Early prediction of tumor response to bevacizumab treatment in murine colon cancer models using three-dimensional dynamic contrast-enhanced ultrasound imaging.

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.

Biomechanical effects of microbubbles: from radiosensitization to cell death.

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.

Sunitinib effects on the radiation response of endothelial and breast tumor cells.

BACKGROUND: Endothelial cells are suggested regulators of tumor response to radiation. Anti-vascular targeting agents can enhance tumor response by targeting endothelial cells. Here, we have conducted experiments in vitro to discern the effects of radiation combined with the anti-angiogenic Sunitinib on endothelial (HUVEC) and tumor (MDA-MB-231) cells, and further compared findings to results obtained in vivo. METHODS: In vitro and in vivo treatments consisted of single dose radiation therapy of 2, 4, 8 or 16 Gy administered alone or in combination with bFGF or Sunitinib. In vitro, in situ end labeling (ISEL) was used to assess 24-hour apoptotic cell death, and clonogenic assays were used to assess long-term response. In vivo MDA-MB-231 tumors were grown in CB-17 SCID mice. The vascular marker CD31 was used to assess 24-hour acute response while tumor clonogenic assays were used to assess long-term tumor cell viability following treatments. RESULTS: Using in vitro studies, we observed an enhanced endothelial cell response to radiation doses of 8 and 16 Gy when compared to tumor cells. Administering Sunitinib alone significantly increased HUVEC cell death, while having modest additive effects when combined with radiation. Sunitinib also increased tumor cell death when combined with 8 and 16 Gy radiation doses. In comparison, we found that the clonogenic response of in vivo treated tumor cells more closely resembled that of in vitro treated endothelial cells than in vitro treated tumor cells. CONCLUSION: Our results indicate that the endothelium is an important regulator of tumor response to radiotherapy, and that Sunitinib can enhance tumor radiosensitivity. To the best of our knowledge, this is the first time that Sunitinib is investigated in combination with radiotherapy on the MDA-MB-231 breast cancer cell line.

Dose-dependent response of tumor vasculature to radiation therapy in combination with Sunitinib depicted by three-dimensional high-frequency power Doppler ultrasound.

PURPOSE: Large doses of radiation (8-20 Gy) preferentially target tumor vasculature. This vascular response is suggested to regulate tumor response to radiotherapy. Here, we investigate the relative contributions of direct cell killing by radiation versus tumor cell death due to radiation effects on the vasculature. We also examine Sunitinib's mechanism of action as a tumor radiosensitizer. EXPERIMENTAL DESIGN: MDA-MB-231 xenografts were treated with radiation doses of 2-16 Gy alone, or in combination with bFGF (endothelial radio-protector) or Sunitinib as pharmacological modulators of the vasculature. Sunitinib was orally administered for 2 weeks at 30 mg/kg before radiotherapy; bFGF was intravenously injected 1 h prior to irradiation. Three-dimensional high-frequency power Doppler ultrasound was used to assess relative changes in tumor vasculature. Immunohistochemistry, clonogenic and tumor growth assays were used to quantify tumor response. RESULTS: Significant reductions in power Doppler signal of up to 50 % were observed for 8 and 16 Gy treatments, along with a dose-dependent increase in cell death. No significant change in power Doppler signal and minimal tumor cell death were noted for tumors treated with radiation and bFGF. Treatments where Sunitinib was combined with radiation demonstrated a significant increase in flow signal at doses equal or greater than 8 Gy. This was accompanied with a significant increase in cell death when compared to radiation or Sunitinib alone. CONCLUSION: We confirm that tumor response to high doses of radiation is regulated by its vasculature. We also posit that the response observed when radiation is combined with Sunitinib is linked to a vascular "normalization" effect.

Technical note: Noninvasive monitoring of normal tissue radiation damage using spectral quantitative ultrasound spectroscopy.

