CHINESE JOURNAL OF MEDICINAL GUIDE >
Prediction Model of Axillary Lymph Node Negative after NAC in HER-2 Positive Breast Cancer
Received date: 2025-11-06
Revised date: 2026-03-12
Accepted date: 2026-04-20
Online published: 2026-06-29
Objective： To establish a predictive model of axillary lymph node negative after neoadjuvant chemotherapy （NAC） for human epidermal growth factor receptor 2 （HER-2） positive breast cancer patients based on the apparent diffusion coefficient （ADC） value of magnetic resonance diffusion-weighted imaging （DWI） and the clinical characteristics of patients.Methods： 284 HER-2 positive breast cancer patients who were treated in our hospital from January 2022 to June 2025 were selected， all of whom received NAC treatment. The axillary lymph node negative status was observed， and the differences in clinical characteristics and MRI parameters between patients with and without axillary lymph node negative status were analyzed. A multi-factor logistic regression was employed to screen predictive factors，and build a predictive model of axillary lymph node negative status.Meanwhile， 50 patients with HER-2 positive breast cancer were selected as the external validation set.Results： Among 284 patients， 186 had axillary lymph node negative conversion， with a rate of 65.49%. The proportion of patients under 50 years old， premenopausal， and TNM stage Ⅱ with axillary lymph node negative conversion was 65.59%， 60.22%， and 73.12%， respectively， which was higher than that of patients without axillary lymph node negative conversion （P<0.05）. The proportion of high signal intensity within the tumor on T2WI and the ADC value of patients with axillary lymph node negative conversion was 32.80% and （1.15±0.19）×10-3 mm2·s-1， which was higher than that of patients without axillary lymph node negative conversion （P<0.05）. With the final model variables （ADC values and TNM staging） transformed into a column-line diagram prediction model， the area under the ROC curve of the constructed nomogram model for predicting axillary lymph node negative conversion was 0.918 （95% CI： 0.876-0.959）， with sensitivity and specificity of 97.30% and 73.20%. External validation of the model showed that the area under the ROC curve predicted by the model was 0.845 （95%CI： 0.798-0.901）.Conclusion： The nomogram model based on ADC values and TNM staging constructed in this study demonstrates excellent predictive performance for the conversion of axillary lymph node status to negative in HER-2-positive breast cancer after neoadjuvant chemotherapy， exhibiting superior discrimination and high sensitivity. External validation confirmed its robust predictive ability.
WANG Jiarui, LI Zihan, BAI Junwen
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Prediction Model of Axillary Lymph Node Negative
after NAC in HER-2 Positive Breast Cancer
［1］ Cantini L， Trapani D， Guidi L， et al. Neoadjuvant therapy in hormone Receptor-Positive/HER2-Negative breast cancer［J］.Cancer Treat Rev， 2024，123（8）：e102669.
［2］ 普艳姣，李辉，段雪玉，等.阿贝西利治疗激素受体阳性/人表皮生长因子受体2阴性晚期/转移性乳腺癌的快速卫生技术评估［J］.中国药物评价，2024，41（1）：71-77.
［3］ Bischoff H， Espié M， Petit T. Unveiling neoadjuvant therapy： insights and outlooks for HER2-positive early breast cancer［J］.Curr Treat Options Oncol， 2024，25（9）：1225-1237.
［4］ Zhao F， Polley E， McClellan J， et al. Predicting pathologic complete response to neoadjuvant chemotherapy in breast cancer using a machine learning approach［J］.Breast Cancer Res， 2024，26（1）：148-152.
［5］ 徐明哲，单东秋，曲金荣，等.酰胺质子转移加权成像联合人表皮生长因子受体2状态预测乳腺癌新辅助化疗后病理完全缓解的价值［J］.中华放射学杂志，2025，59（3）：313-320.
［6］ Schettini F， Brasó-Maristany F， Pascual T， et al. Identifying predictors of treatment response and molecular changes induced by neoadjuvant chemotherapy and endocrine therapy in hormone receptor-positive/HER2-negative breast cancer： the NEOENDO translational study［J］.ESMO Open， 2024，9（12）：e103989.
［7］ Cabıoglu N， Karanlik H， Yılmaz R， et al. Targeted axillary dissection reduces residual nodal disease in clinically node- positive breast cancer after neoadjuvant chemotherapy［J］.World J Surg Oncol， 2024，22（1）：178-182.
