Department of Breast Surgery, The First Affiliated Hospital with Nanjing Medical University, Nanjing 210029, China.
Department of General Surgery, The Affiliated Cancer Hospital of Nanjing Medical University & Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research, Nanjing 210009, China.
Comput Math Methods Med. 2022 Aug 17;2022:1507125. doi: 10.1155/2022/1507125. eCollection 2022.
To construct and validate a radiomic-based model for estimating axillary lymph node (ALN) metastasis in patients with breast cancer by dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI).
In this retrospective study, a radiomic-based model was established in a training cohort of 236 patients with breast cancer. Radiomic features were extracted from breast DCE-MRI scans. A method named the least absolute shrinkage and selection operator (LASSO) was applied to select radiomic features based on highly reproducible features. A radiomic signature was built by a support vector machine (SVM). Multivariate logistic regression analysis was adopted to establish a clinical characteristic-based model. The performance of models was analysed through discrimination ability and clinical benefits.
The radiomic signature comprised 6 features related to ALN metastasis and showed significant differences between the patients with ALN metastasis and without ALN metastasis ( < 0.001). The area under the curve (AUC) of the radiomic model was 0.990 and 0.858, respectively, in the training and validation sets. The clinical feature-based model, including MRI-reported status and palpability, performed slightly worse, with an AUC of 0.784 in the training cohort and 0.789 in the validation cohort. The radiomic signature was confirmed to provide more clinical benefits by decision curve analysis.
The radiomic-based model developed in this study can successfully diagnose the status of lymph nodes in patients with breast cancer, which may reduce unnecessary invasive clinical operations.
通过动态对比增强磁共振成像（DCE-MRI）构建并验证一种基于放射组学的模型，用于预测乳腺癌患者腋窝淋巴结（ALN）转移。
在这项回顾性研究中，我们在 236 例乳腺癌患者的训练队列中建立了一个基于放射组学的模型。从乳腺 DCE-MRI 扫描中提取放射组学特征。使用最小绝对收缩和选择算子（LASSO）方法基于高可重复性特征选择放射组学特征。通过支持向量机（SVM）构建放射组学特征。采用多变量逻辑回归分析建立基于临床特征的模型。通过判别能力和临床获益分析评估模型的性能。
放射组学特征由 6 个与 ALN 转移相关的特征组成，在有 ALN 转移和无 ALN 转移的患者之间存在显著差异（<0.001）。放射组学模型在训练集和验证集中的曲线下面积（AUC）分别为 0.990 和 0.858。包含 MRI 报告状态和可触知性的临床特征模型表现略差，在训练队列中的 AUC 为 0.784，在验证队列中的 AUC 为 0.789。决策曲线分析证实放射组学特征可提供更多的临床获益。
本研究中开发的基于放射组学的模型可成功诊断乳腺癌患者的淋巴结状态，这可能减少不必要的有创临床操作。