BACKGROUND: While radiation therapy (RT) is a critical component of breast cancer therapy and is known to decrease overall local recurrence rates, recent studies have shown that normal tissue radiation damage may increase recurrence risk. Fibrosis is a well-known consequence of RT, but the specific sequence of molecular and mechanical changes induced by RT remains poorly understood. PURPOSE: To improve cancer therapy outcomes, there is a need to understand the role of the irradiated tissue microenvironment in tumor recurrence. This study seeks to evaluate the use of spectral quantitative ultrasound (spectral QUS) for real time determination of the normal tissue characteristic radiation response and to correlate these results to molecular features in irradiated tissues. METHODS: Murine mammary fat pads (MFPs) were irradiated to 20 Gy, and spectral QUS was used to analyze tissue physical properties pre-irradiation as well as at 1, 5, and 10 days post-irradiation. Tissues were processed for scanning electron microscopy imaging as well as histological and immunohistochemical staining to evaluate morphology and structure. RESULTS: Tissue morphological and structural changes were observed non-invasively following radiation using mid-band fit (MBF), spectral slope (SS), and spectral intercept (SI) measurements obtained from spectral QUS. Statistically significant shifts in MBF and SI indicate structural tissue changes in real time, which matched histological observations. Radiation damage was indicated by increased adipose tissue density and extracellular matrix (ECM) deposition. CONCLUSIONS: Our findings demonstrate the potential of using spectral QUS to noninvasively evaluate normal tissue changes resulting from radiation damage. This supports further pre-clinical studies to determine how the tissue microenvironment and physical properties change in response to therapy, which may be important for improving treatment strategies.

Automated Image Analysis for Characterization of Circulating Tumor Cells and Clusters Sorted by Magnetic Levitation.

Magnetic levitation-based sorting technologies have revolutionized the detection and isolation of rare cells, including circulating tumor cells (CTCs) and circulating tumor cell clusters (CTCCs). Manual counting and quantification of these cells are prone to time-consuming processes, human error, and inter-observer variability, particularly challenging when heterogeneous cell types in 3D clusters are present. To overcome these challenges, we developed "Fastcount," an in-house MATLAB-based algorithm for precise, automated quantification and phenotypic characterization of CTCs and CTCCs, in both 2D and 3D. Fastcount is 120 times faster than manual counting and produces reliable results with a ±7.3% deviation compared to a trained laboratory technician. By analyzing 400 GB of fluorescence imaging data, we showed that Fastcount outperforms manual counting and commercial software when cells are aggregated in 3D or staining artifacts are present, delivering more accurate results. We further employed Fastcount for automated analysis of 3D image stacks obtained from CTCCs isolated from colorectal adenocarcinoma and renal cell carcinoma blood samples. Interestingly, we observed a highly heterogeneous spatial cellular composition within CTCCs, even among clusters from the same patient. Overall, Fastcount can be employed for various applications with lab-chip devices, such as CTC detection, CTCC analysis in 3D and cell detection in biosensors.

Automated fatty liver disease detection in point-of-care ultrasound B-mode images.

Purpose: Non-alcoholic fatty liver disease (NAFLD) is an increasing global health concern, with a prevalence of 25% worldwide. The rising incidence of NAFLD, an asymptomatic condition, reinforces the need for systematic screening strategies in primary care. We present the use of non-expert acquired point-of-care ultrasound (POCUS) B-mode images for the development of an automated steatosis classification algorithm. Approach: We obtained a Health Insurance Portability and Accountability Act compliant dataset consisting of 478 patients [body mass index 23.60±3.55, age 40.97±10.61], imaged with POCUS by non-expert health care personnel. A U-Net deep learning (DL) model was used for liver segmentation in the POCUS B-mode images, followed by 224×224 patch extraction of liver parenchyma. Several DL models including VGG-16, ResNet-50, Inception V3, and DenseNet-121 were trained for binary classification of steatosis. All layers of each tested model were unfrozen, and the final layer was replaced with a custom classifier. Majority voting was applied for patient-level results. Results: On a hold-out test set of 81 patients, the final DenseNet-121 model yielded an area under the receiver operator characteristic curve of 90.1%, sensitivity of 95.0%, and specificity of 85.2% for the detection of liver steatosis. Average cross-validation performance in models using patches of liver parenchyma as input outperformed methods using complete B-mode frames. Conclusions: Despite minimal POCUS acquisition training, and low-quality B-mode images, it is possible to detect steatosis using DL algorithms. Implementation of this algorithm in POCUS software may offer an accessible, low-cost steatosis screening technology, for use by non-expert health care personnel.

