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Nonalcoholic fatty liver disease is a major global health concern with increasing prevalence, associated with obesity and metabolic syndrome. Recently, quantitative ultrasound-based imaging techniques have dramatically improved the ability of ultrasound to detect and quantify hepatic steatosis. These newer ultrasound techniques possess many inherent advantages similar to conventional ultrasound such as universal availability, real-time capability, and relatively low cost along with quantitative rather than a qualitative assessment of liver fat. In addition, quantitative ultrasound-based imaging techniques are less operator dependent than traditional ultrasound. Here we review several different emerging quantitative ultrasound-based approaches used for detection and quantification of hepatic steatosis in patients at risk for nonalcoholic fatty liver disease. We also briefly summarize other clinically available imaging modalities for evaluating hepatic steatosis such as MRI, CT, and serum analysis.

Ultrasound-stimulated microbubbles (USMB) cause localized vascular effects and sensitize tumors to radiation therapy (XRT). We investigated acoustic parameter optimization for combining USMB and XRT. We treated breast cancer xenograft tumors with 500 kHz pulsed ultrasound at varying pressures (570 or 740 kPa), durations (1 to 10 minutes), and microbubble concentrations (0.01 to 1% (v/v)). Radiation therapy (2 Gy) was administered immediately or after a 6-hour delay. Histological staining of tumors 24 hours after treatment detected changes in cell morphology, cell death, and microvascular density. Significant cell death resulted at 570 kPa after a 1-minute exposure with 1% (v/v) microbubbles with or without XRT. However, significant microvascular disruption required higher ultrasound pressure and exposure duration greater than 5 minutes. Introducing a 6-hour delay between treatments (USMB and XRT) showed a similar tumor effect with no further improvement in response as compared to when XRT was delivered immediately after USMB.

Due to spatial tumor heterogeneity and consecutive sampling errors, it is critically important to assess treatment response following antiangiogenic therapy in three dimensions as two-dimensional assessment has been shown to substantially over- and underestimate treatment response. In this study, we evaluated whether three-dimensional (3D) dynamic contrast-enhanced ultrasound (DCE-US) imaging allows assessing early changes in tumor perfusion following antiangiogenic treatment (bevacizumab administered at a dose of 10 mg/kg b.w.), and whether these changes could predict treatment response in colon cancer tumors that either are responsive (LS174T tumors) or none responsive (CT26) to the proposed treatment. Our results showed that the perfusion parameters of 3D DCE-US including peak enhancement (PE) and area under curve (AUC) significantly decreased by up to 69 and 77%, respectively, in LS174T tumors within 1 day after antiangiogenic treatment ( P  = 0.005), but not in CT26 tumors ( P  > 0.05). Similarly, the percentage area of neovasculature significantly decreased in treated versus control LS174T tumors ( P  < 0.001), but not in treated versus control CT26 tumors (P  = 0.796). Early decrease in both PE and AUC by 45–50% was predictive of treatment response in 100% (95% CI 69.2, 100%) of responding tumors, and in 100% (95% CI 88.4, 100%) and 86.7% (95% CI 69.3, 96.2%), respectively, of nonresponding tumors. In conclusion, 3D DCE-US provides clinically relevant information on the variability of tumor response to antiangiogenic therapy and may be further developed as biomarker for predicting treatment outcomes.

Background: Liver ultrasound segmentation is challenging due to low image quality and variability. While deep learning (DL) models have been widely applied for medical segmentation, generic pre-configured models may not meet the specific requirements for targeted areas in liver ultrasound. Quantitative ultrasound (QUS) is emerging as a promising tool for liver fat measurement; however, accurately segmenting regions of interest within liver ultrasound images remains a challenge. Methods: We introduce a generalizable framework using an adaptive evolutionary genetic algorithm to optimize deep learning models, specifically U-Net, for focused liver segmentation. The algorithm simultaneously adjusts the depth (number of layers) and width (neurons per layer) of the network, dropout, and skip connections. Various architecture configurations are evaluated based on segmentation performance to find the optimal model for liver ultrasound images. Results: The model with a depth of 4 and filter sizes of [16, 64, 128, 256] achieved the highest mean adjusted Dice score of 0.921, outperforming the other configurations, using three-fold cross-validation with early stoppage. Conclusions: Adaptive evolutionary optimization enhances the deep learning architecture for liver ultrasound segmentation. Future work may extend this optimization to other imaging modalities and deep learning architectures.

We report a low-cost protocol and platform for whole-abdomen 3D dynamic contrast-enhanced ultrasound (DCE-US) imaging in mice using a clinical matrix-array transducer. Background/Objectives: This platform addresses common limitations of preclinical ultrasound systems. In particular, these systems often lack real-time volumetric and molecular imaging capabilities. Methods: Using a modified silicone cup and water bath configuration, mice with dual subcutaneous tumors were imaged in vivo on a clinical EPIQ 7 system equipped with an X6-1 transducer. Results: Intravenous administration of targeted microbubbles enabled high-resolution, contrast-mode 3D imaging at multiple time points. Volumetric reconstructions captured both tumors and surrounding anatomy in a single scan, while time–intensity curves and Differential Targeted Enhancement (DTE) analysis revealed greater microbubble uptake in irradiated tumors, consistent with elevated P-selectin expression. Conclusions: This standardized imaging platform enables whole-abdomen molecular DCE-US in preclinical studies, facilitating intra-animal comparisons of vascular and molecular features across lesions or organs.

