Can Artificial Intelligence Aid Diagnosis by Teleguided Point-of-Care Ultrasound? A Pilot Study for Evaluating a Novel Computer Algorithm for COVID-19 Diagnosis Using Lung Ultrasound.

With the 2019 coronavirus disease (COVID-19) pandemic, there is an increasing demand for remote monitoring technologies to reduce patient and provider exposure. One field that has an increasing potential is teleguided ultrasound, where telemedicine and point-of-care ultrasound (POCUS) merge to create this new scope. Teleguided POCUS can minimize staff exposure while preserving patient safety and oversight during bedside procedures. In this paper, we propose the use of teleguided POCUS supported by AI technologies for the remote monitoring of COVID-19 patients by non-experienced personnel including self-monitoring by the patients themselves. Our hypothesis is that AI technologies can facilitate the remote monitoring of COVID-19 patients through the utilization of POCUS devices, even when operated by individuals without formal medical training. In pursuit of this goal, we performed a pilot analysis to evaluate the performance of users with different clinical backgrounds using a computer-based system for COVID-19 detection using lung ultrasound. The purpose of the analysis was to emphasize the potential of the proposed AI technology for improving diagnostic performance, especially for users with less experience.

Hydralazine-augmented contrast ultrasound imaging improves the detection of hepatocellular carcinoma.

BACKGROUND: Hepatocellular carcinoma (HCC) detection with B-mode and contrast-enhanced ultrasound (CUS) imaging often varies between subjects, especially in patients with background cirrhosis. Various factors contribute to this variability, including the tumor blood flow, tumor size, internal echoes, and its location in livers with diffuse fibro-cirrhotic changes. OBJECTIVE: Towards improving lesion detection, this study evaluates a vasodilator, hydralazine, to enhance the visibility of HCC by reducing its blood flow relative to the surrounding liver tissue. METHODS: HCC were analyzed for tumor visibility measured for B-mode, CUS, and hydralazine-augmented-contrast ultrasound (HyCUS) in an autochthonous HCC rat model. 21 tumors from 12 rats were studied. B-mode and CUS images were acquired before hydralazine injection. Rats received an intravenous hydralazine injection of 5 mg/kg, then images were acquired 20 min later. Four rats were used as controls. The difference in echo intensity of the lesion and the surrounding tissue was used to determine the visibility index (VI). RESULTS: The visibility index for HCC was found to be significantly improved with the use of HyCUS imaging compared to traditional B-mode and CUS imaging. The visibility index for HCC was 16.5 ± 2.8 for HyCUS, compared to 5.3 ± 4.8 for B-mode and 4.1 ± 3.8 for CUS. The differences between HyCUS and the other imaging modalities were statistically significant, with p-values of 0.001 and 0.02, respectively. Additionally, when compared to control cases, HyCUS showed higher discrimination of HCC (VI = 6.4 ± 1.2) with a p-value of 0.003, while B-mode (VI = 6.7 ± 1.4, p = 0.5) and CUS (VI = 6.4 ± 1.2, p = 0.3) showed lower discrimination. CONCLUSION: Vascular blood flow modulation by hydralazine enhances the visibility of HCC. HyCUS offers a potential problem-solving method for detecting HCC when B-mode and CUS are unsuccessful, especially with background fibro-cirrhotic liver disease. Future evaluation of the approach in humans will determine its translatability for clinical applications.

A novel quantitative approach to evaluate femoral head perfusion by contrast-enhanced ultrasound: A pilot study in infants with developmental dysplasia of the hip.

