Machine learning for diagnostic ultrasound of triple-negative breast cancer.

PURPOSE: Early diagnosis of triple-negative (TN) breast cancer is important due to its aggressive biological characteristics, poor clinical outcomes, and limited options for therapy. The goal of this study is to evaluate the potential of machine learning with quantitative ultrasound image features for the diagnosis of TN breast cancer. METHODS: Ultrasonic and clinical data of 140 surgically confirmed breast cancer cases were analyzed retrospectively for the diagnosis of TN and non-TN (NTN) subtypes. The subtypes were classified based on the expression of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2). Ultrasound image features were measured from the grayscale and color Doppler images and used with logistic regression for classification by machine learning. Leave-one-out cross validation was used to train and test the differentiation. Diagnostic performance was measured by the area under receiver operating characteristic (ROC) curve, and sensitivity and specificity determined at the Youdons index. RESULTS: Of the twelve grayscale and Doppler features measured, eight were found to be statistically different for the TN and NTN subtypes (p < 0.05). The area under the ROC curve (AUC) of the statistically significant grayscale (GS) and color Doppler (CD) features was 0.85 and 0.65, respectively. The AUC increased to 0.88 when the GS and CD features were used together, with sensitivity of 86.96% and specificity of 82.91%. Consideration of patient age in the analysis did not improve discrimination of TN and NTN. CONCLUSIONS: The analysis of breast ultrasound images by machine learning achieves high level of differentiation between the TN and NTN subtypes, exceeding the diagnostic performance by standard visual assessments of the images.

Feed-forward active contour analysis for improved brachial artery reactivity testing.

The object of this study was to utilize a novel feed-forward active contour (FFAC) algorithm to find a reproducible technique for analysis of brachial artery reactivity. Flow-mediated dilation (FMD) is an important marker of vascular endothelial function but has not been adopted for widespread clinical use given its technical limitations, including inter-observer variability and differences in technique across clinical sites. We developed a novel FFAC algorithm with the goal of validating a more reliable standard. Forty-six healthy volunteers underwent FMD measurement according to the standard technique. Ultrasound videos lasting 5-10 seconds each were obtained pre-cuff inflation and at minutes 1 through 5 post-cuff deflation in longitudinal and transverse views. Automated segmentation using the FFAC algorithm with initial boundary definition from three different observers was used to analyze the images to measure diameter/cross-sectional area over the cardiac cycle. The %FMD was calculated for average, minimum, and maximum diameters/areas. Using the FFAC algorithm, the population-specific coefficient of variation (CV) at end-diastole was 3.24% for transverse compared to 9.96% for longitudinal measurements; the subject-specific CV was 15.03% compared to 57.41%, respectively. For longitudinal measurements made via the conventional method, the population-specific CV was 4.77% and subject-specific CV was 117.79%. The intraclass correlation coefficient (ICC) for transverse measurements was 0.97 (95% CI: 0.95-0.98) compared to 0.90 (95% CI: 0.84-0.94) for longitudinal measurements with FFAC and 0.72 (95% CI: 0.51-0.84) for conventional measurements. In conclusion, transverse views using the novel FFAC method provide less inter-observer variability than traditional longitudinal views. Improved reproducibility may allow adoption of FMD testing in a clinical setting. The FFAC algorithm is a robust technique that should be evaluated further for its ability to replace the more limited conventional technique for measurement of FMD.

Diagnostic accuracy of hepatorenal index in the detection and grading of hepatic steatosis.

PURPOSE: The objectives of our study were to assess the accuracy of hepatorenal index (HRI) in detection and grading of hepatic steatosis and to evaluate various factors that can affect the HRI measurement. METHODS: Forty-five patients, who had undergone an abdominal sonographic examination within 30 days of liver biopsy, were enrolled. The HRI was calculated as the ratio of the mean brightness levels of the liver and renal parenchymas. The effect of the measurement technique on the HRI was evaluated by using various sizes, depths, and locations of the regions of interest (ROIs) in the liver. The measurements were obtained by two observers. The HRI was compared with the subjective grading of steatosis. RESULTS: The optimal HRI cutoff to detect steatosis was 2.01, yielding a sensitivity of 62.5% and specificity of 95.2%. Subjective grading had a sensitivity of 87.5% and specificity of 62.5%. HRIs of the hepatic steatosis group were statistically different from the no-steatosis group (p < 0.05). However, there was no statistically significant difference between mild steatosis and no-steatosis groups (p value = 0.72). There was a strong correlation between different HRIs based on variable placements of ROIs, except when the ROIs were positioned randomly. Interclass correlation coefficient for measurements performed by two observers was 0.74 (confidence interval: 0.58-0.86). CONCLUSIONS: The HRI is an effective tool for detecting hepatic steatosis. It provides similar accuracy for different methods of ROI placement (except for random placement) and has good interobserver agreement. It, however, is unable to effectively differentiate between absent and mild steatosis. © 2016 Wiley Periodicals, Inc. J Clin Ultrasound 44:580-586, 2016.

Bayesian probability of malignancy with BI-RADS sonographic features.

OBJECTIVES: The purpose of this study was to develop a quantitative approach for combining individual American College of Radiology Breast Imaging Reporting and Data System (BI-RADS) sonographic features of breast masses for assessing the overall probability of malignancy. METHODS: Sonograms of solid breast masses were analyzed by 2 observers blinded to patient age, mammographic features, and lesion pathologic findings. BI-RADS sonographic features were determined by using American College of Radiology criteria. A naïve Bayes model was used to determine the probability of malignancy of all the sonographic features together and with age and BI-RADS mammographic features. The diagnostic performance for various combinations was evaluated by using the area under the receiver operating curve (Az). RESULTS: Sonographic features had high positive and negative predictive values. The Az values for BI-RADS sonographic features for the 2 observers ranged from 0.772 to 0.884, which increased to 0.866 to 0.924 when used with patient age and BI-RADS mammographic features. The benefit of adding age and mammographic information was more marked for the observer with lower initial diagnostic performance. Age-specific analysis showed that diagnostic performance varied with age, with higher performance for patients aged 45 years and younger and patients older than 60 years compared to those aged 46 to 60 years. In 85% of cases, the diagnosis of the observers matched. When the consensus between the observers was used for diagnostic decisions, a high level of diagnostic performance (Az, 0.954) was achieved. CONCLUSIONS: A naïve Bayes model provides a systematic approach for combining sonographic features and other patient characteristics for assessing the probability of malignancy to differentiate malignant and benign breast masses.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.