Machine learning for the prediction of prostate cancer biopsy based on 3D dynamic contrast-enhanced ultrasound quantification: 2018 IEEE International Ultrasonics Symposium (IUS)

Research output: Contribution to conferencePaperAcademic

Abstract

Non-targeted transrectal-ultrasound-guided 12-core systematic biopsy (SBx) is the current guideline-recommended clinical pathway for prostate cancer (PCa) diagnosis, despite being associated with a risk of complications as well as un-derdiagnosis or overtreatment. Quantification algorithms for dynamic contrast-enhanced ultrasound (DCE-US) have shown good potential for PCa localisation in two dimensions (2D), and a few have recently been expanded to 3D. In this work, we present a 3D implementation of all estimators in the contrast ultrasound dispersion imaging (CUDI) family and exploit combinations of the extracted parameters to predict individual SBx-core outcomes. We show that machine-learning approaches can improve the classification performance compared to individual CUDI parameters and foresee potential for further development in image-based PCa localisation.
Original languageEnglish
Pages1-9
Number of pages9
DOIs
Publication statusPublished - 17 Dec 2018
Event2018 IEEE International Ultrasonics Symposium, IUS 2018 - Kobe, Japan
Duration: 22 Oct 201825 Oct 2018

Conference

Conference2018 IEEE International Ultrasonics Symposium, IUS 2018
Abbreviated titleIUS 2018
CountryJapan
CityKobe
Period22/10/1825/10/18

Keywords

  • Three-dimensional displays
  • Biopsy
  • Principal component analysis
  • Dispersion
  • Machine learning
  • Prostate cancer
  • Prostate Cancer
  • Systematic Biopsy
  • Dynamic Contrast-Enhanced Ultrasound
  • CUDI
  • 3D
  • Machine Learning

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