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Ultrasound-Based Predication of Prostate Cancer in MRI-guided Biopsy

  • Nishant Uniyal
  • Farhad Imani
  • Amir Tahmasebi
  • Harsh Agarwal
  • Shyam Bharat
  • Pingkun Yan
  • Jochen Kruecker
  • Jin Tae Kwak
  • Sheng Xu
  • Bradford Wood
  • Peter Pinto
  • Baris Turkbey
  • Peter Choyke
  • Purang Abolmaesumi
  • Parvin Mousavi
  • Mehdi MoradiEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8680)

Abstract

In this paper, we report an in vivo clinical feasibility study for ultrasound-based detection of prostate cancer in MRI selected biopsy targets. Methods: Spectral analysis of a temporal sequence of ultrasound RF data reflected from a fixed location in the tissue results in features that can be used for separating cancerous from benign biopsies. Data from 18 biopsy cores and their respective histopathology are used in an innovative computational framework, consisting of unsupervised and supervised learning, to identify and verify cancer in regions as small as 1 mm \(\times \) 1 mm. Results: In leave-one-subject-out cross validation experiments, an area under ROC of 0.91 is obtained for cancer detection in the biopsy cores. Cancer probability maps that highlight the predicted distribution of cancer along the biopsy core, also closely match histopathology. Our results demonstrate the potential of the RF time series to assist patient-specific targeting during prostate biopsy.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Nishant Uniyal
    • 1
  • Farhad Imani
    • 2
  • Amir Tahmasebi
    • 3
  • Harsh Agarwal
    • 3
  • Shyam Bharat
    • 3
  • Pingkun Yan
    • 3
  • Jochen Kruecker
    • 3
  • Jin Tae Kwak
    • 4
  • Sheng Xu
    • 4
  • Bradford Wood
    • 4
  • Peter Pinto
    • 4
  • Baris Turkbey
    • 4
  • Peter Choyke
    • 4
  • Purang Abolmaesumi
    • 1
  • Parvin Mousavi
    • 2
  • Mehdi Moradi
    • 1
    • 5
    Email author
  1. 1.University of British ColumbiaVancouverCanada
  2. 2.Queen’s UniversityKingstonCanada
  3. 3.Philips Research North AmericaBriarcliff ManorUSA
  4. 4.National Institutes of HealthBethesdaUSA
  5. 5.IBM Almaden Research CenterSan JoseUSA

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