Avascular necrosis (AVN) is a major morbidity that can occur after surgical reduction of a hip with developmental dysplasia. Early detection of changes in femoral head perfusion during surgery may help detect a hip at risk for AVN and guide intraoperative management. Contrast-enhanced ultrasound (CEUS) can be employed for visualization of femoral head perfusion. In this study we evaluate a quantitative CEUS technique to assess femoral head perfusion pre- and post-surgical reduction. CEUS images were obtained following a bolus injection of an ultrasound contrast agent, prior to and again following surgical reduction and casting. An image processing technique called delta projection was used to quantify hip perfusion, measuring peak enhancement (PE) and perfusion index (PI). We analyzed CEUS images of the hips of eight patients, including seven females, whose ages ranged from 4 months to 1 year. In five hips, perfusion increased following surgery, with a mean pre-surgery PE of 6.7 ±2.5(± SE) and PI of 10.5 ±6.3; and a post-reduction PE of 13.1±6.1 (p=0.07) and PI of 14.2 ±6.2 (p=0.008). The change in contrast visualization was observed to be greater within the central aspect of the cartilaginous femoral epiphysis. The proposed technique can quantify pre- and post-surgical perfusion changes on CEUS images in patients with developmental dysplasia. This quantitative technique may provide a more objective and accurate assessment of changes in femoral head perfusion that may have the potential to be indicative of the risk of developing AVN.

Ultrasound Radiomics for the Detection of Early-Stage Liver Fibrosis.

Objective: The study evaluates quantitative ultrasound (QUS) texture features with machine learning (ML) to enhance the sensitivity of B-mode ultrasound (US) for the detection of fibrosis at an early stage and distinguish it from advanced fibrosis. Different ML methods were evaluated to determine the best diagnostic model. Methods: 233 B-mode images of liver lobes with early and advanced-stage fibrosis induced in a rat model were analyzed. Sixteen features describing liver texture were measured from regions of interest (ROIs) drawn on B-mode images. The texture features included a first-order statistics run length (RL) and gray-level co-occurrence matrix (GLCM). The features discriminating between early and advanced fibrosis were used to build diagnostic models with logistic regression (LR), naïve Bayes (nB), and multi-class perceptron (MLP). The diagnostic performances of the models were compared by ROC analysis using different train-test sampling approaches, including leave-one-out, 10-fold cross-validation, and varying percentage splits. METAVIR scoring was used for histological fibrosis staging of the liver. Results: 15 features showed a significant difference between the advanced and early liver fibrosis groups, p < 0.05. Among the individual features, first-order statics features led to the best classification with a sensitivity of 82.1−90.5% and a specificity of 87.1−89.8%. For the features combined, the diagnostic performances of nB and MLP were high, with the area under the ROC curve (AUC) approaching 0.95−0.96. LR also yielded high diagnostic performance (AUC = 0.91−0.92) but was lower than nB and MLP. The diagnostic variability between test-train trials, measured by the coefficient-of-variation (CV), was higher for LR (3−5%) than nB and MLP (1−2%). Conclusion: Quantitative ultrasound with machine learning differentiated early and advanced fibrosis. Ultrasound B-mode images contain a high level of information to enable accurate diagnosis with relatively straightforward machine learning methods like naïve Bayes and logistic regression. Implementing simple ML approaches with QUS features in clinical settings could reduce the user-dependent limitation of ultrasound in detecting early-stage liver fibrosis.

Can Sequential Images from the Same Object Be Used for Training Machine Learning Models? A Case Study for Detecting Liver Disease by Ultrasound Radiomics.

Machine learning for medical imaging not only requires sufficient amounts of data for training and testing but also that the data be independent. It is common to see highly interdependent data whenever there are inherent correlations between observations. This is especially to be expected for sequential imaging data taken from time series. In this study, we evaluate the use of statistical measures to test the independence of sequential ultrasound image data taken from the same case. A total of 1180 B-mode liver ultrasound images with 5903 regions of interests were analyzed. The ultrasound images were taken from two liver disease groups, fibrosis and steatosis, as well as normal cases. Computer-extracted texture features were then used to train a machine learning (ML) model for computer-aided diagnosis. The experiment resulted in high two-category diagnosis using logistic regression, with AUC of 0.928 and high performance of multicategory classification, using random forest ML, with AUC of 0.917. To evaluate the image region independence for machine learning, Jenson-Shannon (JS) divergence was used. JS distributions showed that images of normal liver were independent from each other, while the images from the two disease pathologies were not independent. To guarantee the generalizability of machine learning models, and to prevent data leakage, multiple frames of image data acquired of the same object should be tested for independence before machine learning. Such tests can be applied to real-world medical image problems to determine if images from the same subject can be used for training.

