Published in:
Open Access
01-08-2017 | Urogenital
Multiparametric dynamic contrast-enhanced ultrasound imaging of prostate cancer
Authors:
Rogier R. Wildeboer, Arnoud W. Postema, Libertario Demi, Maarten P. J. Kuenen, Hessel Wijkstra, Massimo Mischi
Published in:
European Radiology
|
Issue 8/2017
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Abstract
Objectives
The aim of this study is to improve the accuracy of dynamic contrast-enhanced ultrasound (DCE-US) for prostate cancer (PCa) localization by means of a multiparametric approach.
Materials and Methods
Thirteen different parameters related to either perfusion or dispersion were extracted pixel-by-pixel from 45 DCE-US recordings in 19 patients referred for radical prostatectomy. Multiparametric maps were retrospectively produced using a Gaussian mixture model algorithm. These were subsequently evaluated on their pixel-wise performance in classifying 43 benign and 42 malignant histopathologically confirmed regions of interest, using a prostate-based leave-one-out procedure.
Results
The combination of the spatiotemporal correlation (r), mean transit time (μ), curve skewness (κ), and peak time (PT) yielded an accuracy of 81% ± 11%, which was higher than the best performing single parameters: r (73%), μ (72%), and wash-in time (72%). The negative predictive value increased to 83% ± 16% from 70%, 69% and 67%, respectively. Pixel inclusion based on the confidence level boosted these measures to 90% with half of the pixels excluded, but without disregarding any prostate or region.
Conclusions
Our results suggest multiparametric DCE-US analysis might be a useful diagnostic tool for PCa, possibly supporting future targeting of biopsies or therapy. Application in other types of cancer can also be foreseen.
Key points
• DCE-US can be used to extract both perfusion and dispersion-related parameters.
• Multiparametric DCE-US performs better in detecting PCa than single-parametric DCE-US.
• Multiparametric DCE-US might become a useful tool for PCa localization.