Authors: Fan Wang (Stony Brook University)*; Saarthak Kapse (Indian Institute of Technology Bombay); Steven H. Liu (Stony Brook University); Prateek Prasanna (Stony Brook University); Chao Chen (Stony Brook University)
Abstract: 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 geometrically 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, on treatment-naïve imaging, in patients who respond favorably to therapy versus those who do not.