Hyperechoic Renal Masses: Differentiation of Angiomyolipomas from Renal Cell Carcinomas using Tumor Size and Ultrasound Radiomics.

A retrospective single-center study was performed to assess the performance of ultrasound image-based texture analysis in differentiating angiomyolipoma (AML) from renal cell carcinoma (RCC) on incidental hyperechoic renal lesions. Ultrasound reports of patients from 2012 to 2017 were queried, and those with a hyperechoic renal mass <5 cm in diameter with further imaging characterization and/or pathological correlation were included. Quantitative texture analysis was performed using a model including 18 texture features. Univariate logistic regression was used to identify texture variables differing significantly between AML and RCC, and the performance of the model was measured using the area under the receiver operating characteristic (ROC) curve. One hundred thirty hyperechoic renal masses in 127 patients characterized as RCCs (25 [19%]) and AMLs (105 [81%]) were included. Size (odds ratio [OR] = 0.12, 95% confidence interval [CI]: 0.04-0.43, p < 0.001) and 4 of 18 texture features, including entropy (OR = 0.09, 95% CI: 0.01-0.81, p = 0.03), gray-level non-uniformity (OR = 0.12, 95% CI: 0.02-0.72, p = 0.02), long-run emphasis (OR = 0.49, 95% CI: 0.27-0.91, p = 0.02) and run-length non-uniformity (OR = 2.18, 95% CI: 1.14-4.16, p = 0.02) were able to differentiate AMLs from RCCs. The area under the ROC curve for the performance of the model, including texture features and size, was 0.945 (p < 0.001). Ultrasound image-based textural analysis enables differentiation of hyperechoic RCCs from AMLs with high accuracy, which improves further when combined with tumor size.

Contrast-enhanced ultrasound for assessing blood flow modulation of hepatocellular carcinoma by hydralazine.

Modulating aberrant tumor microvasculature provides unique opportunities for enhancing ultrasound imaging of hepatocellular carcinoma (HCC). This study aims to use contrast-enhanced ultrasound to evaluate the potential of a potent vasodilator, hydralazine, to attenuate blood flow in HCC while enhancing it in the surrounding liver tissue. The "steel effect," where blood flow is diverted from the lesion to the surrounding tissue aims to enhance lesion-tissue contrast. Methods: HCC was induced in six rats by oral ingestion of diethylnitrosamine for 12 weeks. 10 tumors were studied to assess the enhancement in HCC tumors and surrounding tissue. Contrast-enhanced ultrasound images (CEUS) of each tumor were acquired before and after hydralazine injection. The enhancement of images was analyzed for the qualitative and quantitative assessment of HCC enhancement. Peak enhancement (PE) was calculated, representing the maximum signal intensity reached during the transit of the contrast bolus for both the tumor and the surrounding tissue. Intravenous administration of hydralazine significantly reduced CEUS signals in HCC tumors. The visual examination of images showed that the enhancement of tumors dramatically decreased after hydralazine injection. On the other hand, the surrounding tissue showed an increased enhancement. PE for the HCC changed from (71.8 ± 5) pre hydralazine to (28.7± 4.9), a 61.7% reduction after hydralazine injection, p=0.01. Future studies validating the technique in clinical settings for enhancing lesion-tissue contrast may allow physicians greater precision and accuracy in HCC surveillance for early detection of small tumors.

Health Care and Care-Seeking in Mosul 1 Year After Defeat of ISIS.

