Wanis Kerollos Nashat, Dong Wenli, Shen Yu, Meric-Bernstam Funda, Adesoye Taiwo, Kuerer Henry M, Caudle Abigail S, Tamirisa Nina, DeSnyder Sarah M, Sun Susie X, Bedrosian Isabelle, Singh Puneet, Cox Solange E, Hunt Kelly K, Hwang Rosa F
Department of Breast Surgical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Department of Health Services Research, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
NPJ Breast Cancer. 2025 May 22;11(1):46. doi: 10.1038/s41523-025-00757-4.
The distinction between pN1 and ≥pN2 breast cancer impacts treatment decisions. Using data from a single institution on women with cN0 invasive breast cancer who were treated with upfront surgery, had 1-3 positive SLNs, and underwent completion ALND, we used gradient boosted trees (XGBoost) to develop a model for predicting ≥pN2 disease using clinicopathologic variables. Model performance was tested in a held-out subsample (20%) and validated using data from the National Cancer Database (NCDB). Of 3574 patients with cN0 breast cancer, 587 underwent upfront surgery and had 1-3 positive SLNs. Of these, 415 (70.7%) underwent completion ALND, with 64 (15.4%) having ≥pN2 disease. The trained algorithm had an AUC of 0.87 (95% CI: 0.74, 0.97) in the held-out test data, and 0.78 (95% CI: 0.76, 0.79) in recent NCDB data where completion ALND was much less commonly performed. The number of positive SLNs and the total number of SLNs removed had the greatest influence on model predictions in the held-out test data. The developed model effectively estimates the probability of ≥pN2 disease in cN0 patients with positive SLNs, providing guidance for the management of patients with breast cancer.
pN1与≥pN2期乳腺癌之间的区别会影响治疗决策。利用来自单一机构的关于cN0期浸润性乳腺癌女性患者的数据，这些患者接受了 upfront 手术，前哨淋巴结（SLN）有1 - 3枚阳性，并接受了腋窝淋巴结清扫术（ALND），我们使用梯度提升树（XGBoost），根据临床病理变量建立了一个预测≥pN2期疾病的模型。在一个留出的子样本（20%）中测试了模型性能，并使用国家癌症数据库（NCDB）的数据进行验证。在3574例cN0期乳腺癌患者中，587例接受了 upfront 手术且前哨淋巴结有1 - 3枚阳性。其中，415例（70.7%）接受了腋窝淋巴结清扫术，64例（15.4%）为≥pN2期疾病。在留出的测试数据中，训练后的算法曲线下面积（AUC）为0.87（95%可信区间：0.74，0.97），在近期NCDB数据中为0.78（95%可信区间：0.76，0.79），在该数据集中腋窝淋巴结清扫术的实施频率要低得多。在前哨淋巴结阳性的数量和切除的前哨淋巴结总数中，在前瞻性测试数据中对模型预测的影响最大。所建立的模型有效地估计了前哨淋巴结阳性的cN0患者发生≥pN2期疾病的概率，为乳腺癌患者的管理提供了指导。
