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.

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.

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.

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.

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.

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.

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.

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.

Machine learning to improve breast cancer diagnosis by multimodal ultrasound.

Despite major advances in breast cancer imaging there is compelling need to reduce unnecessary biopsies by improving characterization of breast lesions. This study demonstrates the use of machine learning to enhance breast cancer diagnosis with multimodal ultrasound. Surgically proven solid breast lesions were studied using quantitative features extracted from grayscale and Doppler ultrasound images. Statistically different features from the logistic regression classifier were used train and test lesion differentiation by leave-one-out cross-validation. The area under the ROC curve (AUC) of the grayscale morphologic features was 0.85 (sensitivity = 87, specificity = 69). The diagnostic performance improved (AUC = 0.89, sensitivity = 79, specificity = 89) when Doppler features were added to the analysis. Reliability of the individual training cycles of leave-one-out cross-validation was tested by measuring dispersion from the mean model. Significant dispersion from the mean, representing weak learning, was observed in 11.3% of cases. Pruning the high-dispersion cases improved the diagnostic performance markedly (AUC 0.96, sensitivity = 92, specificity = 95). These results demonstrate the effectiveness of dispersion to identify weakly learned cases. In conclusion, machine learning with multimodal ultrasound including grayscale and Doppler can achieve high performance for breast cancer diagnosis, comparable to that of human observers. Identifying weakly learned cases can markedly enhance diagnosis.

The diagnostic performance of leak-plugging automated segmentation versus manual tracing of breast lesions on ultrasound images.

PURPOSE: To assess the diagnostic performance of a leak-plugging segmentation method that we have developed for delineating breast masses on ultrasound images. MATERIALS AND METHODS: Fifty-two biopsy-proven breast lesion images were analyzed by three observers using the leak-plugging and manual segmentation methods. From each segmentation method, grayscale and morphological features were extracted and classified as malignant or benign by logistic regression analysis. The performance of leak-plugging and manual segmentations was compared by: size of the lesion, overlap area (Oa ) between the margins, and area under the ROC curves (Az ). RESULTS: The lesion size from leak-plugging segmentation correlated closely with that from manual tracing (R2 of 0.91). Oa was higher for leak plugging, 0.92 ± 0.01 and 0.86 ± 0.06 for benign and malignant masses, respectively, compared to 0.80 ± 0.04 and 0.73 ± 0.02 for manual tracings. Overall Oa between leak-plugging and manual segmentations was 0.79 ± 0.14 for benign and 0.73 ± 0.14 for malignant lesions. Az for leak plugging was consistently higher (0.910 ± 0.003) compared to 0.888 ± 0.012 for manual tracings. The coefficient of variation of Az between three observers was 0.29% for leak plugging compared to 1.3% for manual tracings. CONCLUSION: The diagnostic performance, size measurements, and observer variability for automated leak-plugging segmentations were either comparable to or better than those of manual tracings.

Brachial artery vasomotion and transducer pressure effect on measurements by active contour segmentation on ultrasound.

PURPOSE: To use feed-forward active contours (snakes) to track and measure brachial artery vasomotion on ultrasound images recorded in both transverse and longitudinal views; and to compare the algorithm's performance in each view. METHODS: Longitudinal and transverse view ultrasound image sequences of 45 brachial arteries were segmented by feed-forward active contour (FFAC). The segmented regions were used to measure vasomotion artery diameter, cross-sectional area, and distention both as peak-to-peak diameter and as area. ECG waveforms were also simultaneously extracted frame-by-frame by thresholding a running finite-difference image between consecutive images. The arterial and ECG waveforms were compared as they traced each phase of the cardiac cycle. RESULTS: FFAC successfully segmented arteries in longitudinal and transverse views in all 45 cases. The automated analysis took significantly less time than manual tracing, but produced superior, well-behaved arterial waveforms. Automated arterial measurements also had lower interobserver variability as measured by correlation, difference in mean values, and coefficient of variation. Although FFAC successfully segmented both the longitudinal and transverse images, transverse measurements were less variable. The cross-sectional area computed from the longitudinal images was 27% lower than the area measured from transverse images, possibly due to the compression of the artery along the image depth by transducer pressure. CONCLUSIONS: FFAC is a robust and sensitive vasomotion segmentation algorithm in both transverse and longitudinal views. Transverse imaging may offer advantages over longitudinal imaging: transverse measurements are more consistent, possibly because the method is less sensitive to variations in transducer pressure during imaging.

In vivo noninvasive temperature measurement by B-mode ultrasound imaging.

OBJECTIVE: This study investigated the use of ultrasound image analysis in quantifying temperature changes in tissue, both ex vivo and in vivo, undergoing local hyperthermia. METHODS: Temperature estimation is based on the thermal dependence of the acoustic speed in a heated medium. Because standard beam-forming algorithms on clinical ultrasound scanners assume a constant acoustic speed, temperature-induced changes in acoustic speed produce apparent scatterer displacements in B-mode images. A cross-correlation algorithm computes axial speckle pattern displacement in B-mode images of heated tissue, and a theoretically derived temperature-displacement relationship is used to generate maps of temperature changes within the tissue. Validation experiments were performed on excised tissue and in murine subjects, wherein low-intensity ultrasound was used to thermally treat tissue for several minutes. Diagnostic temperature estimation was performed using a linear array ultrasound transducer, while a fine-wire thermocouple invasively measured the temperature change. RESULTS: Pearson correlations ± SDs between the image-derived and thermocouple-measured temperature changes were R² = 0.923 ± 0.066 for 4 thermal treatments of excised bovine muscle tissue and R² = 0.917 ± 0.036 for 4 treatments of in vivo murine tumor tissue. The average differences between the two temperature measurements were 0.87°C ± 0.72°C for ex vivo studies and 0.97°C ± 0.55°C for in vivo studies. Maps of the temperature change distribution in tissue were generated for each experiment. CONCLUSIONS: This study demonstrates that velocimetric measurement on B-mode images has potential to assess temperature changes noninvasively in clinical applications.

