Effects of contrast-enhanced ultrasound treatment on neoadjuvant chemotherapy in breast cancer.

Purpose: Preclinical and clinical data indicate that contrast-enhanced ultrasound can enhance tumor perfusion and vessel permeability, thus, improving chemotherapy accumulation and therapeutic outcome. Therefore, we investigated the effects of high mechanical index (MI) contrast-enhanced Doppler ultrasound (CDUS) on tumor perfusion in breast cancer. Methods: In this prospective study, breast cancer patients were randomly assigned to receive either 18 minutes of high MI CDUS during chemotherapy infusion (n = 6) or chemotherapy alone (n = 5). Tumor perfusion was measured before and after at least six chemotherapy cycles using motion-model ultrasound localization microscopy. Additionally, acute effects of CDUS on vessel perfusion and chemotherapy distribution were evaluated in mice bearing triple-negative breast cancer (TNBC). Results: Morphological and functional vascular characteristics of breast cancer in patients were not significantly influenced by high MI CDUS. However, complete clinical tumor response after neoadjuvant chemotherapy was lower in high MI CDUS-treated (1/6) compared to untreated patients (4/5) and size reduction of high MI CDUS treated tumors tended to be delayed at early chemotherapy cycles. In mice with TNBC high MI CDUS decreased the perfused tumor vessel fraction (p < 0.01) without affecting carboplatin accumulation or distribution. Higher vascular immaturity and lower stromal stabilization may explain the stronger vascular response in murine than human tumors. Conclusion: High MI CDUS had no detectable effect on breast cancer vascularization in patients. In mice, the same high MI CDUS setting did not affect chemotherapy accumulation although strong effects on the tumor vasculature were detected histologically. Thus, sonopermeabilization in human breast cancers might not be effective using high MI CDUS protocols and future applications may rather focus on low MI approaches triggering microbubble oscillations instead of destruction. Furthermore, our results show that there are profound differences in the response of mouse and human tumor vasculature to high MI CDUS, which need to be further explored and considered in clinical translation.

Clinical Pilot Application of Super-Resolution US Imaging in Breast Cancer.

Recently, we proved in the first measurements of breast carcinomas the feasibility of super-resolution ultrasound (US) imaging by motion-model ultrasound localization microscopy in a clinical setup. Nevertheless, pronounced in-plane and out-of-plane motions, a nonoptimized microbubble injection scheme, the lower frame rate and the larger slice thickness made the processing more complex than in preclinical investigations. Here, we compare the results of state-of-the-art contrast-enhanced to super-resolution US imaging and systematically analyze the measurements to get indications for the improvement of image acquisition and processing in the future clinical studies. In this regard, the application of a saturation model to the reconstructed vessels is shown to be a valuable tool not only to estimate the measurement times necessary to adequately reconstruct the microvasculature but also for the validation of the measurements. The parameters from this model can also serve to optimize contrast agent concentration and injection protocols. Finally, for the measurements of well-perfused tumors, we observed between 28% and 50% filling for 90-s examination times.

Radiomic analysis of contrast-enhanced ultrasound data.

Radiomics describes the use radiological data in a quantitative manner to establish correlations in between imaging biomarkers and clinical outcomes to improve disease diagnosis, treatment monitoring and prediction of therapy responses. In this study, we evaluated whether a radiomic analysis on contrast-enhanced ultrasound (CEUS) data allows to automatically differentiate three xenograft mouse tumour models. Next to conventional imaging biomarker classes, i.e. intensity-based, textural, and wavelet-based features, we included biomarkers describing morphological and functional characteristics of the tumour vasculature. In total, 235 imaging biomarkers were extracted and evaluated. Dedicated feature selection allowed us to identify user-independent and stable imaging biomarkers for each imaging biomarker class. The selected radiomic signature, composed of median image intensity, energy of grey-level co-occurrence matrix, vessel network length, and run length nonuniformity of the grey-level run length matrix from the diagonal details, was used to train a linear support vector machine (SVM) to classify tumour phenotypes. The model was trained by using a four-fold cross-validation scheme and achieved 82.1% (95% CI [0.64 0.92]) correct classifications. In conclusion, our results show that a radiomic analysis can be successfully performed on CEUS data and may help to render ultrasound-based tumour imaging more accurate, reproducible and reliable.

Semi-Automated Segmentation of the Tumor Vasculature in Contrast-Enhanced Ultrasound Data.

