Sabeti, Soroosh

Introduction/Background: Peripheral arterial disease (PAD) exhibits reduced hyperemic response in the affected legs following stimuli such as pressure-cuff-induced occlusion or exercise. Therefore, an analysis of the perfusion changes in the calf muscle using a widely available imaging modality such as ultrasound could serve as a complementary tool to existing methods such as ankle-brachial index (ABI) for PAD detection. Research Questions/Hypothesis: This study evaluates the utility of ultrasound perfusion imaging and estimation technique in the calf muscle to differentiate PAD affected legs based on their flow response to external pressure and exercise. Methods/Approach: Data acquisition was performed using a research ultrasound system (Vantage NXT, Verasonics) with an ultrasound probe secured inside a probe fixation device and positioned to the calf muscle of the subject. Two separate studies were performed on each subject: 1) A flow variations response to external pressure study, 2) a flow variation response to exercise. For the pressure study, a pressure cuff was wrapped around the subject’s thigh and inflated during the study using an automatic rapid cuff inflation device. Data was continuously acquired for 6 minutes including 1 minute of baseline, 3 minutes of pressure cuff occlusion, 2 minutes of post-occlusion acquisition. The exercise study consisted of data acquisition in the seated position with 1 minute of baseline, 30 repetitions of plantar flexion using a calf training device, and 2 minutes of post-exercise acquisition. The acquired data were then clutter-filtered and denoised for perfusion visualization. Four metrics including post-occlusion/post-exercise to baseline flow intensity/density variation (POBFIV, PEBFIV, POBFDV, PEBFDV) were obtained for analysis. Results/Data: Figure 1 shows perfusion imaging results during the pressure and the exercise studies for two example legs, one healthy and one of a clinically diagnosed PAD patient (mild disease), respectively. Percentage-wise POBFIV, PEBFIV, POBFDV, and PEBFDV values were 44%, 57%, 197%, and 2231% for the healthy leg and 12%, 27%, 63%, and 181% for the PAD leg, respectively, shown in Table 1. Conclusion: The proposed method shows promise in differentiating PAD-affected legs from healthy individual’s through evaluating the hyperemic response to external pressure and exercise. Statistical analysis of the results for a cohort of healthy and PAD-affected legs will constitute future work.

Introduction: The ankle-brachial index (ABI) is the most common method for detecting peripheral arterial disease (PAD), but its sensitivity is limited, especially in patients with poorly compressible vessels. This study proposes a novel ultrasound-based approach for PAD diagnosis by analyzing calf muscle perfusion responses to pressure-cuff occlusion and exercise, interpreted using machine learning (ML) techniques. Methods: Under an IRB approved protocol and signed IRB-approved written consent, 48 participants, 43 symptomatic with abnormal ABI and 5 healthy volunteers were recruited for this study. High-frame-rate ultrasound B-mode image data (500 frames/s) were collected from 64 legs and categorized into two classes based on clinical diagnosis: Abnormal ABI (54 legs) and Healthy (10 legs). Legs with normal ABI from symptomatic patients were excluded to avoid diagnostic ambiguity, as these cases may lie within the diagnostic gray zone for PAD. Each leg underwent a 9-minute scanning protocol comprising 1 minute of baseline, 3 minutes of pressure cuff occlusion (using an automatic rapid inflation/deflation device), 2 minutes post-occlusion, 1 minute of plantar flexion exercise, and 2 minutes post-exercise. The ultrasound data were processed using singular-value decomposition (SVD) clutter filtering and noise equalization to generate Power-Doppler perfusion images at 2 frames per second. The PAD prediction pipeline involved data augmentation to address class imbalance and limited sample size, followed by histogram-based thresholding to extract blood pixels—intensity thresholding was avoided due to variability in pixel intensities. Blood pixel counts were calculated over time and used to train nine machine learning classification models (Figure 1). Results: The best-performing ML classification model achieved an accuracy of 77%, with the median accuracy across all models ranging between 70% and 72%. Notably, an ensemble model that combined Logistic Regression, Gradient Boosting, and Support Vector Machines using a majority voting strategy yielded the highest accuracy of 81% (Table 1). Conclusion: We proposed a novel ultrasound-based perfusion estimation method for diagnosing peripheral arterial disease (PAD), with data analyzed using ML approach. Although this is a feasibility study, the observed diagnostic accuracy exceeding 80% is promising. With further research and larger datasets, this approach has the potential to become a valuable tool for PAD.

