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TopoTxR: A Topological Biomarker for Predicting Treatment Response in Breast Cancer

  • Stony Brook University

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

24 Scopus citations

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 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-naïve imaging, in patients who respond favorably to therapy versus those who do not.

Original languageEnglish
Title of host publicationInformation Processing in Medical Imaging - 27th International Conference, IPMI 2021, Proceedings
EditorsAasa Feragen, Stefan Sommer, Julia Schnabel, Mads Nielsen
PublisherSpringer Science and Business Media Deutschland GmbH
Pages386-397
Number of pages12
ISBN (Print)9783030781903
DOIs
StatePublished - 2021
Event27th International Conference on Information Processing in Medical Imaging, IPMI 2021 - Virtual, Online
Duration: Jun 28 2021Jun 30 2021

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12729 LNCS

Conference

Conference27th International Conference on Information Processing in Medical Imaging, IPMI 2021
CityVirtual, Online
Period06/28/2106/30/21

Keywords

  • Breast cancer
  • Neoadjuvant chemotherapy
  • Persistent homology
  • Topology

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