Characterization of spatially mapped volumetric molecular ultrasound signals for predicting response to anti-vascular therapy.

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.

Comparative Study of Raw Ultrasound Data Representations in Deep Learning to Classify Hepatic Steatosis.

Adiposity accumulation in the liver is an early-stage indicator of non-alcoholic fatty liver disease. Analysis of ultrasound (US) backscatter echoes from liver parenchyma with deep learning (DL) may offer an affordable alternative for hepatic steatosis staging. The aim of this work was to compare DL classification scores for liver steatosis using different data representations constructed from raw US data. Steatosis in N = 31 patients with confirmed or suspected non-alcoholic fatty liver disease was stratified based on fat-fraction cutoff values using magnetic resonance imaging as a reference standard. US radiofrequency (RF) frames (raw data) and clinical B-mode images were acquired. Intermediate image formation stages were modeled from RF data. Power spectrum representations and phase representations were also calculated. Co-registered patches were used to independently train 1-, 2- and 3-D convolutional neural networks (CNNs), and classifications scores were compared with cross-validation. There were 67,800 patches available for 2-D/3-D classification and 1,830,600 patches for 1-D classification. The results were also compared with radiologist B-mode annotations and quantitative ultrasound (QUS) metrics. Patch classification scores (area under the receiver operating characteristic curve [AUROC]) revealed significant reductions along successive stages of the image formation process (p < 0.001). Patient AUROCs were 0.994 for RF data and 0.938 for clinical B-mode images. For all image formation stages, 2-D CNNs revealed higher patch and patient AUROCs than 1-D CNNs. CNNs trained with power spectrum representations converged faster than those trained with RF data. Phase information, which is usually discarded in the image formation process, provided a patient AUROC of 0.988. DL models trained with RF and power spectrum data (AUROC = 0.998) provided higher scores than conventional QUS metrics and multiparametric combinations thereof (AUROC = 0.986). Radiologist annotations indicated lower hepatic steatosis classification accuracies (Acc = 0.914) with respect to magnetic resonance imaging proton density fat fraction that DL models (Acc = 0.989). Access to raw ultrasound data combined with artificial intelligence techniques may offer superior opportunities for quantitative tissue diagnostics than conventional sonographic images.

Dynamic Contrast-Enhanced Ultrasound Modeling of an Analog to Pseudo-Diffusivity in Intravoxel Incoherent Motion Magnetic Resonance Imaging.

Tumor perfusion and vascular properties are important determinants of cancer response to therapy and thus various approaches for imaging perfusion are being explored. In particular, Intravoxel Incoherent Motion (IVIM) MRI has been actively researched as an alternative to Dynamic-Contrast-Enhanced (DCE) CT and DCE-MRI as it offers non-ionizing, non-contrast-based perfusion imaging. However, for repetitive treatment assessment in a short time period, high cost, limited access, and inability to scan at the bedside remain disadvantages of IVIM MRI. We propose an analysis framework that may enable 3D DCE Ultrasound (DCE-US) - low cost, bedside imaging with excellent safety record - as an alternative modality to IVIM MRI for the generation of DCE-US based pseudo-diffusivity maps in acoustically accessible anatomy and tumors. Modelling intravascular contrast propagation as a convective-diffusive process, we reconstruct parametric maps of pseudo-diffusivity by solving a large-scale fully coupled inverse problem without any assumptions regarding local constancy of the reconstructed parameters. In a mouse tumor model, we demonstrate that the 3D DCE-US pseudo-diffusivity is repeatable, sensitive to treatment with an antiangiogenic agent, and moderately correlated to histological measures of perfusion and angiogenesis.

Diagnostic Performance of 9 Quantitative Ultrasound Parameters for Detection and Classification of Hepatic Steatosis in Nonalcoholic Fatty Liver Disease.