OBJECTIVE: Our objective was to compare care-seeking patterns in Mosul, Iraq, in 2018, 1 y after Islamic State of Iraq and Syria (ISIS) control, with findings from neighborhoods that had been sampled in 2017. METHODS: For this multi-stage randomized cluster household survey, we created one cluster in each of 20 neighborhoods randomly selected from the 40 neighborhoods in the 2016/17 survey; 12 in east Mosul, 8 in west Mosul. In each, 30 households were interviewed beginning at a randomly selected start house. Questions were derived from the 2016/2017 post-ISIS survey. RESULTS: We interviewed the head of household or senior female in 600 households containing 3375 persons. One year after ISIS, some household demographic shifts had occurred. Diarrhea in children during the past 2 wk decreased from 50.1% to 7.5% (P < 0.001); however, cough/difficulty breathing increased from 15.5% to 33.6% (P < 0.01). Among adults, care-seeking for noncommunicable diseases increased from 22.3% to 43.5% (P < 0.001). Emotional and psychological complaints common in the previous survey were now nearly absent. Pregnancy complications diminished from 65.2% to 15.4% (P < 0.001). CONCLUSIONS: Communicable diseases predominated among children and noncommunicable diseases among adults. Access to health care substantially improved, although barriers remained. Satisfaction with services was mixed, with dissatisfaction expressed about testing, medicine access, and costs, but the work of health providers was rated highly.

Machine learning improves early detection of liver fibrosis by quantitative ultrasound radiomics.

Progression of liver fibrosis to cirrhosis, a severe non-reversible process, is one of the most critical risk factors in developing hepatocellular carcinoma and liver failure. Detection of liver fibrosis at an early stage is therefore essential for better patient management. Ultrasound (US) imaging can provide a noninvasive alternative to biopsies. This study evaluates quantitative US texture features to improve early-stage versus advanced liver fibrosis detection. 157 B-mode US images of different liver lobes acquired from early and advanced fibrosis rat cases were used for analysis. 5-6 regions of interest were placed on each image. Twelve quantitative features that describe liver texture changes were extracted from the images, including first-order histogram, run length (RL), and gray level co-occurrence matrix (GLCM). The diagnostic performance of individual features was high with AUC ranging from 0.80 to 0.94. Logistic regression with leave-one-out cross-validation was used to evaluate the performance of the combined features. All features combined showed a slight improvement in performance with AUC = 0.95, sensitivity = 96.8%, and specificity = 93.7%. Quantitative US texture features characterize liver fibrosis changes with high accuracy and can differentiate early from advanced disease. Quantitative ultrasound, if validated in future clinical studies, can have a potential role in identifying fibrosis changes that are not easily detected by visual US image assessments.

Hydralazine augmented ultrasound hyperthermia for the treatment of hepatocellular carcinoma.

This study investigates the use of hydralazine to enhance ultrasound hyperthermia for the treatment of hepatocellular carcinoma (HCC) by minimizing flow-mediated heat loss from the tumor. Murine HCC tumors were treated with a continuous mode ultrasound with or without an intravenous administration of hydralazine (5 mg/kg). Tumor blood flow and blood vessels were evaluated by contrast-enhanced ultrasound (CEUS) imaging and histology, respectively. Hydralazine markedly enhanced ultrasound hyperthermia through the disruption of tumor blood flow in HCC. Ultrasound treatment with hydralazine significantly reduced peak enhancement (PE), perfusion index (PI), and area under the curve (AUC) of the CEUS time-intensity curves by 91.9 ± 0.9%, 95.7 ± 0.7%, and 96.6 ± 0.5%, compared to 71.4 ± 1.9%, 84.7 ± 1.1%, and 85.6 ± 0.7% respectively without hydralazine. Tumor temperature measurements showed that the cumulative thermal dose delivered by ultrasound treatment with hydralazine (170.8 ± 11.8 min) was significantly higher than that without hydralazine (137.7 ± 10.7 min). Histological assessment of the ultrasound-treated tumors showed that hydralazine injection formed larger hemorrhagic pools and increased tumor vessel dilation consistent with CEUS observations illustrating the augmentation of hyperthermic effects by hydralazine. In conclusion, we demonstrated that ultrasound hyperthermia can be enhanced significantly by hydralazine in murine HCC tumors by modulating tumor blood flow. Future studies demonstrating the safety of the combined use of ultrasound and hydralazine would enable the clinical translation of the proposed technique.

Quantitative pleural line characterization outperforms traditional lung texture ultrasound features in detection of COVID-19.