Abstract Background Accurate assessment of axillary lymph nodes (ALNs) is a critical step for timely diagnosis of metastasis and proper subsequent intervention in breast cancer patients. Herein, we compare the diagnostic utility of quantitative high-definition microvasculature imaging (qHDMI), shear wave elastography (SWE) and their combination for differentiation of metastatic ALNs from reactive. Methods A total of 85 female patients with suspicious ALNs recommended for fine needle aspiration biopsy (FNAB) were included in the study, and the pathology results were used as the gold standard for labeling the status of each ALN. Three SWE metrics and ten qHDMI-derived biomarkers were used in our analyses. Additionally, age, as well as clinical ultrasound features such as nodal size and cortical thickness were included as clinical factors. The Wilcoxon rank-sum test was utilized to analyze distributional differences in biomarkers between metastatic and reactive ALNs. Multiple elastic-net logistic regression models were developed based on varying combinations of clinical, qHDMI, and SWE feature sets. A 70%/30% train/test split was adopted, and ROC curve analyses were performed to evaluate and compare classification performance. Moreover, distributional differences in qHDMI and SWE biomarkers between ALNs corresponding to breast cancer immunohistochemical subtypes luminal A and B were investigated. Results Of the total of 85 ALNs included in the analysis, 42 were metastatic. Statistically significant (p-value < 0.05) differences were observed in all but one of the qHDMI biomarkers, as well as all the SWE metrics. Test-set discrimination defined by area under ROC curve (AUC) was low for the model using only clinical features (0.62; 95% CI = [0.39,0.84]), with higher performance observed for models using SWE only (0.93; [0.82,1.00]), qHDMI only (0.97; [0.91,1.00]), qHDMI-SWE (0.97; [0.92,1.00]), and qHDMI-SWE plus clinical biomarkers (0.98; [0.94,1.00]). No statistically significant improvements were seen in the combined SWE-qHDMI and SWE-qHDMI-C classification models relative to the qHDMI-only model, although power for comparison was limited. Four qHDMI biomarkers and two SWE measures exhibited statistically significant distributions among breast cancer luminal A and B subtypes. Conclusions qHDMI classification model was able to separate metastatic from reactive ALNs with high accuracy. qHDMI, SWE, and the combined models had improved classification performance over the baseline Clinical model. qHDMI biomarkers can be valuable in determining the malignancy status of suspicious ALNs, providing helpful information regarding breast cancer prognosis.

Angiogenesis has an essential role in the de novo evolution of choroidal melanoma as well as choroidal nevus transformation into melanoma. Differentiating early-stage melanoma from nevus is of high clinical importance; thus, imaging techniques that provide objective information regarding tumor microvasculature structures could aid accurate early detection. Herein, we investigated the feasibility of quantitative high-definition microvessel imaging (qHDMI) for differentiation of choroidal tumors in humans. This new ultrasound-based technique encompasses a series of morphological filtering and vessel enhancement techniques, enabling the visualization of tumor microvessels as small as 150 microns and extracting vessel morphological features as new tumor biomarkers. Distributional differences between the malignant melanomas and benign nevi were tested on 37 patients with choroidal tumors using a non-parametric Wilcoxon rank-sum test, and statistical significance was declared for biomarkers with p-values < 0.05. The ocular oncology diagnosis was choroidal melanoma (malignant) in 21 and choroidal nevus (benign) in 15 patients. The mean thickness of benign and malignant masses was 1.70 ± 0.40 mm and 3.81 ± 2.63 mm, respectively. Six HDMI biomarkers, including number of vessel segments (p = 0.003), number of branch points (p = 0.003), vessel density (p = 0.03), maximum tortuosity (p = 0.001), microvessel fractal dimension (p = 0.002), and maximum diameter (p = 0.003) exhibited significant distributional differences between the two groups. Contrast-free HDMI provided noninvasive imaging and quantification of microvessels of choroidal tumors. The results of this pilot study indicate the potential use of qHDMI as a complementary tool for characterization of small ocular tumors and early detection of choroidal melanoma.

