Skarping Ida, Bendahl Pär-Ola, Szulkin Robert, Alkner Sara, Andersson Yvette, Bergkvist Leif, Christiansen Peer, Filtenborg Tvedskov Tove, Frisell Jan, Gentilini Oreste D, Kontos Michalis, Kühn Thorsten, Lundstedt Dan, Vrou Offersen Birgitte, Olofsson Bagge Roger, Reimer Toralf, Sund Malin, Rydén Lisa, de Boniface Jana
Division of Oncology, Department of Clinical Sciences, Lund University, Lund, Sweden.
Department of Clinical Physiology and Nuclear Medicine, Skane University Hospital, Lund, Sweden.
JAMA Surg. 2024 Dec 1;159(12):1393-1403. doi: 10.1001/jamasurg.2024.3944.
In patients with clinically node-negative (cN0) breast cancer and 1 or 2 sentinel lymph node (SLN) macrometastases, omitting completion axillary lymph node dissection (CALND) is standard. High nodal burden (≥4 axillary nodal metastases) is an indication for intensified treatment in luminal breast cancer; hence, abstaining from CALND may result in undertreatment.
To develop a prediction model for high nodal burden in luminal ERBB2-negative breast cancer (all histologic types and lobular breast cancer separately) without CALND.
DESIGN, SETTING, AND PARTICIPANTS: The prospective Sentinel Node Biopsy in Breast Cancer: Omission of Axillary Clearance After Macrometastases (SENOMAC) trial randomized patients 1:1 to CALND or its omission from January 2015 to December 2021 among adult patients with cN0 T1-T3 breast cancer and 1 or 2 SLN macrometastases across 5 European countries. The cohort was randomly split into training (80%) and test (20%) sets, with equal proportions of high nodal burden. Prediction models were developed by multivariable logistic regression in the complete luminal ERBB2-negative cohort and a lobular breast cancer subgroup. Nomograms were constructed. The present diagnostic/prognostic study presents the results of a prespecified secondary analysis of the SENOMAC trial. Herein, only patients with luminal ERBB2-negative tumors assigned to CALND were selected. Data analysis for this article took place from June 2023 to April 2024.
Predictors of high nodal burden.
High nodal burden was defined as ≥4 axillary nodal metastases. The luminal prediction model was evaluated regarding discrimination and calibration.
Of 1010 patients (median [range] age, 61 [34-90] years; 1006 [99.6%] female and 4 [0.4%] male), 138 (13.7%) had a high nodal burden and 212 (21.0%) had lobular breast cancer. The model in the training set (n = 804) included number of SLN macrometastases, presence of SLN micrometastases, SLN ratio, presence of SLN extracapsular extension, and tumor size (not included in lobular subgroup). Upon validation in the test set (n = 201), the area under the receiver operating characteristic curve (AUC) was 0.74 (95% CI, 0.62-0.85) and the calibration was satisfactory. At a sensitivity threshold of ≥80%, all but 5 low-risk patients were correctly classified corresponding to a negative predictive value of 94%. The prediction model for the lobular subgroup reached an AUC of 0.74 (95% CI, 0.66-0.83).
The predictive models and nomograms may facilitate systemic treatment decisions without exposing patients to the risk of arm morbidity due to CALND. External validation is needed.
ClinicalTrials.gov Identifier: NCT02240472.
在临床淋巴结阴性（cN0）乳腺癌且有1个或2个前哨淋巴结（SLN）大转移灶的患者中，省略腋窝淋巴结清扫术（CALND）是标准做法。高淋巴结负荷（≥4个腋窝淋巴结转移灶）是管腔型乳腺癌强化治疗的指征；因此，不进行CALND可能导致治疗不足。
建立一种在未进行CALND的情况下预测管腔型ERBB2阴性乳腺癌（分别针对所有组织学类型和小叶型乳腺癌）高淋巴结负荷的预测模型。
设计、设置和参与者：乳腺癌前哨淋巴结活检：大转移灶后省略腋窝清扫术（SENOMAC）前瞻性试验在2015年1月至2021年12月期间，将5个欧洲国家的成年cN0 T1 - T3乳腺癌且有1个或2个SLN大转移灶的患者按1:1随机分为CALND组或省略CALND组。该队列被随机分为训练集（80%）和测试集（20%），高淋巴结负荷比例相等。通过多变量逻辑回归在完整的管腔型ERBB2阴性队列和小叶型乳腺癌亚组中建立预测模型，并构建列线图。本诊断/预后研究展示了SENOMAC试验预先指定的二次分析结果。在此，仅选择分配到CALND组的管腔型ERBB2阴性肿瘤患者。本文的数据分析于2023年6月至2024年4月进行。
高淋巴结负荷的预测因素。
高淋巴结负荷定义为≥4个腋窝淋巴结转移灶。对管腔型预测模型进行判别和校准评估。
1010例患者（中位[范围]年龄，61[34 - 90]岁；1006例[99.6%]为女性，4例[0.4%]为男性）中，138例（13.7%）有高淋巴结负荷，212例（21.0%）为小叶型乳腺癌。训练集（n = 804）中的模型包括SLN大转移灶数量、SLN微转移灶的存在情况、SLN比率、SLN包膜外侵犯的存在情况以及肿瘤大小（小叶型亚组中不包括）。在测试集（n = 201）中验证时，受试者操作特征曲线（AUC）下面积为0.74（95%CI，0.62 - 0.85），校准情况良好。在灵敏度阈值≥80%时，除5例低风险患者外，所有患者均被正确分类，阴性预测值为94%。小叶型亚组的预测模型AUC为0.74（95%CI，0.66 - 0.83）。
预测模型和列线图可能有助于做出全身治疗决策，而不会使患者面临因CALND导致手臂发病的风险。需要进行外部验证。
ClinicalTrials.gov标识符：NCT02240472。