BACKGROUND: Nonalcoholic fatty liver disease (NAFLD) is a leading cause of chronic liver disease worldwide. Quantitative ultrasound (QUS) parameters based on radiofrequency raw data show promise in quantifying liver fat. PURPOSE: The aim of this study was to evaluate the diagnostic performance of 9 QUS parameters compared with magnetic resonance imaging (MRI)-estimated proton density fat fraction (PDFF) in detecting and staging hepatic steatosis in patients with or suspected of NAFLD. MATERIALS AND METHODS: In this Health Insurance Portability and Accountability Act-compliant institutional review board-approved prospective study, 31 participants with or suspected of NAFLD, without other underlying chronic liver diseases (13 men, 18 women; average age, 52 years [range, 26-90 years]), were examined. The following parameters were obtained: acoustic attenuation coefficient (AC); hepatorenal index (HRI); Nakagami parameter; shear wave elastography measures such as shear wave elasticity, viscosity, and dispersion; and spectroscopy-derived parameters including spectral intercept (SI), spectral slope (SS), and midband fit (MBF). The diagnostic ability (area under the receiver operating characteristic curves and accuracy) of QUS parameters was assessed against different MRI-PDFF cutoffs (the reference standard): 6.4%, 17.4%, and 22.1%. Linearity with MRI-PDFF was evaluated with Spearman correlation coefficients (p). RESULTS: The AC, SI, Nakagami, SS, HRI, and MBF strongly correlated with MRI-PDFF (P = 0.89, 0.89, 0.88, -0.87, 0.81, and 0.71, respectively [P < 0.01]), with highest area under the receiver operating characteristic curves (ranging from 0.85 to 1) for identifying hepatic steatosis using 6.4%, 17.4%, and 22.1% MRI-PDFF cutoffs. In contrast, shear wave elasticity, shear wave viscosity, and shear wave dispersion did not strongly correlate to MRI-PDFF (P = 0.45, 0.38, and 0.07, respectively) and had poor diagnostic performance. CONCLUSION: The AC, Nakagami, SI, SS, MBF, and HRI best correlate with MRI-PDFF and show high diagnostic performance for detecting and classifying hepatic steatosis in our study population. SUMMARY STATEMENT: Quantitative ultrasound is an accurate alternative to MRI-based techniques for evaluating hepatic steatosis in patients with or at risk of NAFLD. KEY FINDINGS: Our preliminary results show that specific quantitative ultrasound parameters accurately detect different degrees of hepatic steatosis in NAFLD.

Contrast-enhanced ultrasound imaging for assessing organ perfusion in rainbow trout (Oncorhynchus mykiss).

Contrast-enhanced ultrasound (CEUS) imaging has great potential as a non-lethal, inexpensive monitoring tool in aquatic toxicology. It is a well-established clinical imaging approach that combines real-time, quantitative assessment of organ blood flow, with morphological data. In humans, it has been extensively used to measure changes in blood flow that can be attributed to cancer, inflammation, and other biological abnormalities. However, it has yet to be explored as a tool for fish physiology or environmental toxicology. In this study, our goal was to determine if CEUS could be used to visualize and measure blood flow in the liver of a rainbow trout. All rainbow trout received two injections of an ultrasound contrast agent, microbubbles. A subset received a third injection after administration of propranolol, a non-specific beta1 & 2-blocker, to determine if changes in blood flow could be detected. Ultrasound contrast time-intensity curves (TIC) were obtained, fit to a lognormal model, and different perfusion parameters were calculated. Contrast enhancement was observed in all rainbow trout livers, with high percentage between repeated measurements, including blood flow (80.6 ± 27.3%), area under the curve (73.2 ± 14%), blood volume (84 ± 14.2%) and peak enhancement (86.7 ± 7.5%). After administration of propranolol, we detected a non-significant (p > 0.05) increase in area under the curve (102.6 ± 44.2%), peak enhancement (77.3 ± 106.4), blood volume (48.2 ± 74.5%), and decrease in hepatic blood flow (-17.3 ± 37.1%). These data suggest that CEUS imaging is suitable to measure organ blood flow in fish, and demonstrates tremendous potential for exploring different organs, fish species, and effects of chemical contaminants in future studies.

The measurement of ultrasound scattering from individual micron-sized objects and its application in single cell scattering.

