Xu Tingting, Zhang Xueli, Tang Huan, Hua Bd Ting, Xiao Fuxia, Cui Zhijun, Tang Guangyu, Zhang Lin
Department of Radiology, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, China.
Department of Radiology, Huadong Hospital of Fudan University, Shanghai, China.
J Comput Assist Tomogr. 2025;49(3):407-416. doi: 10.1097/RCT.0000000000001691. Epub 2024 Nov 25.
This study aimed to investigate the value of radiomics analysis in the precise diagnosis of triple-negative breast cancer (TNBC) based on breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and apparent diffusion coefficient (ADC) maps.
This retrospective study included 326 patients with pathologically proven breast cancer (TNBC: 129, non-TNBC: 197). The lesions were segmented using the ITK-SNAP software, and whole-volume radiomics features were extracted using a radiomics platform. Radiomics features were obtained from DCE-MRI and ADC maps. The least absolute shrinkage and selection operator regression method was employed for feature selection. Three prediction models were constructed using a support vector machine classifier: Model A (based on the selected features of the ADC maps), Model B (based on the selected features of DCE-MRI), and Model C (based on the selected features of both combined). Receiver operating characteristic curves were used to evaluate the diagnostic performance of the conventional MR image model and the 3 radiomics models in predicting TNBC.
In the training dataset, the AUCs for the conventional MR image model and the 3 radiomics models were 0.749, 0.801, 0.847, and 0.896. The AUCs for the conventional MR image model and 3 radiomics models in the validation dataset were 0.693, 0.742, 0.793, and 0.876, respectively.
Radiomics based on the combination of whole volume DCE-MRI and ADC maps is a promising tool for distinguishing between TNBC and non-TNBC.
本研究旨在探讨基于乳腺动态对比增强磁共振成像（DCE-MRI）和表观扩散系数（ADC）图的放射组学分析在三阴性乳腺癌（TNBC）精确诊断中的价值。
本回顾性研究纳入了326例经病理证实的乳腺癌患者（TNBC：129例，非TNBC：197例）。使用ITK-SNAP软件对病变进行分割，并使用放射组学平台提取全容积放射组学特征。从DCE-MRI和ADC图中获取放射组学特征。采用最小绝对收缩和选择算子回归方法进行特征选择。使用支持向量机分类器构建了三个预测模型：模型A（基于ADC图的选定特征）、模型B（基于DCE-MRI的选定特征）和模型C（基于两者组合的选定特征）。使用受试者操作特征曲线评估传统MR图像模型和三个放射组学模型在预测TNBC方面的诊断性能。
在训练数据集中，传统MR图像模型和三个放射组学模型的AUC分别为0.749、0.801、0.847和0.896。在验证数据集中，传统MR图像模型和三个放射组学模型的AUC分别为0.693、0.742、0.793和0.876。
基于全容积DCE-MRI和ADC图组合的放射组学是区分TNBC和非TNBC的一种有前景的工具。
