Wildeboer Rogier R, Postema Arnoud W, Demi Libertario, Kuenen Maarten P J, Wijkstra Hessel, Mischi Massimo
Laboratory of Biomedical Diagnostics, Department of Electrical Engineering, Eindhoven University of Technology, PO-Box 513, 5600 MB, Eindhoven, The Netherlands.
Department of Urology, Academic Medical Center University Hospital, Meibergdreef 9, 1105 AZ, Amsterdam, The Netherlands.
Eur Radiol. 2017 Aug;27(8):3226-3234. doi: 10.1007/s00330-016-4693-8. Epub 2016 Dec 21.
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.
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.
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.
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.
• 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.
本研究旨在通过多参数方法提高动态对比增强超声（DCE-US）对前列腺癌（PCa）定位的准确性。
从19例接受根治性前列腺切除术患者的45份DCE-US记录中逐像素提取13个与灌注或弥散相关的不同参数。使用高斯混合模型算法回顾性生成多参数图。随后，采用基于前列腺的留一法程序，对这些图在对43个良性和42个经组织病理学证实的恶性感兴趣区域进行逐像素分类时的性能进行评估。
时空相关性（r）、平均通过时间（μ）、曲线偏度（κ）和峰值时间（PT）的组合产生了81%±11%的准确率，高于表现最佳的单个参数：r（73%）、μ（72%）和流入时间（72%）。阴性预测值分别从70%、69%和67%提高到83%±16%。基于置信水平的像素纳入将这些指标提高到90%，同时排除了一半的像素，但没有忽略任何前列腺或区域。
我们的结果表明，多参数DCE-US分析可能是一种用于PCa的有用诊断工具，可能支持未来活检或治疗的靶向定位。也可以预见其在其他类型癌症中的应用。
• DCE-US可用于提取与灌注和弥散相关的参数。• 多参数DCE-US在检测PCa方面比单参数DCE-US表现更好。• 多参数DCE-US可能成为PCa定位的有用工具。
