Department of Radiology, Stony Brook University School of Medicine, Stony Brook, NY; Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY.
Department of Radiology, Stony Brook University School of Medicine, Stony Brook, NY.
Clin Breast Cancer. 2020 Feb;20(1):68-79.e1. doi: 10.1016/j.clbc.2019.06.006. Epub 2019 Jun 26.
Longitudinal monitoring of breast tumor volume over the course of chemotherapy is informative of pathologic response. This study aims to determine whether axillary lymph node (aLN) volume by magnetic resonance imaging (MRI) could augment the prediction accuracy of treatment response to neoadjuvant chemotherapy (NAC).
Level-2a curated data from the I-SPY-1 TRIAL (2002-2006) were used. Patients had stage 2 or 3 breast cancer. MRI was acquired pre-, during, and post-NAC. A subset with visible aLNs on MRI was identified (N = 132). Prediction of pathologic complete response (PCR) was made using breast tumor volume changes, nodal volume changes, and combined breast tumor and nodal volume changes with sub-stratification with and without large lymph nodes (3 mL or ∼1.79 cm diameter cutoff). Receiver operating characteristic curve analysis was used to quantify prediction performance.
The rate of change of aLN and breast tumor volume were informative of pathologic response, with prediction being most informative early in treatment (area under the curve (AUC), 0.57-0.87) compared with later in treatment (AUC, 0.50-0.75). Larger aLN volume was associated with hormone receptor negativity, with the largest nodal volume for triple negative subtypes. Sub-stratification by node size improved predictive performance, with the best predictive model for large nodes having AUC of 0.87.
aLN MRI offers clinically relevant information and has the potential to predict treatment response to NAC in patients with breast cancer.
化疗过程中对乳腺肿瘤体积的纵向监测可提供病理反应信息。本研究旨在确定磁共振成像（MRI）检测腋窝淋巴结（aLN）体积是否可以提高新辅助化疗（NAC）治疗反应预测的准确性。
使用 I-SPY-1 试验（2002-2006 年）的 2a 级精选数据。患者患有 2 或 3 期乳腺癌。在 NAC 前、期间和之后采集 MRI。确定了一组在 MRI 上可见 aLN 的患者（N=132）。使用乳腺肿瘤体积变化、淋巴结体积变化以及乳腺肿瘤和淋巴结体积的联合变化来预测病理完全缓解（PCR），并进行了大淋巴结（3 mL 或约 1.79 cm 直径截断）亚组分层。使用接收者操作特征曲线分析来量化预测性能。
aLN 和乳腺肿瘤体积的变化率与病理反应相关，与治疗后期相比，治疗早期的预测信息更丰富（曲线下面积（AUC），0.57-0.87）。淋巴结体积越大，与激素受体阴性相关，三阴性亚型的淋巴结体积最大。根据节点大小进行分层可提高预测性能，对于大节点，最佳预测模型的 AUC 为 0.87。
aLN MRI 提供了临床相关信息，并有潜力预测乳腺癌患者 NAC 的治疗反应。
