可触及钙化病变中的价值

目的 探讨乳腺不可触及钙化病变患者的临床及BI-RADS-X线影像特征,建立Logistic多变量预测模型评估乳腺病变良恶性的概率,以提高乳腺不可触及钙化病变诊断准确率.方法 回顾性分析133例(147个病灶)乳腺不可触及钙化病变,先根据个人经验对病变的X线影像资料进行分析并做出BI-RADS评估分类,与病理对照绘制受试者工作特征(ROC)曲线.之后再对病变X线影像及临床特征进行单因素及多因素分析,筛选与良恶性相关的影响因素,建立Logistic模型并选取合适截点,绘制ROC曲线.最后比较术前BI-RADS分类与Logistic模型对乳腺病变诊断准确性的差别.结果 术前BI-RADS分类判断病变良恶性得到曲线下面积(AUC)为0.867 9.患者临床特征(年龄、位置、病变所在象限)及BI-RADS-X影像特征(分布方式、形态特征、病变部位腺体密度)单因素分析结果显示上述特征差异均有统计学差异;Logistic回归多因素分析示年龄、象限及形态特征差异有统计学意义,建立方程判断病变良恶性得到Logistic模型的AUC为0.906 3.结论 Logistic模型对乳腺病变诊断的准确性高于术前BI-RADS分类,对乳腺病变的正确诊断具有一定的参考价值.

Value of prediction model for BI-RADS imaging features in nonpalpable calcified breast lesions

Objective To improve the diagnostic accuracy of nonpalpable calcified breast lesions by establishing a Logistic multivariate prediction model to assess the probability of benign/malignant breast lesions.The proposed model is based on the clinical and BI-RADS-X-ray imaging features of patients with nonpalpable calcified breast lesions.Methods A total of 147 nonpalpable calcified breast lesions were analyzed retrospectively.Firstly, based on the personal experience,the X-ray imaging data of lesions were analyzed to obtain the BI-RADS categorization, and the ROC curve was plotted by comparison with pathology.Then the univariate and multivariate analysis was performed on the clinical and X-ray imaging features of pathology to select the independent factors related to benign/malignant features.Further,a Logistic regression model was built,the suitable cut-off point was determined, and the ROC curve was obtained.Finally,the comparisons of the diagnostic accuracy of breast lesions were made between the method using the BI-RADS categorization and the method using the Logistic regression model.Results The AUC of the BI-RADS method was 0.867 9.The univariate analysis showed that there exist statistical differences among clinical features of patients(age,location,and quadrant),as well as the BI-RADS-X-ray imaging features (distribution,morphological and gland density).Also,by using the multivariate Logistic regression equation,the statistical differences among age,quadrant and morphological difference can be observed.The AUC using the built Logistic regression model was 0.906 3.Conclusion The diagnostic accuracy of breast lesions using the Logistic model is higher than that using the BI-RADS categorization method.Therefore, the proposed model is valuable for obtaining accurate diagnosis of breast lesions.