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Detection and grading of prostate cancer using temporal enhanced ultrasound: combining deep neural networks and tissue mimicking simulations

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International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Purpose 

Temporal Enhanced Ultrasound (TeUS) has been proposed as a new paradigm for tissue characterization based on a sequence of ultrasound radio frequency (RF) data. We previously used TeUS to successfully address the problem of prostate cancer detection in the fusion biopsies.

Methods 

In this paper, we use TeUS to address the problem of grading prostate cancer in a clinical study of 197 biopsy cores from 132 patients. Our method involves capturing high-level latent features of TeUS with a deep learning approach followed by distribution learning to cluster aggressive cancer in a biopsy core. In this hypothesis-generating study, we utilize deep learning based feature visualization as a means to obtain insight into the physical phenomenon governing the interaction of temporal ultrasound with tissue.

Results 

Based on the evidence derived from our feature visualization, and the structure of tissue from digital pathology, we build a simulation framework for studying the physical phenomenon underlying TeUS-based tissue characterization.

Conclusion 

Results from simulation and feature visualization corroborated with the hypothesis that micro-vibrations of tissue microstructure, captured by low-frequency spectral features of TeUS, can be used for detection of prostate cancer.

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Acknowledgements

This work was supported in part by the Natural Sciences and Engineering Research Council of Canada (NSERC) and in part by the Canadian Institutes of Health Research (CIHR).

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Authors and Affiliations

  1. The University of British Columbia, Vancouver, BC, Canada

    Shekoofeh Azizi, Sharareh Bayat, Guy Nir, Septimiu E. Salcudean & Purang Abolmaesumi

  2. Philips Research North America, Cambridge, MA, USA

    Pingkun Yan & Amir Tahmasebi

  3. Sejong University, Gwangjin-Gu, Seoul, South Korea

    Jin Tae Kwak

  4. University of Colorado, Denver, CO, USA

    Storey Wilson, Kenneth A. Iczkowski & M. Scott Lucia

  5. Vancouver Prostate Centre, Vancouver, BC, Canada

    Larry Goldenberg

  6. National Institutes of Health, Bethesda, MD, USA

    Sheng Xu, Peter A. Pinto & Bradford Wood

  7. Queen’s University, Kingston, ON, Canada

    Parvin Mousavi

Authors
  1. Shekoofeh Azizi
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  2. Sharareh Bayat
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  3. Pingkun Yan
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  4. Amir Tahmasebi
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  5. Guy Nir
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  7. Sheng Xu
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  8. Storey Wilson
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  9. Kenneth A. Iczkowski
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  10. M. Scott Lucia
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  11. Larry Goldenberg
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  12. Septimiu E. Salcudean
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  13. Peter A. Pinto
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  14. Bradford Wood
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  15. Purang Abolmaesumi
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  16. Parvin Mousavi
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Corresponding authors

Correspondence to Shekoofeh Azizi, Purang Abolmaesumi or Parvin Mousavi.

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The authors declare that they have no conflict of interest.

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All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

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Informed consent was obtained from all individual participants included in the study.

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Azizi, S., Bayat, S., Yan, P. et al. Detection and grading of prostate cancer using temporal enhanced ultrasound: combining deep neural networks and tissue mimicking simulations. Int J CARS 12, 1293–1305 (2017). https://doi.org/10.1007/s11548-017-1627-0

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  • DOI: https://doi.org/10.1007/s11548-017-1627-0

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