BACKGROUND AND OBJECTIVE: Lung ultrasound is an inherently user-dependent modality that could benefit from quantitative image analysis. In this pilot study we evaluate the use of computer-based pleural line (p-line) ultrasound features in comparison to traditional lung texture (TLT) features to test the hypothesis that p-line thickening and irregularity are highly suggestive of coronavirus disease 2019 (COVID-19) and can be used to improve the disease diagnosis on lung ultrasound. METHODS: Twenty lung ultrasound images, including normal and COVID-19 cases, were used for quantitative analysis. P-lines were detected by a semiautomated segmentation method. Seven quantitative features describing thickness, margin morphology, and echo intensity were extracted. TLT lines were outlined, and texture features based on run-length and gray-level co-occurrence matrix were extracted. The diagnostic performance of the 2 feature sets was measured and compared using receiver operating characteristics curve analysis. Observer agreements were evaluated by measuring interclass correlation coefficients (ICC) for each feature. RESULTS: Six of 7 p-line features showed a significant difference between normal and COVID-19 cases. Thickness of p-lines was larger in COVID-19 cases (6.27 ± 1.45 mm) compared to normal (1.00 ± 0.19 mm), P < 0.001. Among features describing p-line margin morphology, projected intensity deviation showed the largest difference between COVID-19 cases (4.08 ± 0.32) and normal (0.43 ± 0.06), P < 0.001. From the TLT line features, only 2 features, gray-level non-uniformity and run-length non-uniformity, showed a significant difference between normal cases (0.32 ± 0.06, 0.59 ± 0.06) and COVID-19 (0.22 ± 0.02, 0.39 ± 0.05), P = 0.04, respectively. All features together for p-line showed perfect sensitivity and specificity of 100; whereas, TLT features had a sensitivity of 90 and specificity of 70. Observer agreement for p-lines (ICC = 0.65-0.85) was higher than for TLT features (ICC = 0.42-0.72). CONCLUSION: P-line features characterize COVID-19 changes with high accuracy and outperform TLT features. Quantitative p-line features are promising diagnostic tools in the interpretation of lung ultrasound images in the context of COVID-19.

Photoacoustic monitoring of oxygenation changes induced by therapeutic ultrasound in murine hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is a highly vascular solid tumor. We have previously shown that ultrasound (US) therapy significantly reduces tumor vascularity. This study monitors US-induced changes in tumor oxygenation on murine HCC by photoacoustic imaging (PAI). Oxygen saturation and total hemoglobin were assessed by PAI before and after US treatments performed at different intensities of continuous wave (CW) bursts and pulsed wave (PW) bursts US. PAI revealed significant reduction both in HCC oxygen saturation and in total hemoglobin, proportional to the US intensity. Both CW bursts US (1.6 W/cm2) and the PW bursts US (0.8 W/cm2) significantly reduced HCC oxygen saturation and total hemoglobin which continued to diminish with time following the US treatment. The effects of US therapy were confirmed by power Doppler and histological examination of the hemorrhage in tumors. By each measure, the changes observed in US-treated HCC were more prevalent than those in sham-treated tumors and were statistically significant. In conclusion, the results show that US is an effective vascular-targeting therapy for HCC. The changes in oxygenation induced by the US treatment can be noninvasively monitored longitudinally by PAI without the use of exogenous image-enhancing agents. The combined use of PAI and the therapeutic US has potential for image-guided vascular therapy for HCC.

Subsequent Ultrasound Vascular Targeting Therapy of Hepatocellular Carcinoma Improves the Treatment Efficacy.

