| 摘要: |
| 摘要 目的 探讨自动乳腺全容积扫查(Automated breast volume scanning，ABVS) 的乳腺癌超声特征在腋窝淋巴结转移负荷术前预测中的应用价值。方法 回顾性分析经手术病理证实的106例（106个病灶）乳腺癌的ABVS超声征象，根据腋窝淋巴结转移状态及负荷的定义，分低负荷组（≤2个转移淋巴结）和高负荷组（≥3个转移淋巴结），对比研究两组超声特征的差异，将单因素分析中具有统计学意义的因素纳入二元Logistic回归模型，建立回归方程，绘制受试者工作特征（Receiver operating characteristic，ROC）曲线，计算曲线下面积（Area under curve，AUC），分析该模型的诊断效能。结果 低负荷组与高负荷组超声特征单因素分析病灶位置、形态、生长方位、内部回声、后方回声、钙化、冠状面高回声晕差异均不具有统计学意义（P＞0.05），病灶最大径、边缘情况、距乳头距离、距皮肤距离、冠状面汇聚征、冠状面虫噬征、血流信号差异均具有统计学意义（P＜0.05），多因素二元Logistic回归分析病灶最大径（OR=4.971，P=0.007）、距皮肤距离（OR=3.559，P=0.017）、冠状面汇聚征（OR=5.932，P=0.019）、冠状面虫噬征（OR=9.426，P=0.003）及血流信号（OR=3.367，P=0.033）均是腋窝淋巴结转移高负荷的独立危险因素，Logistic回归方程为：Logistic（P）=-4.402+1.604×最大径＞2 cm+1.270×距皮肤距离≤0.2 cm+1.780×汇聚征+2.244×虫噬征+1.214×血流信号Ⅱ~Ⅲ级。Logistic回归模型以预测概率P=0.50作为阈值，预测腋窝淋巴结转移高负荷的敏感度71.7%，特异度81.7%，准确率77.4%，AUC为0.872。 结论 依据Logistic回归分析建立预测模型对于乳腺癌腋窝淋巴结转移高负荷有较高的诊断准确性，有一定的临床实用价值。 |
| 关键词: 自动乳腺全容积扫查 腋窝淋巴结 转移负荷 Logistic回归 |
| DOI： |
| 投稿时间：2021-04-22修订日期：2021-05-14 |
| 基金项目:上海市卫生和计划生育委员会基金(20184Y0261), |
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| A retrospective study on the prediction of axillary lymph node metastatic burden by Logistic regression model of breast cancer ultrasonographic features with Automated breast volume scanning |
| wangmeichen,zhuhuihui,shenjunkang,lizhaoxi,shiliqun,liuhaizhen,minxian |
| () |
| Abstract: |
| ABSTRACT Objective To investigate the value of Automated breast volume scanning (ABVS) ultrasonographic features in preoperative prediction of axillary lymph node metastatic burden. Methods A retrospective analysis of 106 cases of breast cancer, which was confirmed by surgical pathology of ABVS ultrasonographic features, according to the burden of axillary lymph node metastasis, points of low burden group (≤2 metastasis lymph nodes) and high burden group (≥3 metastasis lymph nodes). The differences in ultrasonic features of the two groups were compared and studied. The factors with statistical significance in univariate analysis were included into the binary Logistic regression model and the regression equation was established. Receiver operating characteristic (ROC) curve was drawn and the Area under curve (AUC) was calculated to analyze the diagnostic efficiency of the model. Results Single factor analysis of ultrasonic features between the low burden group and the high burden group showed no statistical significance in the location, morphology, growth orientation, internal echo, posterior echo, calcification and coronal high-echo halo (P＞0.05). There were statistically significant differences in the maximum diameter of lesion, marginal condition, distance to nipple, distance to skin, retraction phenomenon on the coronal planes, worm biting sign on the coronal planes, and blood flow signal (P＜0.05). Multi-factor binary Logistic regression analysis was used to analyze the maximum diameter (OR=4.971, P=0.007), distance to skin (OR=3.559, P=0.017), retraction phenomenon on the coronal planes (OR=5.932, P=0.019), worm biting sign on the coronal planes (OR=9.426, P=0.003) and blood flow signal (OR=3.367, P=0.033) were independent risk factors for high burden of axillary lymph node metastasis. Logistic regression equation was as follows: Logistic (P) =-4.402+1.604× maximum diameter ＞2 cm+1.270× distance to skin ≤0.2 cm+1.780× retraction phenomenon +2.244× entomophagy phenomenon +1.214× blood flow signal Ⅱ~Ⅲ. The Logistic regression model demonstrated that with prediction probablity P=0.50 as the cut-off value, sensitivity was 71.7%，specificity was 81.7%，the diagnostic accuracy was 77.4% and area under ROC curve(AUC) was 0.872. Conclusion The model of axillary lymph node metastatic burden prediction based on multivariate Logistic regression analysis has a certain predictive effect on high lymph node metastatic burden. It has a higher diagnostic accuracy and a certain clinical practical value. |
| Key words: Automated breast volume scanning Axillary lymph node Lymph node metastasis burden Logistic regression |
