University College London Medical School, London, UK.
Division of Surgery and Interventional Science, University College London, London, UK.
Breast Cancer Res Treat. 2022 Sep;195(2):161-169. doi: 10.1007/s10549-022-06672-7. Epub 2022 Jul 21.
Axillary staging is an important prognostic factor in breast cancer. Sentinel lymph node biopsy (SNB) is currently used to stage patients who are clinically and radiologically node-negative. Since the establishment that axillary node clearance (ANC) does not improve overall survival in breast-conserving surgery for patients with low-risk biological cancers, axillary management has become increasingly conservative. This study aims to identify and assess the clinical predictive value of variables that could play a role in the quantification of axillary burden, including the accuracy of quantifying abnormal axillary nodes on ultrasound.
A retrospective analysis was conducted of hospital data for female breast cancer patients receiving an ANC at our centre between January 2018 and January 2020. The reference standard for axillary burden was surgical histology following SNB and ANC, allowing categorisation of the patients under 'low axillary burden' (2 or fewer pathological macrometastases) or 'high axillary burden' (> 2). After exploratory univariate analysis, multivariate logistic regression was conducted to determine relationships between the outcome category and candidate predictor variables: patient age at diagnosis, tumour focality, tumour size on ultrasound and number of abnormal lymph nodes on axillary ultrasound.
One hundred and thirty-five patients were included in the analysis. Logistic regression showed that the number of abnormal lymph nodes on axillary ultrasound was the strongest predictor of axillary burden and statistically significant (P = 0.044), with a sensitivity of 66.7% and specificity of 86.8% (P = 0.011).
Identifying the number of abnormal lymph nodes on preoperative ultrasound can help to quantify axillary nodal burden and identify patients with high axillary burden, and should be documented as standard in axillary ultrasound reports of patients with breast cancer.
腋窝分期是乳腺癌的一个重要预后因素。前哨淋巴结活检（SNB）目前用于对临床和影像学上淋巴结阴性的患者进行分期。由于腋窝淋巴结清扫（ANC）并不能改善低危生物学癌症保乳手术患者的总体生存率，因此腋窝管理变得越来越保守。本研究旨在确定和评估可能在腋窝负担量化中发挥作用的变量的临床预测价值，包括超声定量异常腋窝淋巴结的准确性。
对 2018 年 1 月至 2020 年 1 月期间在我院接受 ANC 的女性乳腺癌患者的医院数据进行回顾性分析。腋窝负担的参考标准是 SNB 和 ANC 后的手术组织学，允许将患者分为“低腋窝负担”（2 个或更少的病理性巨转移）或“高腋窝负担”（>2 个）。在进行探索性单变量分析后，进行多变量逻辑回归以确定结果类别与候选预测变量之间的关系：诊断时患者年龄、肿瘤局灶性、超声上肿瘤大小和腋窝超声上异常淋巴结数量。
共纳入 135 例患者进行分析。逻辑回归显示，腋窝超声上异常淋巴结数量是腋窝负担的最强预测因素，具有统计学意义（P=0.044），其敏感性为 66.7%，特异性为 86.8%（P=0.011）。
术前超声识别异常淋巴结数量有助于定量腋窝淋巴结负担，并识别高腋窝负担患者，在乳腺癌患者的腋窝超声报告中应作为标准记录。
