3-D multi-parametric contrast-enhanced ultrasound for the prediction of prostate cancer

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Abstract

Trans-rectal ultrasound-guided 12-core systematic biopsy (SBx) is the standard diagnostic pathway for prostate cancer (PCa) because of a lack of sufficiently accurate imaging. Quantification of 3-D dynamic contrast-enhanced ultrasound (US) might open the way for a targeted procedure in which biopsies are directed at lesions suspicious on imaging. This work describes the expansion of contrast US dispersion imaging algorithms to 3-D and compares its performance against malignant and benign disease. Furthermore, we examined the feasibility of a multi-parametric approach to predict SBx-core outcomes using machine learning. An area under the receiver operating characteristic (ROC) curve of 0.76 and 0.81 was obtained for all PCa and significant PCa, respectively, an improvement over previous US methods. We found that prostatitis, in particular, was a source of false-positive readings.
Original languageEnglish
Pages (from-to)2713-2724
Number of pages12
JournalUltrasound in Medicine and Biology
Volume45
Issue number10
DOIs
Publication statusPublished - 10 Jul 2019

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Keywords

  • Prostate cancer
  • Systematic biopsy
  • Dynamic contrast-enhanced ultrasound
  • Contrast ultrasound dispersion imaging
  • 3-D
  • Machine learning

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