Combining the Ultrasound Features of Primary Tumor and Axillary Lymph Nodes Can Reduce False-Negative Rate during the Prediction of High Axillary Node Burden in BI-RADS Category 4 or 5 Breast Cancer Lesions

Abstract

The purpose of this study was to determine whether incorporation of the ultrasound (US) features of the primary tumor and axillary lymph node (ALN) could improve the prediction of high axillary nodal burden (HNB) and, thus, avoid unnecessary sentinel lymph node biopsy (SLNB). A total of 347 patients with Breast Imaging Reporting and Data System US category 4 or 5 breast cancer lesions were included. Their pre-operative US features and post-operative pathologic results were collected. The patients were then divided into the following groups based on surgical histology: limited nodal burden (LNB: 0–2 LNs involved) and heavy nodal burden (HNB: ≥3 metastatic LNs). Univariate and multivariate logistic regression analyses were conducted to determine the most valuable variables for HNB prediction. Receiver operating characteristic curves were obtained to assess their values. We found that a non-circumscribed margin, cortical thickness (≥3 mm) and number (≥3) of suspicious ALNs are indicators for HNB prediction. The false-negative rate (FNR) in model 1 (cortical thickness + number of suspicious ALNs) was 15.5% versus 3.4% in model 2 (non-circumscribed margin + cortical thickness + number of suspicious ALNs). Our results indicate that combining the US features of the primary tumor and ALNs can reduce the FNR during HNB prediction.