Predictive value for axillary lymph node metastases in early breast cancer: Based on contrast-enhanced ultrasound characteristics of the primary lesion and sentinel lymph node
Affiliations: [a]
Ultrasound Medical Center, Lanzhou University Second Hospital, Lanzhou, China
| [b]
Gansu Province Clinical Research Center for Ultrasonography, Lanzhou, China
Correspondence:
[*]
Corresponding author: Fang Nie, Ultrasound Medical Center, Lanzhou University Second Hospital, Chengguan District, Cuiyingmen No. 82, Lanzhou 730030, China. E-mail: ery_nief@lzu.edu.cn.
Note: [1] First author. E-mail: pangwenjing1998@163.com.
Abstract: OBJECTIVE:To evaluate the value of contrast-enhanced ultrasound (CEUS) characteristics based on primary lesion combined with lymphatic contrast-enhanced ultrasound (LCEUS) patterns of SLN in predicting axillary lymph node metastasis (ALNM) with T1-2N0 breast cancer. METHODS:A retrospective study was conducted in 118 patients with clinically confirmed T1-2N0 breast cancer. Conventional ultrasound (CUS) and CEUS characteristics of the primary lesion and enhancement patterns of SLN were recorded. The risk factors associated with ALNM were selected by univariate and binary logistic regression analysis, and the receiver operating characteristic (ROC) curve was drawn for the evaluation of predictive ALNM metastasis performance. RESULTS:Univariate analysis showed that age, HER-2 status, tumor size, nutrient vessels, extended range of enhancement lesion, and the enhancement patterns of SLN were significant predictive features of ALNM. Further binary logistic regression analysis indicated that the extended range of enhancement lesion (p < 0.001) and the enhancement patterns of SLN (p < 0.001) were independent risk factors for ALNM. ROC analysis showed that the AUC of the combination of these two indicators for predicting ALNM was 0.931 (95% CI: 0.887–0.976, sensitivity: 75.0%, specificity: 99.8%). CONCLUSION:The CEUS characteristics of primary lesion combined with enhancement patterns of SLN are highly valuable in predicting ALNM and can guide clinical axillary surgery decision-making in early breast cancer.
