Comprehensive Risk System Based on Shear Wave Elastography and BI-RADS Categories in Assessing Axillary Lymph Node Metastasis of Invasive Breast Cancer-A Multicenter Study.

Huiting Zhang,Jinfeng Xu,Yonglin Mou,Ping Zhou,Ligang Cui,Liying Shi,Jingwen Zhang,Chengrong Wu,Weiwei Zhan,Jinping Liu,Yanan Zhao,Fajin Dong,Gang Wu,Yanyan Song,Yang Shao,Cai Chang,Qin Chen,Wenjia Hu,Linxue Qian,Bin Hu,Ping He,Xiaojing Xu,Xiaohong Jia,Huannan Yan,Hongju Yan,Wanyue Deng,Xiaona Xia,Yijie Dong,Qiongchao Jiang,Zhiyao Li,Qiaoying Li,Yao Qi Huang ,Zhi-Rui Chuan ,Yong Xu ,Yongfeng Xu ,Jinhua Zhou

doi:10.3389/fonc.2022.830910

Abstract

PurposeTo 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.Materials and MethodsA 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.ResultsA 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 (p < 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.ConclusionA 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.

Highlights

Female breast cancer, the first cause of the death in malignant tumors [1], is the most common neoplasm in 20–59-year-old women; early diagnosis can reduce 40% of deaths [2–5]
According to the rules established by the study, only one lesion was evaluated per patient, i.e., the most suspicious lesion in ultrasound examination or the largest one among the same Breast Imaging Reporting and Data System (BI-RADS) category was selected in patients with multiple masses
Previous studies have reported that higher elasticity of the tumor was related to lymph node metastasis of breast cancer [22, 23], and further study has revealed that the mean elastic modulus of internal breast tumor could independently predict axillary LN metastasis [17]

Summary

Introduction

The first cause of the death in malignant tumors [1], is the most common neoplasm in 20–59-year-old women; early diagnosis can reduce 40% of deaths [2–5]. According to the American Joint Committee on Cancer, tumor size (T), node (N), and distant metastasis (M) have served as the global standard for conveying disease status among clinicians [6]. Sentinel lymph node biopsy (SLNB) is regarded as the standard method for identifying the staging and determining the clinical arrangement. SLNB may cause complications like arm numbness or upper limb edema in 3.5%– 10.9% patients because of the increasing anesthesia time [10]. An accurate and non-invasive examination for evaluating axillary LN status is expected before surgery or clinical treatment. Showing a great difference among various studies, the sensitivity and specificity of axillary ultrasound in detection of LN metastasis ranged from 45.2% to 92.7% and from 40.5% to 93.9%, respectively [11–14]

Methods

Results

Conclusion