The measurement of the ultrasound backscatter from individual micron-sized objects such as cells is required for various applications such as tissue characterization. However, performing such a measurement remains a challenge. For example, the presence of air bubbles in a suspension of cells during the measurements may lead to the incorrect interpretation of the acoustic signals. This work introduces a technique for measuring the ultrasound backscatter from individual micron-sized objects by combining a microinjection system with a co-registered optical microscope and an ultrasound imaging device. This allowed the measurement of the ultrasound backscatter response from a single object under optical microscope guidance. The optical and ultrasonic data were used to determine the size of the object and to deduce its backscatter responses, respectively. In order to calibrate the system, the backscatter frequency responses from polystyrene microspheres were measured and compared to theoretical predictions. A very good agreement was found between the measured backscatter responses of individual microspheres and theoretical predictions of an elastic sphere. The backscatter responses from single OCI-AML-5 cells were also investigated. It was found that the backscatter responses from AML cells are best modeled using the fluid sphere model. The advantages, limitations, and future applications of the developed technique are discussed.

Quantitative ultrasound approaches for diagnosis and monitoring hepatic steatosis in nonalcoholic fatty liver disease.

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.

Spatial Characterization of Tumor Perfusion Properties from 3D DCE-US Perfusion Maps are Early Predictors of Cancer Treatment Response.

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.

A multi-model framework to estimate perfusion parameters using contrast-enhanced ultrasound imaging.

PURPOSE: Contrast-enhanced ultrasound imaging has expanded the diagnostic potential of ultrasound by enabling real-time imaging and quantification of tissue perfusion. Several perfusion models and curve fitting methods have been developed to quantify the temporal behavior of tracer signal and standardize perfusion quantification. While the least-squares approach has traditionally been applied for curve fitting, it can be inadequate for noisy and complex data. Moreover, previous research suggests that certain perfusion models may be more relevant depending on the organ or tissue imaged. We propose a multi-model framework to select the most appropriate perfusion model and curve fitting method for each diagnostic application. METHODS: Our multi-model approach uses a system identification method, which estimates perfusion parameters from the model with the best fit to a given time-intensity curve. We compared current perfusion quantification methods that use a single perfusion model and curve fitting method and our proposed multi-model framework on bolus 3D dynamic contrast-enhanced ultrasound (DCE-US) in vivo images obtained in mice implanted with a colon cancer, as well as on simulation data. The quality of fit in estimating perfusion parameters was evaluated using the Spearman correlation coefficient, the coefficient of determination (R2 ), and the normalized root-mean-square error (NRMSE) to ensure that the multi-model framework finds the best perfusion model and curve fitting algorithm. RESULTS: Our multi-model framework outperforms conventional single perfusion model approaches with least-squares optimization, providing more robust perfusion parameter estimation. R2 and NRMSE are 0.98 and 0.18, respectively, for our proposed method. By comparison, the performance of the traditional approach is much more dependent upon the selection of the appropriate model. The R2 and NRMSE are 0.91 and 0.31, respectively. CONCLUSIONS: The proposed multi-model framework for perfusion modeling outperforms the current approach of single perfusion modeling using least-squares optimization and more robustly estimates perfusion parameters when using empiric data labeled by an expert as the gold standard. Our technique is minimally sensitive to issues affecting the accuracy of perfusion parameter estimation, including rise time, noise, region of interest size, and frame rate. This framework could be of key utility in modeling different perfusion systems in different tissues and organs.

Quantitative Ultrasound Spectroscopy for Differentiation of Hepatocellular Carcinoma from At-Risk and Normal Liver Parenchyma.

PURPOSE: Quantitative ultrasound approaches can capture tissue morphologic properties to augment clinical diagnostics. This study aims to clinically assess whether quantitative ultrasound spectroscopy (QUS) parameters measured in hepatocellular carcinoma (HCC) tissues can be differentiated from those measured in at-risk or healthy liver parenchyma. EXPERIMENTAL DESIGN: This prospective Health Insurance Portability and Accountability Act (HIPAA)-compliant study was approved by the Institutional Review Board. Fifteen patients with HCC, 15 non-HCC patients with chronic liver disease, and 15 healthy volunteers were included (31.1% women; 68.9% men). Ultrasound radiofrequency data were acquired in each patient in both liver lobes at two focal depths (3/9 cm). Region of interests (ROIs) were drawn on HCC and liver parenchyma. The average normalized power spectrum for each ROI was extracted, and a linear regression was fit within the -6 dB bandwidth, from which the midband fit (MBF), spectral intercept (SI), and spectral slope (SS) were extracted. Differences in QUS parameters between the ROIs were tested by a mixed-effects regression. RESULTS: There was a significant intraindividual difference in MBF, SS, and SI between HCC and adjacent liver parenchyma (P < 0.001), and a significant interindividual difference between HCC and at-risk and healthy non-HCC parenchyma (P < 0.001). In patients with HCC, cirrhosis (n = 13) did not significantly change any of the three parameters (P > 0.8) in differentiating HCC from non-HCC parenchyma. MBF (P = 0.12), SI (P = 0.33), and SS (P = 0.57) were not significantly different in non-HCC tissue among the groups. CONCLUSIONS: The QUS parameters are significantly different in HCC versus non-HCC liver parenchyma, independent of underlying cirrhosis. This could be leveraged for improved HCC detection with ultrasound in the future.

