Mao Ning, Dai Yi, Lin Fan, Ma Heng, Duan Shaofeng, Xie Haizhu, Zhao Wenlei, Hong Nan
Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, Yantai, China.
Department of Radiology, Peking University Shenzhen Hospital, Shenzhen, China.
Front Oncol. 2020 Oct 27;10:541849. doi: 10.3389/fonc.2020.541849. eCollection 2020.
This study aimed to establish and validate a radiomics nomogram based on dynamic contrast-enhanced (DCE)-MRI for predicting axillary lymph node (ALN) metastasis in breast cancer.
This retrospective study included 296 patients with breast cancer who underwent DCE-MRI examinations between July 2017 and June 2018. A total of 396 radiomics features were extracted from primary tumor. In addition, the least absolute shrinkage and selection operator (LASSO) algorithm was used to select the features. Radiomics signature and independent risk factors were incorporated to build a radiomics nomogram model. Calibration and receiver operator characteristic (ROC) curves were used to confirm the performance of the nomogram in the training and validation sets. The clinical usefulness of the nomogram was evaluated by decision curve analysis (DCA).
The radiomics signature consisted of three ALN-status-related features, and the nomogram model included the radiomics signature and the MR-reported lymph node (LN) status. The model showed good calibration and discrimination with areas under the ROC curve (AUC) of 0.92 [95% confidence interval (CI), 0.87-0.97] in the training set and 0.90 (95% CI, 0.85-0.95) in the validation set. In the MR-reported LN-negative (cN0) subgroup, the nomogram model also exhibited favorable discriminatory ability (AUC, 0.79; 95% CI, 0.70-0.87). DCA findings indicated that the nomogram model was clinically useful.
The MRI-based radiomics nomogram model could be used to preoperatively predict the ALN metastasis of breast cancer.
本研究旨在建立并验证一种基于动态对比增强（DCE）-MRI的影像组学列线图，用于预测乳腺癌腋窝淋巴结（ALN）转移。
本回顾性研究纳入了296例在2017年7月至2018年6月期间接受DCE-MRI检查的乳腺癌患者。从原发肿瘤中提取了总共396个影像组学特征。此外，使用最小绝对收缩和选择算子（LASSO）算法来选择特征。将影像组学特征和独立危险因素纳入以构建影像组学列线图模型。使用校准曲线和受试者操作特征（ROC）曲线来确认列线图在训练集和验证集中的性能。通过决策曲线分析（DCA）评估列线图的临床实用性。
影像组学特征由三个与ALN状态相关的特征组成，列线图模型包括影像组学特征和MR报告的淋巴结（LN）状态。该模型在训练集中显示出良好的校准和区分能力，ROC曲线下面积（AUC）为0.92 [95%置信区间（CI），0.87 - 0.97]，在验证集中为0.90（95% CI，0.85 - 0.95）。在MR报告的LN阴性（cN0）亚组中，列线图模型也表现出良好的区分能力（AUC，0.79；95% CI，0.70 - 0.87）。DCA结果表明列线图模型具有临床实用性。
基于MRI的影像组学列线图模型可用于术前预测乳腺癌的ALN转移。