Quantitative three-dimensional molecular ultrasound is a promising technology for longitudinal imaging applications such as therapy monitoring; the risk profile is favorable compared to positron emission tomography and computed tomography. However, clinical translation of quantitative methods for this technology are limited in that they assume that tumor tissues are homogeneous, and often depend on contrast-destruction events that can produce unintended bioeffects. Here, we develop quantitative features (henceforth image features) that capture tumor spatial information, and that are extracted without contrast destruction. We compare these techniques with the contrast-destruction derived differential targeted enhancement parameter (dTE) in predicting response to therapy. We found thirty-three reproducible image features that predict response to antiangiogenic therapy, without the need for a contrast agent disruption pulse. Multiparametric analysis shows that several of these image features can differentiate treated versus control animals with comparable performance to post-destruction measurements, suggesting that these can potentially replace parameters such as the dTE. The highest performing pre-destruction image features showed strong linear correlations with conventional dTE parameters with less overall variance. Thus, our study suggests that image features obtained during the wash in of the molecular agent, pre-destruction, may replace conventional post-destruction image features or the dTE parameter.

There is a need for noninvasive repeatable biomarkers to detect early cancer treatment response and spare non-responders unnecessary morbidities and costs. Here, we introduce three-dimensional (3D) dynamic contrast enhanced ultrasound (DCE-US) perfusion map characterization as inexpensive, bedside and longitudinal indicator of tumor perfusion for prediction of vascular changes and therapy response. More specifically, we developed computational tools to generate perfusion maps in 3D of tumor blood flow, and identified repeatable quantitative features to use in machine-learning models to capture subtle multi-parametric perfusion properties, including heterogeneity. Models were developed and trained in mice data and tested in a separate mouse cohort, as well as early validation clinical data consisting of patients receiving therapy for liver metastases. Models had excellent (ROC-AUC > 0.9) prediction of response in pre-clinical data, as well as proof-of-concept clinical data. Significant correlations with histological assessments of tumor vasculature were noted (Spearman R > 0.70) in pre-clinical data. Our approach can identify responders based on early perfusion changes, using perfusion properties correlated to gold-standard vascular properties.

Ultrasound-stimulated microbubbles have been demonstrated to mechanically perturb cell membranes, resulting in the activation of biological signaling pathways that significantly enhance the effects of radiation. The underlying mechanism involves augmented ceramide production following both microbubble stimulation and irradiation, leading to rapid and extensive endothelial apoptosis and tumor cell death as a result of vascular collapse. Endothelial cells are particularly sensitive to ceramide-induced cell death due to an enriched presence of sphingomyelinase in their membranes. In tumors, this consequent rapid vascular shutdown translates to an overall increase in tumor responses to radiation treatments. This review summarizes the groundwork behind endothelial-based radiation enhancement with ultrasound-stimulated microbubbles, and presents ongoing research on the use of microbubbles as therapeutic agents in cancer therapy.

PurposeTo evaluate quantitative and semi-quantitative ultrasound molecular imaging (USMI) for antiangiogenic therapy monitoring in human colon cancer xenografts in mice.ProceduresColon cancer was established in 17 mice by injection of LS174T (Nr = 9) or CT26 (Nn = 8) cancer cells to simulate clinical responders and non-responders, respectively. Antiangiogenic treatment (bevacizumab; Nrt = Nnt = 5) or control treatment (saline; Nrc = 4, Nnc = 3) was administered at days 0, 3, and 7. Three-dimensional USMI was performed by injection at days 0, 1, 3, 7, and 10 of microbubbles targeted to the vascular endothelial growth factor receptor 2 (VEGFR2). Microbubble binding rate (kb), estimated by first-pass binding model fitting, and semi-quantitative parameters late enhancement (LE) and differential targeted enhancement (dTE) were compared at each day to evaluate their ability to assess and predict the response to therapy. Correlation analysis with the ex-vivo immunohistological quantification of VEGFR2 expression and the percentage blood vessel area was also performed.ResultsSignificant changes in the USMI parameters during treatment were observed only in the responders treated with bevacizumab (p-value < 0.05). Prediction of the response to therapy as early as 1 day after treatment was achieved by the quantitative parameter kb (p-value < 0.01), earlier than possible by tumor volume quantification. USMI parameters could significantly distinguish between clinical responders and non-responders (p-value << 0.01) and correlated well with the ex-vivo quantification of VEGFR2 expression and the percentage blood vessels area (p-value << 0.01).ConclusionUSMI (semi)quantitative parameters provide earlier assessment of the response to therapy compared to tumor volume, permit early prediction of non-responders, and correlate well with ex-vivo angiogenesis biomarkers.

Tumour vasculature acts as an essential lifeline for tumour progression and facilitates metastatic spread. Novel vascular targeting strategies aiming to sustain vascular shutdown could potentially induce substantial damage, resulting in a significant tumour growth delay. We investigated the combination of two novel complementary vascular targeting agents with radiation therapy in a strategy aiming to sustain vascular disruption. Experiments were carried out with delta-like ligand 4 (Dll4) blockade (angiogenesis deregulator) treatment administered in combination with a radiation-based vascular destruction treatment in a highly aggressive well-perfused colon cancer tumour line implanted in female athymic nude mice. Tumours were treated with permutations of radiation, ultrasound-stimulated microbubbles (USMB) and Dll4 monoclonal antibody (mAb). Tumour vascular response was assessed with three-dimensional power Doppler ultrasound to measure active flow and immunohistochemistry. Tumour response was assessed with histochemical assays and longitudinal measurements of tumour volume. Our results suggest a significant tumour response in animals treated with USMB combined with radiation, and Dll4 mAb, leading to a synergistic tumour growth delay of up to 24 days. This is likely linked to rapid cell death within the tumour and a sustained tumour vascular shutdown. We conclude that the triple combination treatments cause a vascular shutdown followed by a sustained inhibition of angiogenesis and tumour cell death, leading to a rapid tumour vascular-based 'collapse' and a significant tumour growth delay.