Pharmacokinetic Modeling of Targeted Ultrasound Contrast Agents for Quantitative Assessment of Anti-Angiogenic Therapy: a Longitudinal Case-Control Study in Colon Cancer.

PURPOSE: To evaluate quantitative and semi-quantitative ultrasound molecular imaging (USMI) for antiangiogenic therapy monitoring in human colon cancer xenografts in mice. PROCEDURES: Colon 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. RESULTS: Significant 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). CONCLUSION: USMI (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.

Monitoring Breast Cancer Response to Neoadjuvant Chemotherapy Using Ultrasound Strain Elastography.

Strain elastography was used to monitor response to neoadjuvant chemotherapy (NAC) in 92 patients with biopsy-proven, locally advanced breast cancer. Strain elastography data were collected before, during, and after NAC. Relative changes in tumor strain ratio (SR) were calculated over time, and responder status was classified according to tumor size changes. Statistical analyses determined the significance of changes in SR over time and between response groups. Machine learning techniques, such as a naïve Bayes classifier, were used to evaluate the performance of the SR as a marker for Miller-Payne pathological endpoints. With pathological complete response (pCR) as an endpoint, a significant difference (P < .01) in the SR was observed between response groups as early as 2 weeks into NAC. Naïve Bayes classifiers predicted pCR with a sensitivity of 84%, specificity of 85%, and area under the curve of 81% at the preoperative scan. This study demonstrates that strain elastography may be predictive of NAC response in locally advanced breast cancer as early as 2 weeks into treatment, with high sensitivity and specificity, granting it the potential to be used for active monitoring of tumor response to chemotherapy.

Role of Acid Sphingomyelinase and Ceramide in Mechano-Acoustic Enhancement of Tumor Radiation Responses.

Background: High-dose radiotherapy (>8-10 Gy) causes rapid endothelial cell death via acid sphingomyelinase (ASMase)-induced ceramide production, resulting in biologically significant enhancement of tumor responses. To further augment or solicit similar effects at low radiation doses, we used genetic and chemical approaches to evaluate mechano-acoustic activation of the ASMase-ceramide pathway by ultrasound-stimulated microbubbles (USMB). Methods: Experiments were carried out in wild-type and acid sphingomyelinase (asmase) knockout mice implanted with fibrosarcoma xenografts. A cohort of wild-type mice received the ASMase-ceramide pathway inhibitor sphingosine-1-phosphate (S1P). Mice were treated with varying radiation doses, with or without a priori USMB exposure at different microbubble concentrations. Treatment response was assessed with quantitative 3D Doppler ultrasound and immunohistochemistry at baseline, and at three, 24, and 72 hours after treatment, with three to five mice per treatment group at each time point. All statistical tests were two-sided. Results: Results confirmed an interaction between USMB and ionizing radiation at 24 hours (P < .001), with a decrease in tumor perfusion of up to 46.5% by three hours following radiation and USMB. This peaked at 24 hours, persisting for up to 72 hours, and was accompanied by extensive tumor cell death. In contrast, statistically nonsignificant and minimal tumor responses were noted in S1P-treated and asmase knockout mice for all treatments. Conclusions: This work is the first to confirm the involvement of the ASMase-ceramide pathway in mechanotransductive vascular targeting using USMB. Results also confirm that an acute vascular effect is driving this form of enhanced radiation response, and that it can be elicited at low radiation doses (<8-10 Gy) by a priori USMB exposure.