The response of hepatocellular carcinoma (HCC) to anti-vascular ultrasound therapy (AVUS) can be affected by the inherent differences in tumor vascular structure, and the functionality of tumor vessels at the time of treatment. In this study, we evaluate the hypothesis that repeated subsequent AVUS therapies are a possible approach to overcome these factors and improve the therapeutic efficacy of AVUS. HCC was induced in 30 Wistar rats by oral ingestion of diethylnitrosamine (DEN) for 12 weeks. A total of 24 rats received treatment with low intensity, 2.8 MHz ultrasound with an intravenous injection of microbubbles. Treated rats were divided into three groups: single therapy group (ST), 2-days subsequent therapy group (2DST), and 7-days subsequent therapy group (7DST). A sham control group did not receive ultrasound therapy. Tumor perfusion was measured by quantitative contrast-enhanced ultrasound (CEUS) nonlinear and power-Doppler imaging. Tumors were harvested for histologic evaluation of ultrasound-induced vascular changes. ANOVA was used to compare the percent change of perfusion parameters between the four treatment arms. HCC tumors treated with 2DST showed the largest reduction in tumor perfusion, with 75.3% reduction on average for all perfusion parameters. The ST group showed an average decrease in perfusion of 54.3%. The difference between the two groups was significant p < 0.001. The 7DST group showed a reduction in tumor perfusion of 45.3%, which was significant compared to the 2DST group (p < 0.001) but not different from the ST group (p = 0.2). The use of subsequent targeted AVUS therapies applied shortly (two days) after the first treatment enhanced the anti-vascular effect of ultrasound. This gain, however, was lost for a longer interval (1 week) between the therapies, possibly due to tumor necrosis and loss of tumor viability. These findings suggest that complex interplay between neovascularization and tumor viability plays a critical role in treatment and, therefore, must be actively monitored following treatment by CEUS for optimizing sequential treatment.

Photoacoustic Imaging for Assessing Tissue Oxygenation Changes in Rat Hepatic Fibrosis.

Chronic liver inflammation progressively evokes fibrosis and cirrhosis resulting in compromised liver function, and often leading to cancer. Early diagnosis and staging of fibrosis is crucial because the five-year survival rate of early-stage liver cancer is high. This study investigates the progression of hepatic fibrosis induced in rats following ingestion of diethylnitrosamine (DEN). Changes in oxygen saturation and hemoglobin concentration resulting from chronic inflammation were assayed longitudinally during DEN ingestion by photoacoustic imaging (PAI). Accompanying liver tissue changes were monitored simultaneously by B-mode sonographic imaging. Oxygen saturation and hemoglobin levels in the liver increased over 5 weeks and peaked at 10 weeks before decreasing at 13 weeks of DEN ingestion. The oxygenation changes were accompanied by an increase in hepatic echogenicity and coarseness in the ultrasound image. Histology at 13 weeks confirmed the development of severe fibrosis and cirrhosis. The observed increase in PA signal representing enhanced blood oxygenation is likely an inflammatory physiological response to the dietary DEN insult that increases blood flow by the development of neovasculature to supply oxygen to a fibrotic liver during the progression of hepatic fibrosis. Assessment of oxygenation by PAI may play an important role in the future assessment of hepatic fibrosis.

Color Doppler Ultrasound Improves Machine Learning Diagnosis of Breast Cancer.

Color Doppler is used in the clinic for visually assessing the vascularity of breast masses on ultrasound, to aid in determining the likelihood of malignancy. In this study, quantitative color Doppler radiomics features were algorithmically extracted from breast sonograms for machine learning, producing a diagnostic model for breast cancer with higher performance than models based on grayscale and clinical category from the Breast Imaging Reporting and Data System for ultrasound (BI-RADSUS). Ultrasound images of 159 solid masses were analyzed. Algorithms extracted nine grayscale features and two color Doppler features. These features, along with patient age and BI-RADSUS category, were used to train an AdaBoost ensemble classifier. Though training on computer-extracted grayscale features and color Doppler features each significantly increased performance over that of models trained on clinical features, as measured by the area under the receiver operating characteristic (ROC) curve, training on both color Doppler and grayscale further increased the ROC area, from 0.925 ± 0.022 to 0.958 ± 0.013. Pruning low-confidence cases at 20% improved this to 0.986 ± 0.007 with 100% sensitivity, whereas 64% of the cases had to be pruned to reach this performance without color Doppler. Fewer borderline diagnoses and higher ROC performance were both achieved for diagnostic models of breast cancer on ultrasound by machine learning on color Doppler features.

