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

  • Shekoofeh AziziEmail author
  • Sharareh Bayat
  • Pingkun Yan
  • Amir Tahmasebi
  • Guy Nir
  • Jin Tae Kwak
  • Sheng Xu
  • Storey Wilson
  • Kenneth A. Iczkowski
  • M. Scott Lucia
  • Larry Goldenberg
  • Septimiu E. Salcudean
  • Peter A. Pinto
  • Bradford Wood
  • Purang AbolmaesumiEmail author
  • Parvin MousaviEmail author
Original Article

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.

Keywords

Temporal enhanced ultrasound Deep learning Deep belief network Cancer grading Prostate cancer 

Notes

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).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

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.

Informed consent

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

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Copyright information

© CARS 2017

Authors and Affiliations

  • Shekoofeh Azizi
    • 1
    Email author
  • Sharareh Bayat
    • 1
  • Pingkun Yan
    • 2
  • Amir Tahmasebi
    • 2
  • Guy Nir
    • 1
  • Jin Tae Kwak
    • 3
  • Sheng Xu
    • 6
  • Storey Wilson
    • 4
  • Kenneth A. Iczkowski
    • 4
  • M. Scott Lucia
    • 4
  • Larry Goldenberg
    • 5
  • Septimiu E. Salcudean
    • 1
  • Peter A. Pinto
    • 6
  • Bradford Wood
    • 6
  • Purang Abolmaesumi
    • 1
    Email author
  • Parvin Mousavi
    • 7
    Email author
  1. 1.The University of British ColumbiaVancouverCanada
  2. 2.Philips Research North AmericaCambridgeUSA
  3. 3.Sejong University, Gwangjin-GuSeoulSouth Korea
  4. 4.University of ColoradoDenverUSA
  5. 5.Vancouver Prostate CentreVancouverCanada
  6. 6.National Institutes of HealthBethesdaUSA
  7. 7.Queen’s UniversityKingstonCanada

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