\n @ARTICLE{Wildeboer2016b,
\n author = {Wildeboer, Rogier R. AND Postema, Arnoud W. AND Demi, Libertario AND Kuenen, Maarten P. J. AND Wijkstra, Hessel AND Mischi, Massimo},
\n title = {Multiparametric dynamic contrast-enhanced ultrasound imaging of prostate cancer},
\n 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.},
\n keywords = {Prostate cancer, Ultrasound, Contrast agents, Classification, Multiparametric imaging},
\n pages = {1-9},
\n bookTitle = {European Radiology},
\n year = {2016},
month = {Dec.}