Characterization of breast parenchyma on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a challenging task owing to the complexity of underlying tissue structures. Current quantitative approaches, including radiomics and deep learning models, do not explicitly capture the complex and subtle parenchymal structures, such as fibroglandular tissue. In this paper, we propose a novel method to direct a neural network's attention to a dedicated set of voxels surrounding biologically relevant tissue structures. By extracting multi-dimensional topological structures with high saliency, we build a topology-derived biomarker, TopoTxR. We demonstrate the efficacy of TopoTxR in predicting response to neoadjuvant chemotherapy in breast cancer. Our qualitative and quantitative results suggest differential topological behavior of breast tissue on treatment-na\"ive imaging, in patients who respond favorably to therapy versus those who do not.

本文提出了一种新颖的方法，通过提取具有高显著性的多维拓扑结构，将神经网络的注意力引导到专用的一组体素，从而构建了一种拓扑导出的生物标志物，TopoTxR。实验结果表明，在乳腺癌新辅助化疗反应预测方面，TopoTxR 的有效性优于当前定量方法，且提示治疗前的影像学信息在预测疗效方面具有潜在的作用。

TopoTxR: 一种用于预测乳腺癌治疗反应的拓扑生物标志物