Svensson Miriam, Bendahl Pär-Ola, Alkner Sara, Hansson Emma, Rydén Lisa, Dihge Looket
Department of Clinical Sciences, Division of Surgery, Lund University, Lund, Sweden.
Department of Clinical Sciences, Division of Oncology and Pathology, Lund University, Lund, Sweden.
BJS Open. 2025 Mar 4;9(2). doi: 10.1093/bjsopen/zraf047.
Postmastectomy radiotherapy (PMRT) impairs the outcome of immediate breast reconstruction in patients with breast cancer, and the sentinel lymph node (SLN) status is crucial in evaluating the need for PMRT. The aim of this study was to develop and validate models to stratify the risk of clinically significant SLN macrometastases (macro-SLNMs) before surgery.
Women diagnosed with clinically node-negative (cN0) T1-2 breast cancer were identified within the Swedish National Quality Register for Breast Cancer (2014-2017). Prediction models and corresponding nomograms based on patient and tumour characteristics accessible before surgery were developed using adaptive least absolute shrinkage and selection operator logistic regression. The prediction of at least one and more than two macro-SLNMs adheres to the current guidelines on use of PMRT and reflects the exclusion criteria in ongoing trials aiming to de-escalate locoregional radiotherapy in patients with one or two macro-SLNMs. Predictive performance was evaluated using area under the receiver operating characteristic curve (AUC) and calibration plots.
Overall, 18 185 women were grouped into development (13 656) and validation (4529) cohorts. The well calibrated models predicting at least one and more than two macro-SLNMs had AUCs of 0.708 and 0.740, respectively, upon validation. By using the prediction model for at least one macro-SLNM, the risk could be updated from the pretest population prevalence of 13.2% to the post-test range of 1.6-74.6%.
Models based on routine patient and tumour characteristics could be used for prediction of SLN status that would indicate the need for PMRT and assist decision-making on immediate breast reconstruction for patients with cN0 breast cancer.
乳腺癌患者乳房切除术后放疗（PMRT）会影响即刻乳房重建的效果，前哨淋巴结（SLN）状态对于评估是否需要进行PMRT至关重要。本研究的目的是开发并验证术前对临床显著SLN大转移灶（macro-SLNMs）风险进行分层的模型。
在瑞典国家乳腺癌质量登记处（2014 - 2017年）中识别出诊断为临床淋巴结阴性（cN0）的T1-2期乳腺癌女性患者。使用自适应最小绝对收缩和选择算子逻辑回归，基于术前可获取的患者和肿瘤特征开发预测模型及相应的列线图。对至少一个及两个以上macro-SLNMs的预测符合当前PMRT使用指南，并反映了正在进行的旨在降低有一个或两个macro-SLNMs患者局部区域放疗强度的试验中的排除标准。使用受试者操作特征曲线下面积（AUC）和校准图评估预测性能。
总体而言，18185名女性被分为开发队列（13656名）和验证队列（4529名）。验证时预测至少一个及两个以上macro-SLNMs的校准良好的模型的AUC分别为0.708和0.740。通过使用至少一个macro-SLNM的预测模型，风险可从测试前总体患病率13.2%更新至测试后范围1.6% - 74.6%。
基于常规患者和肿瘤特征的模型可用于预测SLN状态，这将表明是否需要进行PMRT，并有助于为cN0期乳腺癌患者的即刻乳房重建提供决策依据。
