Department of Radiology, Yantai Yuhuangding Hospital, The Affiliated Hospital of Qingdao University, No. 20 Yuhuangding East Street, Yantai, 264000, Shandong, People's Republic of China.
Department of Radiology, Peking University People's Hospital, No. 11 Xizhimen South Street, Beijing, 100044, People's Republic of China.
Eur Radiol. 2020 Dec;30(12):6732-6739. doi: 10.1007/s00330-020-07016-z. Epub 2020 Jun 30.
This study aims to establish and validate a radiomics nomogram based on contrast-enhanced spectral mammography (CESM) for prediction of axillary lymph node (ALN) metastasis in breast cancer.
This retrospective study included 394 patients with breast cancer who underwent CESM examination in two hospitals. The least absolute shrinkage and selection operator (LASSO) logistic regression was established for feature selection and utilized to construct radiomics signature. The nomogram model included the radiomics signature and independent clinical factors. The receiver operating characteristic (ROC) curves were used to confirm the performance of the nomogram in training and validation sets.
The nomogram model, which includes the radiomics signature and the CESM-reported lymph node status, has areas under the ROC curves of 0.774 (95% confidence interval (CI) 0.689-0.858), 0.767 (95% CI 0.583-0.857), and 0.79 (95% CI 0.63-0.94) in the training, internal validation, and external validation sets, respectively. We identified the cutoff score in the radiomics nomogram as - 1.49, which corresponded to a total point of 49 that could diagnose ALN metastasis with a sensitivity of > 95%.
The CESM-based radiomics nomogram is a noninvasive predictive tool that shows good application prospects in the preoperative prediction of ALN metastasis in breast cancer.
• The CESM-based radiomics nomogram shows good performance in predicting ALN metastasis in breast cancer. • The application of radiomics nomogram in this study provides a new approach for establishing a prediction model with multiple characteristics. • The nomogram has good application prospects in assisting clinical decision makers.
本研究旨在建立并验证基于对比增强能谱乳腺摄影术（CESM）的放射组学列线图，用于预测乳腺癌的腋窝淋巴结（ALN）转移。
本回顾性研究纳入了在两家医院接受 CESM 检查的 394 例乳腺癌患者。采用最小绝对值收缩和选择算子（LASSO）逻辑回归进行特征选择，并用于构建放射组学特征。列线图模型包括放射组学特征和独立的临床因素。使用受试者工作特征（ROC）曲线来验证训练集和验证集的列线图性能。
包括放射组学特征和 CESM 报告的淋巴结状态的列线图模型，在训练集、内部验证集和外部验证集中的 ROC 曲线下面积分别为 0.774（95%置信区间[CI]：0.689-0.858）、0.767（95% CI：0.583-0.857）和 0.79（95% CI：0.63-0.94）。我们确定了放射组学列线图中的截断评分，即 -1.49，对应的总分是 49，用于诊断 ALN 转移的敏感度超过 95%。
基于 CESM 的放射组学列线图是一种非侵入性预测工具，在术前预测乳腺癌的 ALN 转移方面具有良好的应用前景。
基于 CESM 的放射组学列线图在预测乳腺癌的 ALN 转移方面表现良好。
本研究中放射组学列线图的应用为建立具有多个特征的预测模型提供了一种新方法。
列线图在协助临床决策者方面具有良好的应用前景。