［8］ Luo Y， Jiang H， Liu C， et al. Axillary downstaging and the impact of clinical axillary status on efficacy of neoadjuvant therapy for HER2-positive breast cancer： a network meta-Analysis［J］.Technol Cancer Res Treat， 2023，22（8）：e150325.
［9］ Fang Y， Zhang Q， Wu Y， et al. HER2-positive is an independent indicator for predicting pathological complete response to neoadjuvant therapy and Ki67-changed after neoadjuvant chemotherapy predicts favorable prognosis in Chinese women with locally advanced breast cancer［J］.Medicine （Baltimore）， 2024，103（6）：e37170.
［10］ Bansal GJ， Jaipal A， Wu GKC，et al. Diagnostic accuracy of magnetic resonance imaging to evaluate axillary lymph node status in breast cancer patients receiving neoadjuvant chemotherapy［J］.Br J Radiol， 2023，96（1143）：e20220904.
［11］ 霍翔，吴兵，方德根，等.HER2阳性乳腺癌新辅助化疗疗效的影响因素及列线图预测模型建立［J］.川北医学院学报，2023，38（1）：126-129.
［12］ Lin Y， Wang J， Li M， et al. Prediction of breast cancer and axillary positive-node response to neoadjuvant chemotherapy based on multi-parametric magnetic resonance imaging radiomics models［J］.Breast， 2024，76（8）：e103737.
［13］ Danzinger S， Pfeifer C， Wimmer S，et al. Neoadjuvant therapy and axillary lymph node status in HER2-positive breast cancer［J］. Cancer Invest， 2023，41（6）：593-600.
［14］ Martínez-Pérez C， Turnbull AK， Kay C， et al. Neoadjuvant endocrine therapy in postmenopausal women with HR+/HER2- breast cancer［J］.Expert Rev Anticancer Ther， 2023，23（1）：67-86.
［15］ 王正通，赵凡，李冲冲，等.磁共振成像与病理学多参数预测中晚期乳腺癌新辅助化疗疗效的价值［J］.中国医师杂志，2024，26（9）：1343-1349.
［16］ Kwak L， Santa-Maria C， Di Carlo P， et al. Can breast MRI predict pathologic response following neoadjuvant chemotherapy for breast cancer？A retrospective cohort study［J］.Clin Imaging， 2023，101（8）：105-112.
［17］ Liu C， Huang X， Chen X， et al. Use of pretreatment multiparametric MRI to predict tumor regression pattern to neoadjuvant chemotherapy in breast cancer［J］.Acad Radiol， 2023，30（2）：62-70.
［18］ Hottat NA， Badr DA， Lecomte S， et al. Assessment of diffusion-weighted MRI in predicting response to neoadjuvant chemotherapy in breast cancer patients［J］.Sci Rep， 2023，13（1）：614-617.
［19］ 丁佳，黄碧云，段庆红.基于MR影像组学预测不同亚型乳腺癌新辅助化疗疗效［J］.实用放射学杂志，2024，40（12）：1979-1983.
［20］ 蔡李芬，朱晓萍.乳腺癌患者新辅助化疗后腋窝淋巴结阴性相关因素及预后价值分析［J］.浙江医学，2024，46（9）：932-937.
［21］ 李阳，李玉梅，邓军.乳腺癌新辅助化疗前后ER，PR，Her-2和Ki-67的变化与化疗疗效的关系分析［J］.中华全科医学，2024，22（9）：1500-1503.
［22］ Portnow LH， Kochkodan-Self JM， Maduram A， et al. Multimodality imaging review of HER2-positive breast cancer and response to neoadjuvant chemotherapy［J］.Radiographics， 2023，43（2）：e220103.
［23］ 孙慧玲，王蕊，张祥，等.DCE-MRI和免疫组化特征对乳腺癌患者新辅助化疗反应早期预测价值［J］.四川医学，2024，45（8）：837-842.
［24］ 黄英，高媛媛，刘新兰，等.扩散加权成像预测Ki-67高表达HER-2阳性乳腺癌新辅助化疗后Ki-67降级的价值［J］.中国医学影像学杂志，2023，31（8）：824-827.
［25］ 贺春燕，吉茹，刘兵.平扫MRI和DCE-MRI对乳腺癌新辅助化疗疗效的预测价值比较［J］.中国临床医学影像杂志，2023，34（1）：24-27.
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