A Review of Early Experience in Lung Ultrasound in the Diagnosis and Management of COVID-19.

A novel coronavirus (2019-nCoV) was identified as the cause of a cluster of pneumonia in Wuhan, China, at the end of 2019. Since then more than eight million confirmed cases of coronavirus disease 2019 (COVID-19) have been reported around the globe. The current gold standard for etiologic diagnosis is reverse transcription-polymerase chain reaction analysis of respiratory-tract specimens, but the test has a high false-negative rate owing to both nasopharyngeal swab sampling error and viral burden. Hence diagnostic imaging has emerged as a fundamental component of current management of COVID-19. Currently, high-resolution computed tomography is the main imaging tool for primary diagnosis and evaluation of disease severity in patients. Lung ultrasound (LUS) imaging has become a safe bedside imaging alternative that does not expose the patient to radiation and minimizes the risk of contamination. Although the number of studies to date is limited, LUS findings have demonstrated high diagnostic sensitivity and accuracy, comparable with those of chest computed tomography scans. In this note we review the current state of the art of LUS in evaluating pulmonary changes induced by COVID-19. The goal is to identify characteristic sonographic findings most suited for the diagnosis of COVID-19 pneumonia infections.

Dose-dependent effects of ultrasound therapy on hepatocellular carcinoma.

Non-invasive ischemic cancer therapy requires reduced blood flow whereas drug delivery and radiation therapy require increased tumor perfusion for a better response. In this study we investigate the hypothesis that different dose models of antivascular ultrasound therapy (AVUS) can have opposite effects on hepatocellular carcinoma (HCC) tumor blood flow. HCC was induced in 22 Wistar rats by ingestion of diethylnitrosamine (DEN) for 12 weeks. Rats received AVUS treatment at low and high doses. Low dose group received 1 watt/cm2 ultrasound for 1 min with 0.2 mL microbubbles injected IV. High dose group received 2 watts/cm2 for 2 min with 0.7 mL microbubbles IV. A sham group did not receive any treatment. Tumor perfusion was measured before and after AVUS with contrast-enhanced ultrasound. Quantitative perfusion measures: perfusion index (PI) and peak enhancement (PE) were obtained from each AVUS dose. After high-dose AVUS, PE and PI decreased by an average of 58.1 ± 4.9% and 49.1 ± 6.5 % respectively. Conversely, following low dose AVUS, PE and PI increased from baseline by an average of 47.8 ± 4.5% % and 20.3 ± 2.4 %, respectively. The high-dose AVUS therapy decreased tumoral perfusion, an effect that could be used for noninvasive ischemic therapy. Conversely, low-dose therapy increased tumor perfusion, which may improve drug delivery or radiation therapy. These opposite therapy effects can support multiple roles for AVUS in cancer therapy by tunable modulation of blood flow in tumors.

Brachial flow-mediated dilation by continuous monitoring of arterial cross-section with ultrasound imaging.

