乳腺癌是一种异质性疾病，乳腺癌的分子亚型与治疗和预后密切相关。因此，这项工作的目标是区分乳腺癌的管腔亚型和非管腔亚型。分层放射组学网络（HRadNet）被提出用于基于动态增强磁共振成像的乳腺癌分子亚型预测。 HRadNet 将多层特征与图像元数据融合，以利用传统放射组学方法和通用卷积神经网络。采用两阶段训练机制，提高网络对多中心乳腺癌数据的泛化能力。消融研究显示了 HRadNet 每个组件的有效性。此外，还分析了不同层特征和元数据融合的影响。它表明，为指定领域选择某些特征层可以进一步提高性能。来自不同设备的三个数据集的实验结果证明了所提出网络的有效性。 HRadNet 在无需微调的情况下转移到其他域时也具有良好的性能。

Breast cancer is a heterogeneous disease, where molecular subtypes of breast cancer are closely related to the treatment and prognosis. Therefore, the goal of this work is to differentiate between luminal and non-luminal subtypes of breast cancer. The hierarchical radiomics network (HRadNet) is proposed for breast cancer molecular subtypes prediction based on dynamic contrast-enhanced magnetic resonance imaging. HRadNet fuses multilayer features with the metadata of images to take advantage of conventional radiomics methods and general convolutional neural networks. A two-stage training mechanism is adopted to improve the generalization capability of the network for multicenter breast cancer data. The ablation study shows the effectiveness of each component of HRadNet. Furthermore, the influence of features from different layers and metadata fusion are also analyzed. It reveals that selecting certain layers of features for a specified domain can make further performance improvements. Experimental results on three data sets from different devices demonstrate the effectiveness of the proposed network. HRadNet also has good performance when transferring to other domains without fine-tuning.