Deep learning identification of stiffness markers in breast cancer.

Journal: Biomaterials
Published Date: Apr 27, 2022

Abstract

While essential to our understanding of solid tumor progression, the study of cell and tissue mechanics has yet to find traction in the clinic. Determining tissue stiffness, a mechanical property known to promote a malignant phenotype in vitro and in vivo, is not part of the standard algorithm for the diagnosis and treatment of breast cancer. Instead, clinicians routinely use mammograms to identify malignant lesions and radiographically dense breast tissue is associated with an increased risk of developing cancer. Whether breast density is related to tumor tissue stiffness, and what cellular and non-cellular components of the tumor contribute the most to its stiffness are not well understood. Through training of a deep learning network and mechanical measurements of fresh patient tissue, we create a bridge in understanding between clinical and mechanical markers. The automatic identification of cellular and extracellular features from hematoxylin and eosin (H&E)-stained slides reveals that global and local breast tissue stiffness best correlate with the percentage of straight collagen. Importantly, the percentage of dense breast tissue does not directly correlate with tissue stiffness or straight collagen content.

Authors

  • Alexandra Sneider
    Department of Chemical and Biomolecular Engineering, Johns Hopkins Physical Sciences-Oncology Center, Institute for NanoBioTechnology, Johns Hopkins University, 3400 N Charles St, Baltimore, MD, 21218, USA.
  • Ashley Kiemen
    Department of Chemical and Biomolecular Engineering, Johns Hopkins Physical Sciences-Oncology Center, Institute for NanoBioTechnology, Johns Hopkins University, 3400 N Charles St, Baltimore, MD, 21218, USA.
  • Joo Ho Kim
    Department of Materials Science and Engineering, Institute for NanoBioTechnology, Johns Hopkins University, 3400 N Charles St, Baltimore, MD, 21218, USA.
  • Pei-Hsun Wu
    Department of Chemical and Biomolecular Engineering, Johns Hopkins Physical Sciences-Oncology Center, Institute for NanoBioTechnology, Johns Hopkins University, 3400 N Charles St, Baltimore, MD, 21218, USA.
  • Mehran Habibi
    Johns Hopkins Breast Center, Johns Hopkins Bayview Medical Center, 4940 Eastern Ave, Baltimore, MD, 21224, USA.
  • Marissa White
    Department of Pathology, Johns Hopkins School of Medicine, 401 N Broadway, Baltimore, MD, 21231, USA.
  • Jude M Phillip
    Department of Chemical and Biomolecular Engineering, Johns Hopkins Physical Sciences-Oncology Center, Institute for NanoBioTechnology, Johns Hopkins University, 3400 N Charles St, Baltimore, MD, 21218, USA; Department of Biomedical Engineering, Johns Hopkins University, 3400 N Charles St, Baltimore, MD, 21218, USA.
  • Luo Gu
    Department of Materials Science and Engineering, Institute for NanoBioTechnology, Johns Hopkins University, 3400 N Charles St, Baltimore, MD, 21218, USA.
  • Denis Wirtz
    Department of Chemical and Biomolecular Engineering, Johns Hopkins Physical Sciences-Oncology Center, Institute for NanoBioTechnology, Johns Hopkins University, 3400 N Charles St, Baltimore, MD, 21218, USA; Department of Pathology, Johns Hopkins School of Medicine, 401 N Broadway, Baltimore, MD, 21231, USA; Department of Oncology, Johns Hopkins School of Medicine, 1800 Orleans St, Baltimore, MD, 21205, USA. Electronic address: wirtz@jhu.edu.

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