OBJECTIVE: Impairment of flow-mediated dilation of the brachial artery is a marker of endothelial dysfunction and often predisposes atherosclerosis and cardiovascular events. In this study, we propose a user-guided automated approach for monitoring arterial cross-section during hyperemic response to improve reproducibility and sensitivity of flow-mediated dilation. MATERIAL AND METHODS: Ultrasound imaging of the brachial artery was performed in 11 volunteers in cross-sectional and in 5 volunteers in longitudinal view. During each examination, images were recorded continuously before and after inducing ischemia. Time-dilation curves of the brachial lumen cross-section were measured by user-guided automated segmentation of brachial images with the feed-forward active contour (FFAC) algorithm. %FMD was determined by the ratio of peak dilation to the baseline value. Each measurement was repeated twice in two sessions 1 h apart on the same arm to evaluate the reproducibility of the measurements. The intra-subject variation in flow-mediated dilation between two sessions (subject-specific) and inter-group variation in flow-mediated dilation with all the subjects within a session grouped together (group-specific) were measured for FFAC. The FFAC measurements were compared with the conventional diameter measurements made using echo tracking in longitudinal views. RESULTS: Flow-mediated dilation values for cross-sectional area were greater than those measured by diameter dilation: 33.1% for cross-sectional area compared to 22.5% for diameter. Group-specific flow-mediated dilation measurements for cross-sectional area were highly reproducible: 33.2% vs. 33.0% (p > 0.05) with coefficient of variation CV of 0.4%. The group-specific flow-mediated dilations measured by echo tracking for the two sessions were 21.1 vs. 23.9% with CV of 9%. Subject-specific CV for cross-sectional area by FFAC was 10% ± 2% versus 24% ± 10% for the conventional approach. Using correlation as a metric of evaluation also showed better performance for cross-sectional imaging: correlation coefficient, R, between two sessions for cross-sectional area was 0.92 versus 0.72 for the conventional approach based on diameter measurements. CONCLUSION: Peak dilation area measured by continuous automated monitoring of cross-sectional area of the brachial artery provides more reproducible and higher-sensitivity measurement of flow-mediated dilation compared to the conventional approach of using vascular diameter measured using longitudinal imaging.

Microbubble-enhanced ultrasound for the antivascular treatment and monitoring of hepatocellular carcinoma.

Background and Objective: Hepatocellular carcinoma (HCC) is the most common primary liver malignancy, and its current management relies heavily on locoregional therapy for curative therapy, bridge to transplant, and palliative therapy. Locoregional therapies include ablation and hepatic artery therapies such as embolization and radioembolization. In this study we evaluate the feasibility of using novel antivascular ultrasound (AVUS) as a noninvasive locoregional therapy to reduce perfusion in HCC lesions in a rat model and, monitor the effect with contrast-enhanced ultrasound imaging. Methods: HCC was induced in 36 Wistar rats by the ingestion of 0.01% diethylnitrosamine (DEN) for 12 weeks. Two therapy regimens of AVUS were evaluated. A primary regimen (n = 19) utilized 2-W/cm2, 3-MHz ultrasound (US) for 6 minutes insonation with 0.7 ml of microbubbles administered as an intravenous bolus. An alternate dose at half the primary intensity, sonication time, and contrast concentration was evaluated in 11 rats to assess the efficacy of a reduced dose. A control group (n = 6) received a sham therapy. Tumor perfusion was measured before and after AVUS with nonlinear contrast ultrasound (NLC) and power Doppler (PD). The quantitative perfusion measures included perfusion index (PI), peak enhancement (PE), time to peak (TTP), and perfusion area from NLC and PD scans. Total tumor area perfused during the scan was measured by a postprocessing algorithm called delta projection. Tumor histology was evaluated for signs of tissue injury and for vascular changes using CD31 immunohistochemistry. Results: DEN exposure induced autochthonous hepatocellular carcinoma lesions in all rats. Across all groups prior to therapy, there were no significant differences in the nonlinear contrast observations of peak enhancement and perfusion index. In the control group, there were no significant differences in any of the parameters after sham treatment. After the primary AVUS regimen, there were significant changes in all parameters (p ≤ 0.05) indicating substantial decreases in tumor perfusion. Peak enhancement in nonlinear contrast scans showed a 37.9% ± 10.1% decrease in tumor perfusion. Following reduced-dose AVUS, there were no significant changes in perfusion parameters, although there was a trend for the nonlinear contrast observations of peak enhancement and perfusion index to increase. Conclusion: This study translated low-intensity AVUS therapy into a realistic in vivo model of HCC and evaluated its effects on the tumor vasculature. The primary dose of AVUS tested resulted in significant vascular disruption and a corresponding reduction in tumor perfusion. A reduced dose of AVUS, on the other hand, was ineffective at disrupting perfusion but demonstrated the potential for enhancing tumor blood flow. Theranostic ultrasound, where acoustic energy and microbubbles are used to monitor the tumor neovasculature as well as disrupt the vasculature and treat lesions, could serve as a potent tool for delivering noninvasive, locoregional therapy for hepatocellular carcinoma.