Department of Breast Surgery, Fujian Medical University Union Hospital, Fuzhou, Fujian Province, 350001, China; Department of Breast Surgery, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, Fujian Province, 350014, China.
Department of Breast Surgery, Fujian Medical University Union Hospital, Fuzhou, Fujian Province, 350001, China.
Breast. 2024 Oct;77:103786. doi: 10.1016/j.breast.2024.103786. Epub 2024 Aug 9.
In breast cancer (BC) patients with clinical axillary lymph node metastasis (cN+) undergoing neoadjuvant therapy (NAT), precise axillary lymph node (ALN) assessment dictates therapeutic strategy. There is a critical demand for a precise method to assess the axillary lymph node (ALN) status in these patients.
A retrospective analysis was conducted on 160 BC patients undergoing NAT at Fujian Medical University Union Hospital. We analyzed baseline and two-cycle reassessment dynamic contrast-enhanced MRI (DCE-MRI) images, extracting 3668 radiomic and 4096 deep learning features, and computing 1834 delta-radiomic and 2048 delta-deep learning features. Light Gradient Boosting Machine (LightGBM), Support Vector Machine (SVM), RandomForest, and Multilayer Perceptron (MLP) algorithms were employed to develop risk models and were evaluated using 10-fold cross-validation.
Of the patients, 61 (38.13 %) achieved ypN0 status post-NAT. Univariate and multivariable logistic regression analyses revealed molecular subtypes and Ki67 as pivotal predictors of achieving ypN0 post-NAT. The SVM-based "Data Amalgamation" model that integrates radiomic, deep learning features, and clinical data, exhibited an outstanding AUC of 0.986 (95 % CI: 0.954-1.000), surpassing other models.
Our study illuminates the challenges and opportunities inherent in breast cancer management post-NAT. By introducing a sophisticated, SVM-based "Data Amalgamation" model, we propose a way towards accurate, dynamic ALN assessments, offering potential for personalized therapeutic strategies in BC.
在接受新辅助治疗（NAT）的临床腋窝淋巴结转移（cN+）的乳腺癌（BC）患者中，精确的腋窝淋巴结（ALN）评估决定了治疗策略。因此，迫切需要一种精确的方法来评估这些患者的腋窝淋巴结（ALN）状态。
对在福建医科大学附属协和医院接受 NAT 的 160 例 BC 患者进行回顾性分析。我们分析了基线和两周期再评估的动态对比增强 MRI（DCE-MRI）图像，提取了 3668 个放射组学和 4096 个深度学习特征，并计算了 1834 个 delta 放射组学和 2048 个 delta 深度学习特征。使用 Light Gradient Boosting Machine（LightGBM）、支持向量机（SVM）、随机森林（RandomForest）和多层感知机（MLP）算法开发风险模型，并使用 10 倍交叉验证进行评估。
在这些患者中，有 61 例（38.13%）在接受 NAT 后达到 ypN0 状态。单因素和多因素逻辑回归分析显示，分子亚型和 Ki67 是预测 NAT 后达到 ypN0 状态的关键预测因素。基于 SVM 的“数据融合”模型，该模型整合了放射组学、深度学习特征和临床数据，其 AUC 为 0.986（95%CI：0.954-1.000），明显优于其他模型。
我们的研究揭示了 NAT 后乳腺癌管理中固有的挑战和机遇。通过引入复杂的基于 SVM 的“数据融合”模型，我们提出了一种精确、动态的 ALN 评估方法，为 BC 的个体化治疗策略提供了潜力。
