Rr Rogier Wildeboer

Multiparametric dynamic contrast-enhanced ultrasound imaging of prostate cancer

ObjectivesThe 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 MethodsThirteen 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.ResultsThe 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.ConclusionsOur 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.

Viscoelasticity Mapping by Identification of Local Shear Wave Dynamics

Estimation of soft tissue elasticity is of interest in several clinical applications. For instance, tumors and fibrotic lesions are notoriously stiff compared with benign tissue. A fully quantitative measure of lesion stiffness can be obtained by shear wave (SW) elastography. This method uses an acoustic radiation force to produce laterally propagating SWs that can be tracked to obtain the velocity, which in turn is related to Young’s modulus. However, not only elasticity, but also viscosity plays an important role in the propagation process of SWs. In fact, viscosity itself is a parameter of diagnostic value for the detection and characterization of malignant lesions. In this paper, we describe a new method that enables imaging viscosity from SW elastography by local model-based system identification. By testing the method on simulated data sets and performing in vitro experiments, we show that the ability of the proposed technique to generate parametric maps of the viscoelastic material properties from SW measurements, opening up new possibilities for noninvasive tissue characterization.

A multiparametric approach for dynamic contrast-enhanced ultrasound imaging of prostate cancer

Prostate cancer (PCa) is the most commonly diagnosed type of non-cutaneous cancer in American men [1]. A sufficiently reliable PCa imaging method is currently not available, leaving systematic biopsy as the guideline-recommended technique for PCa diagnosis [2]. Dynamic Contrast-Enhanced Ultrasound (DCE-US) is currently being studied for the characterization of prostatic tissue, since it might prove able to reveal the vascular changes associated with (PCa) angiogenesis [3]. The effect of PCa on blood flow, however, is ambiguous [4]; assessment of the contrast agent kinetics that is more objective than the visual inspection of the contrast video is therefore required.

Shear wave viscoelasticity imaging using local system identification

Tissue elasticity is an important parameter which relates to the pathological state of soft tissue. Fibrotic lesions or malignant tumors are known to be notoriously stiff compared to benign tissue. Shear wave elastography can provide a fully quantitative measure of lesion stiffness by estimating the speed at which acoustically induced shear waves propagate through the material. This speed is in turn related to the Youngs modulus. In soft tissue, elasticity is generally accompanied by viscosity, leading to dispersion of the shear wave. For the detection and characterization of malignant lesions, viscosity has in fact diagnostic value. Here, we describe a new method that enables imaging not only elasticity but also viscosity from shear wave elastography by local model-based system identification. We show that the proposed method can be applied effectively to standard shear wave acquisitions, and is able to generate high-resolution parametric maps of the viscoelastic material properties in an in-vitro setting.

Shear-wave imaging of viscoelasticity using local impulse response identification

Imaging technologies that allow assessment of the elastic properties of soft tissue provide clinicians with an important asset for several diagnostic applications. A quantitative measure of stiffness can be obtained by shear-wave (SW) elasticity imaging, a method that uses acoustic radiation force to produce laterally-propagating shear waves that can be tracked to obtain the velocity, which in turn is related to the shear modulus. If one considers the medium to be purely elastic, its local shear modulus can be estimated by determining the local SW velocity. However, this assumption does not hold for many tissue types, whenever the shear viscosity plays an important role. In fact, there is increasing evidence that viscosity itself could be an important marker for malignancy [1]. In this work, we therefore aim at providing a joint local estimate of tissue elasticity and viscosity based on SW elastography.

Multiparametric dynamic contrast-enhanced ultrasound classification of prostate cancer

Although prostate cancer (PCa) is the most common non-cutaneous form of cancer among Western men, available diagnostic imaging methods are not yet sufficiently reliable to avoid systematic biopsy. In this work, we aim at improving the accuracy of transrectal dynamic contrast-enhanced ultrasonography (DCE-US) for PCa localization by combining local perfusion and dispersion parameters. To this end, ten of these parameters were extracted pixel-by-pixel from 45 DCE-US recordings distributed over 19 patients that were scheduled for radical prostatectomy. Based on 43 benign and 42 malignant histologically-confirmed regions of interest, we produced multiparametric maps using a Gaussian Mixture Model (GMM) algorithm. All possible combinations of one to four parameters were evaluated to select the most suitable subset of parameters. We also tested the GMM algorithms ability to determine the classification confidence for each pixel and the impact of excluding low-confidence pixels from the images. An accuracy and negative predictive value of 81% and 83%, respectively, are obtained, which improved after pixel exclusion. Even though extended validation on a larger patient group is recommended, multiparametric DCE-US shows high potential in localizing PCa and might become an important tool for guiding targeted biopsy or planning of focal treatment.