Novel contrast-enhanced ultrasound imaging in prostate cancer

The purposes of this paper were to present the current status of contrast-enhanced transrectal ultrasound imaging and to discuss the latest achievements and techniques now under preclinical testing. Although grayscale transrectal ultrasound is the standard method for prostate imaging, it lacks accuracy in the detection and localization of prostate cancer. With the introduction of contrast-enhanced ultrasound (CEUS), perfusion imaging of the microvascularization became available. By this, cancer-induced neovascularisation can be visualized with the potential to improve ultrasound imaging for prostate cancer detection and localization significantly. For example, several studies have shown that CEUS-guided biopsies have the same or higher PCa detection rate compared with systematic biopsies with less biopsies needed. This paper describes the current status of CEUS and discusses novel quantification techniques that can improve the accuracy even further. Furthermore, quantification might decrease the user-dependency, opening the door to use in the routine clinical environment. A new generation of targeted microbubbles is now under pre-clinical testing and showed avidly binding to VEGFR-2, a receptor up-regulated in prostate cancer due to angiogenesis. The first publications regarding a targeted microbubble ready for human use will be discussed. Ultrasound-assisted drug delivery gives rise to a whole new set of therapeutic options, also for prostate cancer. A major breakthrough in the future can be expected from the clinical use of targeted microbubbles for drug delivery for prostate cancer diagnosis as well as treatmen

What scans we will read: imaging instrumentation trends in clinical oncology

Oncological diseases account for a significant portion of the burden on public healthcare systems with associated costs driven primarily by complex and long-lasting therapies. Through the visualization of patient-specific morphology and functional-molecular pathways, cancerous tissue can be detected and characterized non- invasively, so as to provide referring oncologists with essential information to support therapy management decisions. Following the onset of stand-alone anatomical and functional imaging, we witness a push towards integrating molecular image information through various methods, including anato-metabolic imaging (e.g., PET/ CT), advanced MRI, optical or ultrasound imaging. This perspective paper highlights a number of key technological and methodological advances in imaging instrumentation related to anatomical, functional, molecular medicine and hybrid imaging, that is understood as the hardware-based combination of complementary anatomical and molecular imaging. These include novel detector technologies for ionizing radiation used in CT and nuclear medicine imaging, and novel system developments in MRI and optical as well as opto-acoustic imaging. We will also highlight new data processing methods for improved non-invasive tissue characterization. Following a general introduction to the role of imaging in oncology patient management we introduce imaging methods with well-defined clinical applications and potential for clinical translation. For each modality, we report first on the status quo and point to perceived technological and methodological advances in a subsequent status go section. Considering the breadth and dynamics of these developments, this perspective ends with a critical reflection on where the authors, with the majority of them being imaging experts with a background in physics and engineering, believe imaging methods will be in a few years from now. Overall, methodological and technological medical imaging advances are geared towards increased image contrast, the derivation of reproducible quantitative parameters, an increase in volume sensitivity and a reduction in overall examination time. To ensure full translation to the clinic, this progress in technologies and instrumentation is complemented by progress in relevant acquisition and image-processing protocols and improved data analysis. To this end, we should accept diagnostic images as “data”, and – through the wider adoption of advanced analysis, including machine learning approaches and a “big data” concept – move to the next stage of non-invasive tumor phenotyping. The scans we will be reading in 10 years from now will likely be composed of highly diverse multi- dimensional data from multiple sources, which mandate the use of advanced and interactive visualization and analysis platforms powered by Artificial Intelligence (AI) for real-time data handling by cross-specialty clinical experts with a domain knowledge that will need to go beyond that of plain imaging