Multiparametric dynamic contrast-enhanced ultrasound imaging of prostate cancer

\u3cp\u3eObjectives: 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.\u3c/p\u3

Contrast-Enhanced Ultrasound (CEUS) and elastographic imaging

Ultrasound imaging has found applications in several areas of interventional urology prior to treatment (diagnosis), after treatment (follow-up), and also during treatment (monitoring/guidance). Contrast-enhanced ultrasound (CEUS) imaging and elastographic imaging are the two widely used imaging techniques. CEUS modalities, such as harmonic and multi-pulse imaging, are increasingly applied in interventional urology, because the contrast agents give a clear enhancement of blood vessels in the tissue. Among others, CEUS is used to study the microvasculature of the prostate and kidney for cancer detection to more accurately identify malignant lesions. Elastography is a technique that has been coined in 1991 for the quantification of elastic properties of biological tissue. Various elastographic techniques have been developed, such as Acoustic radiation force imaging (ARFI) and shear wave elastography. These techniques have found various applications in interventional urology such as monitoring the degree of fibrosis in renal allografts after transplantation and the identification of renal masses and prostatic lesions.\u3cbr/\u3eThese enhanced ultrasound modalities represent novel techniques in the evaluation of the prostate and kidney in urology. There are multiple putative benefits of enhanced ultrasound modalities, including improved targeting for prostate biopsy; improved characterization of suspicious renal masses, especially when contrast-enhanced axial imaging (CT or MRI) are contraindicated; and lower cost than CT or MRI. Further investigations and refinements are necessary to define the role of these techniques in current urologic practice

Ultrasound-contrast-agent dispersion and velocity imaging for prostate cancer localization

Prostate cancer (PCa) is the second-leading cause of cancer death in men; however, reliable tools for detection and localization are still lacking. Dynamic Contrast Enhanced UltraSound (DCE-US) is a diagnostic tool that is suitable for analysis of vascularization, by imaging an intravenously injected microbubble bolus. The localization of angiogenic vascularization associated with the development of tumors is of particular interest. Recently, methods for the analysis of the bolus convective dispersion process have shown promise to localize angiogenesis. However, independent estimation of dispersion was not possible due to the ambiguity between convection and dispersion. Therefore, in this study we propose a new method that considers the vascular network as a dynamic linear system, whose impulse response can be locally identified. To this end, model-based parameter estimation is employed, that permits extraction of the apparent dispersion coefficient (D), velocity (v), and Péclet number (Pe) of the system. Clinical evaluation using data recorded from 25 patients shows that the proposed method can be applied effectively to DCE-US, and is able to locally characterize the hemodynamics, yielding promising results (receiver-operating-characteristic curve area of 0.84) for prostate cancer localization

Entropy of ultrasound-contrast-agent velocity fields for angiogenesis imaging in prostate cancer

Prostate cancer care can benefit from accurate and cost-efficient imaging modalities that are able to reveal prognostic indicators for cancer. Angiogenesis is known to play a central role in the growth of tumors towards a metastatic or a lethal phenotype.With the aim of localizing angiogenic activity in a noninvasive manner, Dynamic Contrast Enhanced Ultrasound (DCEUS) has been widely used. Usually, the passage of ultrasound contrast agents thought the organ of interest is analyzed for the assessment of tissue perfusion. However, the heterogeneous nature of blood flow in angiogenic vasculature hampers the diagnostic effectiveness of perfusion parameters. In this regard, quantification of the heterogeneity of flow may provide a relevant additional feature for localizing angiogenesis. Statistics based on flow magnitude as well as its orientation can be exploited for this purpose. In this paper, we estimate the microbubble velocity fields from a standard bolus injection and provide a first statistical characterization by performing a spatial entropy analysis. By testing the method on 24 patients with biopsyproven prostate cancer, we show that the proposed method can be applied effectively to clinically acquired DCE-US data. The method permits estimation of the in-plane flow vector fields and their local intricacy, and yields promising results (receiveroperating- characteristic curve area of 0.85) for the detection of prostate cancer

Dynamic contrast-enhanced ultrasound parametric imaging for the detection of prostate cancer

\u3cp\u3eOBJECTIVE: To investigate the value of dynamic contrast-enhanced (DCE)-ultrasonography (US) and software-generated parametric maps in predicting biopsy outcome and their potential to reduce the amount of negative biopsy cores.\u3c/p\u3e\u3cp\u3eMATERIALS AND METHODS: For 651 prostate biopsy locations (82 consecutive patients) we correlated the interpretation of DCE-US recordings with and without parametric maps with biopsy results. The parametric maps were generated by software which extracts perfusion parameters that differentiate benign from malignant tissue from DCE-US recordings. We performed a stringent analysis (all tumours) and a clinical analysis (clinically significant tumours). We calculated the potential reduction in biopsies (benign on imaging) and the resultant missed positive biopsies (false-negatives). Additionally, we evaluated the performance in terms of sensitivity, specificity negative predictive value (NPV) and positive predictive value (PPV) on a per-prostate level.\u3c/p\u3e\u3cp\u3eRESULTS: Based on DCE-US, 470/651 (72.2%) of biopsy locations appeared benign, resulting in 40 false-negatives (8.5%), considering clinically significant tumours only. Including parametric maps, 411/651 (63.1%) of the biopsy locations appeared benign, resulting in 23 false-negatives (5.6%). In the per-prostate clinical analysis, DCE-US classified 38/82 prostates as benign, missing eight diagnoses. Including parametric maps, 31/82 prostates appeared benign, missing three diagnoses. Sensitivity, specificity, PPV and NPV were 73, 58, 50 and 79%, respectively, for DCE-US alone and 91, 56, 57 and 90%, respectively, with parametric maps.\u3c/p\u3e\u3cp\u3eCONCLUSION: The interpretation of DCE-US with parametric maps allows good prediction of biopsy outcome. A two-thirds reduction in biopsy cores seems feasible with only a modest decrease in cancer diagnosis.\u3c/p\u3

Zonal segmentation in transrectal ultrasound images of the prostate through deep learning

Segmentation of both prostatic and zonal boundaries in transrectal ultrasound images is of great value in current clinical practice and for advancing techniques in computer-assisted diagnosis and inter-modality fusion. In this work, we propose a deep-learning approach to automatically segment the prostate and its main zones. In comparison with conventional methods, this method shows increased accuracy and Dice coefficients for full-prostate delineation. Moreover, the mean deviation between the annotated and predicted contours decreased substantially. Albeit the method still requires validation for different scanners and configurations, its real-time inference rate highlights the potential of this technique to be applied in clinical practice