TopoTxR: A topology-guided deep convolutional network for breast parenchyma learning on DCE-MRIs

Fan Wang, Zhilin Zou, Nicole Sakla, Luke Partyka, Nil Rawal, Gagandeep Singh, Wei Zhao, Haibin Ling, Chuan Huang, Prateek Prasanna, Chao Chen

Abstract:

Characterization of breast parenchyma in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI)
is a challenging task owing to the complexity of underlying tissue structures. Existing quantitative approaches,
like radiomics and deep learning models, lack explicit quantification of intricate and subtle parenchymal
structures, including fibroglandular tissue. To address this, we propose a novel topological approach that
explicitly extracts multi-scale topological structures to better approximate breast parenchymal structures, and
then incorporates these structures into a deep-learning-based prediction model via an attention mechanism. Our
topology-informed deep learning model, TopoTxR, leverages topology to provide enhanced insights into tissues
critical for disease pathophysiology and treatment response. We empirically validate TopoTxR using the VICTRE
phantom breast dataset, showing that the topological structures extracted by our model effectively approximate
the breast parenchymal structures. We further demonstrate TopoTxR’s efficacy in predicting response to
neoadjuvant chemotherapy. Our qualitative and quantitative analyses suggest differential topological behavior
of breast tissue in treatment-naïve imaging, in patients who respond favorably to therapy as achieving
pathological complete response (pCR) versus those who do not. In a comparative analysis with several baselines
on the publicly available I-SPY 1 dataset (N = 161, including 47 patients with pCR and 114 without) and the
Rutgers proprietary dataset (N = 120, with 69 patients achieving pCR and 51 not), TopoTxR demonstrates a
notable improvement, achieving a 2.6% increase in accuracy and a 4.6% enhancement in AUC compared to
the state-of-the-art method.