Delta-projection imaging on contrast-enhanced ultrasound to quantify tumor microvasculature and perfusion.

RATIONALE AND OBJECTIVES: The aim of this study was to assess the Delta-projection image processing technique for visualizing tumor microvessels and for quantifying the area of tissue perfused by them on contrast-enhanced ultrasound images. MATERIALS AND METHODS: The Delta-projection algorithm was implemented to quantify perfusion by tracking the running maximum of the difference (Delta) between the contrast-enhanced ultrasound image sequence and a baseline image. Twenty-five mice with subcutaneous K1735 melanomas were first imaged with contrast-enhanced grayscale and then with minimum-exposure contrast-enhanced power Doppler (minexCPD) ultrasound. Delta-projection images were reconstructed from the grayscale images and then used to evaluate the evolution of tumor vascularity during the course of contrast enhancement. The extent of vascularity (ratio of the perfused area to the tumor area) for each tumor was determined quantitatively from Delta-projection images and compared to the extent of vascularity determined from contrast-enhanced power Doppler images. Delta-projection and minexCPD measurements were compared using linear regression analysis. RESULTS: Delta-projection was successfully performed in all 25 cases. The technique allowed the dynamic visualization of individual blood vessels as they filled in real time. Individual tumor blood vessels were distinctly visible during early image enhancement. Later, as an increasing number of blood vessels were filled with the contrast agent, clusters of vessels appeared as regions of perfusion, and the identification of individual vessels became difficult. Comparisons were made between the perfused area of tumors in Delta-projections and in minexCPD images. The Delta-projection perfusion measurements were correlated linearly with minexCPD. CONCLUSION: Delta-projection visualized tumor vessels and enabled the quantitative assessment of the tumor area perfused by the contrast agent.

Teaching medical students diagnostic sonography.

OBJECTIVE: The purpose of this pilot project was to train medical students in sonography. METHODS: Thirty-three medical students participated in a pilot sonography course, which included exposure to ultrasound physics, knobology of a compact ultrasound scanner, training in scanning and anatomy of the aorta and right kidney, and reading assignments in these areas. Pretraining and posttraining examinations were given in these areas to analyze the degree of knowledge gained by these methods. RESULTS: Nearly all of the medical students increased their basic knowledge of sonography and improved their scanning skills. The improvement was statistically significant in all areas. CONCLUSIONS: Training in sonography for medical students could be used as a foundation for later, more specialty-specific training to improve the overall medical sonography skills for all physicians.

Computer-based margin analysis of breast sonography for differentiating malignant and benign masses.

OBJECTIVE: To evaluate the role of quantitative margin features in the computer-aided diagnosis of malignant and benign solid breast masses using sonographic imaging. METHODS: Sonographic images from 56 patients with 58 biopsy-proven masses were analyzed quantitatively for the following features: margin sharpness, margin echogenicity, and angular variation in margin. Of the 58 masses, 38 were benign and 20 were malignant. Each feature was evaluated individually and in combination with the others to determine its association with malignancy. The combination of features yielding the highest association with malignancy was analyzed by logistic regression to determine the probability of malignancy. The performance of the probability measurements was evaluated by receiver operating characteristic analysis using a round-robin technique. RESULTS: Margin sharpness, margin echogenicity, and angular variation in margin were significantly different for the malignant and benign masses (P < .03, 2-tailed Student t test). According to quantitative measures, tumor-tissue margins of the malignant masses were less distinct than for the benign masses. Although the mean size of the lesions for the two groups was the same, the mean age of the patients was statistically different (P = .000625). After logistic regression analysis, the individual features age, margin sharpness, margin echogenicity, and angular variation in margin were found to be associated with the probability of malignancy (P < .03). The area under the receiver operating characteristic curve +/- SD for the 3-feature logistic regression model combining age, margin echogenicity, and angular variation of margin was 0.87 +/- 0.05. CONCLUSIONS: The proposed quantitative margin features are robust and can reliably measure margin distinctiveness. These features combined with logistic regression analysis can be useful for computer-aided diagnosis of solid breast lesions.

Multigated contrast-enhanced power Doppler to measure blood flow in mice tumors.

This study evaluated an image-gating method using contrast-enhanced power Doppler ultrasound (US) to estimate blood perfusion in mice tumors. A mathematical model that compensates for the effect of bubble destruction by US pulses was used to determine contrast flow through an image plane. Multigated power Doppler images were obtained following contrast injection. Contrast flow index (CFI) was determined by measuring the area under the color level vs. time curve for each gating frequency. CFI was compared with true flow. The method was first evaluated using a flow phantom with variable flow rates, and then verified in a mouse model with implanted tumors. Color levels in Doppler images were modulated with gating frequency due to variable destruction of microbubbles by US pulses. CFI measured from the images correlated strongly with true flow in the flow phantom (r(2) = 0.87). The proposed method yielded reproducible CFI for mice tumors, suggesting that multigated contrast-enhanced power Doppler imaging may provide noninvasive measurement of tumor perfusion in mice.