Shang Jiabei, Chen Jianzhe, Gao Xudong, Wan Zhipeng, Yang Ruirong, Lei Zhenli, Chen Siqi, Chen Meining, Quan Yi, Bai Jiao
Department of Breast Surgery, The Affiliated Hospital of Southwest Medical University, Luzhou, China.
Department of Breast Surgery, Tianfu Hospital Affiliated to Southwest Medical University, Meishan, China.
Int J Surg. 2025 Jul 22. doi: 10.1097/JS9.0000000000003070.
Accurate assessment of axillary lymph node (ALN) metastasis is essential for developing an effective treatment strategy for breast cancer (BC). Despite advancements in imaging and surgical techniques, a critical need remains for reliable, non-invasive methods to predict axillary response to neoadjuvant therapy (NAT). This study aimed to identify key factors influencing axillary lymph node pathological complete response (pCR) following NAT and to develop a predictive model for axillary pCR (apCR) to support clinical decision-making regarding the necessity of axillary lymph node dissection (ALND).
Clinical data from female patients diagnosed with breast cancer (BC) between January 2019 and December 2024 were retrospectively collected. All patients had biopsy-confirmed metastasis to ipsilateral axillary lymph nodes at initial presentation, received standardized neoadjuvant therapy (NAT), and subsequently underwent ALND. Patients were randomly divided into a training set (n = 354) and a test set (n = 151) in a 7:3 ratio. Based on ALND results, patients were classified into the apCR (axillary pathological complete response) and non-apCR groups, and their clinicopathological and magnetic resonance imaging (MRI) features were compared. Independent predictors of apCR were identified using multivariate logistic regression analysis, and feature selection was performed using the Least Absolute Shrinkage and Selection Operator (LASSO) method. Two predictive models were developed, a Clinical-Pathological-MRI model and a Clinical-Pathological-Delta-MRI model. The predictive performance of both models was evaluated and compared.
A total of 505 patients were enrolled, including 237 patients in the apCR group and 268 in the non-apCR group. The AUC values for the Clinical-Pathological-MRI model were 0.817 in the training set and 0.680 in the test set. For the Clinical-Pathological-Delta-MRI model, the AUC values were 0.844 in the training set and 0.793 in the test set, indicating superior predictive performance. Decision curve analysis (DCA) further demonstrated that the Clinical-Pathological-Delta-MRI model provided greater net clinical benefit compared to the Clinical-Pathological-MRI model in both the training and test sets.
This model may provide valuable support for individualized surgical decision-making and help guide the selective omission of axillary lymph node dissection in appropriate candidates.
准确评估腋窝淋巴结（ALN）转移对于制定有效的乳腺癌（BC）治疗策略至关重要。尽管影像学和手术技术取得了进展，但仍迫切需要可靠的非侵入性方法来预测腋窝对新辅助治疗（NAT）的反应。本研究旨在确定影响新辅助治疗后腋窝淋巴结病理完全缓解（pCR）的关键因素，并建立腋窝pCR（apCR）的预测模型，以支持关于腋窝淋巴结清扫术（ALND）必要性的临床决策。
回顾性收集2019年1月至2024年12月期间诊断为乳腺癌（BC）的女性患者的临床资料。所有患者在初次就诊时经活检证实同侧腋窝淋巴结转移，接受标准化新辅助治疗（NAT），随后接受ALND。患者按7:3的比例随机分为训练集（n = 354）和测试集（n = 151）。根据ALND结果，将患者分为apCR（腋窝病理完全缓解）组和非apCR组，并比较其临床病理和磁共振成像（MRI）特征。采用多因素逻辑回归分析确定apCR的独立预测因素，并使用最小绝对收缩和选择算子（LASSO）方法进行特征选择。建立了两个预测模型，即临床病理-MRI模型和临床病理-增量-MRI模型。对两个模型的预测性能进行评估和比较。
共纳入505例患者，其中apCR组237例，非apCR组268例。临床病理-MRI模型在训练集中的AUC值为0.817，在测试集中为0.680。临床病理-增量-MRI模型在训练集中的AUC值为0.844，在测试集中为0.793，表明其预测性能更佳。决策曲线分析（DCA）进一步表明，在训练集和测试集中，临床病理-增量-MRI模型比临床病理-MRI模型提供了更大的净临床效益。
该模型可为个体化手术决策提供有价值的支持，并有助于指导在合适的患者中选择性省略腋窝淋巴结清扫术。
