Zhang Huiting, Dong Yijie, Jia Xiaohong, Zhang Jingwen, Li Zhiyao, Chuan Zhirui, Xu Yanjun, Hu Bin, Huang Yunxia, Chang Cai, Xu Jinfeng, Dong Fajin, Xia Xiaona, Wu Chengrong, Hu Wenjia, Wu Gang, Li Qiaoying, Chen Qin, Deng Wanyue, Jiang Qiongchao, Mou Yonglin, Yan Huannan, Xu Xiaojing, Yan Hongju, Zhou Ping, Shao Yang, Cui Ligang, He Ping, Qian Linxue, Liu Jinping, Shi Liying, Zhao Yanan, Xu Yongyuan, Song Yanyan, Zhan Weiwei, Zhou Jianqiao
Department of Ultrasound, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China.
Department of Medical Ultrasound, Yunnan Cancer Hospital & The Third Affiliated Hospital of Kunming Medical University, Kunming, China.
Front Oncol. 2022 Mar 10;12:830910. doi: 10.3389/fonc.2022.830910. eCollection 2022.
To develop a risk stratification system that can predict axillary lymph node (LN) metastasis in invasive breast cancer based on the combination of shear wave elastography (SWE) and conventional ultrasound.
A total of 619 participants pathologically diagnosed with invasive breast cancer underwent breast ultrasound examinations were recruited from a multicenter of 17 hospitals in China from August 2016 to August 2017. Conventional ultrasound and SWE features were compared between positive and negative LN metastasis groups. The regression equation, the weighting, and the counting methods were used to predict axillary LN metastasis. The sensitivity, specificity, and the areas under the receiver operating characteristic curve (AUC) were calculated.
A significant difference was found in the Breast Imaging Reporting and Data System (BI-RADS) category, the "stiff rim" sign, minimum elastic modulus of the internal tumor and peritumor region of 3 mm between positive and negative LN groups ( < 0.05 for all). There was no significant difference in the diagnostic performance of the regression equation, the weighting, and the counting methods (p > 0.05 for all). Using the counting method, a 0-4 grade risk stratification system based on the four characteristics was established, which yielded an AUC of 0.656 (95% CI, 0.617-0.693, p < 0.001), a sensitivity of 54.60% (95% CI, 46.9%-62.1%), and a specificity of 68.99% (95% CI, 64.5%-73.3%) in predicting axillary LN metastasis.
A 0-4 grade risk stratification system was developed based on SWE characteristics and BI-RADS categories, and this system has the potential to predict axillary LN metastases in invasive breast cancer.
开发一种基于剪切波弹性成像（SWE）与传统超声相结合的风险分层系统，以预测浸润性乳腺癌腋窝淋巴结（LN）转移情况。
2016年8月至2017年8月，从中国17家医院的多中心招募了619例经病理诊断为浸润性乳腺癌并接受乳腺超声检查的参与者。比较了LN转移阳性组和阴性组的传统超声和SWE特征。采用回归方程、加权法和计数法预测腋窝LN转移情况。计算了敏感性、特异性和受试者操作特征曲线（AUC）下的面积。
在乳腺影像报告和数据系统（BI-RADS）分类、“硬边”征、肿瘤内部及肿瘤周围3mm区域的最小弹性模量方面，LN阳性组和阴性组存在显著差异（均P<0.05）。回归方程、加权法和计数法的诊断性能无显著差异（均P>0.05）。采用计数法，基于这四个特征建立了0-4级风险分层系统，该系统在预测腋窝LN转移时，AUC为0.656（95%CI，0.617-0.693，P<0.001），敏感性为54.60%（95%CI，46.9%-62.1%），特异性为68.99%（95%CI，64.5%-73.3%）。
基于SWE特征和BI-RADS分类开发了0-4级风险分层系统，该系统具有预测浸润性乳腺癌腋窝LN转移的潜力。