The vascular architecture in tumors contains relevant information for tumor classification and evaluation of therapy responses. To develop a reliable and user-independent analysis tool, a foreground detection algorithm was combined with a maximum-intensity projection to obtain a high signal-to-noise image from contrast-enhanced B-mode data sets, enabling vessel segmentation by thresholding. Parameters describing the density of the vascular network, the number of vessels and the number of branches were extracted. The highly angiogenic A431 tumors had a relative blood volume of 49%, a mean pixel distance to the next vessel of 1.8 ± 0.3 px, 51 ± 29 individual vessels and 478 ± 184 branching points, whereas the more mature and heterogeneous vascularized human epithelial ovarian carcinoma (MLS) and A549 tumors had values of 30%, 3.7 ± 2.7 px, 65 ± 12 and 220 ± 159, and 13%, 7.4 ± 2 px, 31 ± 9 and 59 ± 40, respectively. Thus, our semi-automated analysis method enables the extraction of quantitative vascular features that may help to simplify and standardize tumor characterization.

Motion model ultrasound localization microscopy for preclinical and clinical multiparametric tumor characterization.

Super-resolution imaging methods promote tissue characterization beyond the spatial resolution limits of the devices and bridge the gap between histopathological analysis and non-invasive imaging. Here, we introduce motion model ultrasound localization microscopy (mULM) as an easily applicable and robust new tool to morphologically and functionally characterize fine vascular networks in tumors at super-resolution. In tumor-bearing mice and for the first time in patients, we demonstrate that within less than 1 min scan time mULM can be realized using conventional preclinical and clinical ultrasound devices. In this context, next to highly detailed images of tumor microvascularization and the reliable quantification of relative blood volume and perfusion, mULM provides multiple new functional and morphological parameters that discriminate tumors with different vascular phenotypes. Furthermore, our initial patient data indicate that mULM can be applied in a clinical ultrasound setting opening avenues for the multiparametric characterization of tumors and the assessment of therapy response.

Automated Generation of Reliable Blood Velocity Parameter Maps from Contrast-Enhanced Ultrasound Data.

Objectives: The purpose of this study was the automated generation and validation of parametric blood flow velocity maps, based on contrast-enhanced ultrasound (CEUS) scans. Materials and Methods: Ethical approval for animal experiments was obtained. CEUS destruction-replenishment sequences were recorded in phantoms and three different tumor xenograft mouse models. Systematic pixel binning and intensity averaging was performed to generate parameter maps of blood flow velocities with different pixel resolution. The 95% confidence interval of the mean velocity, calculated on the basis of the whole tumor segmentation, served as ground truth for the different parameter maps. Results: In flow phantoms the measured mean velocity values were only weakly influenced by the pixel resolution and correlated with real velocities (r2 ≥ 0.94, p < 0.01). In tumor xenografts, however, calculated mean velocities varied significantly (p < 0.0001), depending on the parameter maps' resolution. Pixel binning was required for all in vivo measurements to obtain reliable parameter maps and its degree depended on the tumor model. Conclusion: Systematic pixel binning allows the automated identification of optimal pixel resolutions for parametric maps, supporting textural analysis of CEUS data. This approach is independent from the ultrasound setup and can be implemented in the software of other (clinical) ultrasound devices.

Status and trends in the development of clinical diagnostic agents.

Contrast agents (CA) are routinely used in clinical practice to improve the diagnosis of diseases and to monitor therapy response. The majority of CA comprises small molecules accumulating at pathological sites due to vascular abnormalities, such as changes in perfusion and permeability. For many diseases, high diagnostic accuracy can be achieved with contrast-enhanced imaging. This means that new CA will only succeed in translation if they either show superior performance with respect to diagnostic accuracy, safety and cost, support a new imaging modality, or are directly linked to the refinement of therapy, e.g., as a companion diagnostic. Unfortunately, these basic demands are often not carefully considered by the scientific community, leading to concepts with low chances of clinical translation. Thus, it is not surprising that, despite steadily increasing numbers of publications, there is quite the opposite trend when it comes to the clinical approval of new diagnostics. As a matter of fact, except for PET tracers, in the last decade, only a handful of CA received FDA or EMA approval. Furthermore, several approved products were discontinued by the manufacturers because of low market potential, a competitive own product, suboptimal clinical performance, or safety concerns. This review article discusses the current status of approved diagnostic probes for clinical imaging modalities, with a focus on new trends in CA development. In this context, molecularly targeted diagnostics or probes for emerging fields, such as image-guided surgery, nanomedicine, or theranostics, will be introduced and discussed with regard to their clinical translation. WIREs Nanomed Nanobiotechnol 2017, 9:e1441. doi: 10.1002/wnan.1441 For further resources related to this article, please visit the WIREs website.