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Multi-parametric 3D Quantitative Ultrasound Vibro-Elastography Imaging for Detecting Palpable Prostate Tumors

  • Omid Mohareri
  • Angelica Ruszkowski
  • Julio Lobo
  • Joseph Ischia
  • Ali Baghani
  • Guy Nir
  • Hani Eskandari
  • Edward Jones
  • Ladan Fazli
  • Larry Goldenberg
  • Mehdi Moradi
  • Septimiu Salcudean
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8673)

Abstract

In this article, we describe a system for detecting dominant prostate tumors, based on a combination of features extracted from a novel multi-parametric quantitative ultrasound elastography technique. The performance of the system was validated on a data-set acquired from n = 10 patients undergoing radical prostatectomy. Multi-frequency steady-state mechanical excitations were applied to each patient’s prostate through the perineum and prostate tissue displacements were captured by a transrectal ultrasound system. 3D volumetric data including absolute value of tissue elasticity, strain and frequency-response were computed for each patient. Based on the combination of all extracted features, a random forest classification algorithm was used to separate cancerous regions from normal tissue, and to compute a measure of cancer probability. Registered whole mount histopathology images of the excised prostate gland were used as a ground truth of cancer distribution for classifier training. An area under receiver operating characteristic curve of 0.82±0.01 was achieved in a leave-one-patient-out cross validation. Our results show the potential of multi-parametric quantitative elastography for prostate cancer detection for the first time in a clinical setting, and justify further studies to establish whether the approach can have clinical use.

Keywords

Radical Prostatectomy Random Forest Peripheral Zone Acoustic Radiation Force Impulse Shear Wave Elastography 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Omid Mohareri
    • 1
  • Angelica Ruszkowski
    • 1
  • Julio Lobo
    • 1
  • Joseph Ischia
    • 2
  • Ali Baghani
    • 3
  • Guy Nir
    • 1
  • Hani Eskandari
    • 1
    • 3
  • Edward Jones
    • 4
  • Ladan Fazli
    • 4
  • Larry Goldenberg
    • 2
  • Mehdi Moradi
    • 1
  • Septimiu Salcudean
    • 1
  1. 1.Department of Electrical and Computer EngineeringUniversity of British ColumbiaVancouverCanada
  2. 2.Department of Urological SciencesUniversity of British ColumbiaVancouverCanada
  3. 3.Ultrasonix Medical CorporationRichmondCanada
  4. 4.Department of Pathology and Laboratory MedicineUniversity of British ColumbiaVancouverCanada

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