Axillary lymph nodes (ALNs) are generally among the first to be impacted by the metastatic spread of breast tumors. Consequently, evaluating their condition is of great importance as the removal of affected ALNs can potentially curtail the further spread of cancer cells to other parts of the body. Conventional ultrasound imaging suffers from low sensitivity and specificity and does not provide a comprehensive picture of the status of the node. In this work, we utilize an ultrasound microvessel imaging technique to visualize vascular structures within ALNs and, through several image processing steps, characterize the morphology of these structures. Several morphological metrics are used as biomarkers to differentiate between reactive and metastatic lymph nodes of patients with suspicious ALNs. A group of 68 patients is included in this study. Pathology examination results are used as the gold-standard reference for data labeling. A support vector machine (SVM) model is trained, and its performance is evaluated. Statistical analysis of the discriminative potential of individual biomarkers and receiver operating characteristic (ROC) curve analysis of the trained model are presented. Preliminary results suggest that a sensitivity of 0.88, specificity of 0.88, and area under the curve of 0.94 are achievable. Work supported by NIH-R01CA239548.

Observation of thyroid nodules in ultrasound examinations is a common occurrence. However, accurate diagnosis of the malignancy status of these nodules is a challenging task relying only on conventional ultrasound imaging. Therefore, increasing the sensitivity and specificity of non-invasive screening methods can be helpful in reducing the probability of missed diagnosis and decreasing the rate of unnecessary biopsies. In this feasibility study, a quantitative ultrasound microvasculature imaging technique is applied to ultrasound data collected from a cohort of 92 patients. This technique consists of first enabling the visualization of vascular networks inside the scanned nodules, followed by a morphometric analysis framework with a number of biomarkers to distinguish between benign and malignant thyroid nodules. Statistical tests are performed to find significant differences between the distribution of the values of the biomarkers. Subsequently, these biomarkers are utilized to train classification models that solely benefit from morphological biomarkers and those trained with additional clinical features. The receiver operating characteristic (ROC) curve analysis of these models, which helps us evaluate their predictive power, is presented. In our preliminary results, the model trained with combined morphological and clinical features achieves an AUC of 0.90, a sensitivity of 0.81, and a specificity of 0.79. [Work supported by R01CA239548.]

Abstract While being a relatively prevalent condition particularly among aging patients, peripheral arterial disease (PAD) of lower extremities commonly goes undetected or misdiagnosed due to its symptoms being nonspecific. Additionally, progression of PAD in the absence of timely intervention can lead to dire consequences. Therefore, development of non-invasive and affordable diagnostic approaches can be highly beneficial in detection and treatment planning for PAD patients. In this study, we present a contrast-free ultrasound-based quantitative blood flow imaging technique for PAD diagnosis. The method involves monitoring the variations of blood flow in the calf muscle in response to thigh-pressure-cuff-induced occlusion. Four quantitative metrics are introduced for analysis of these variations. We examine the feasibility of this method through an in vivo study consisting of 14 PAD patients with abnormal ankle-brachial index (ABI) and 8 healthy volunteers. Ultrasound data acquired from 13 legs in the patient group and 13 legs in the healthy group are analyzed. Out of the four utilized metrics, three exhibited significantly different distributions between the two groups (p-value < 0.05). The results of this feasibility study indicate the diagnostic potential of the proposed method for the detection of PAD.

Low specificity in current ultrasound modalities for thyroid cancer detection necessitates the development of new imaging modalities for optimal characterization of thyroid nodules. Herein, the quantitative biomarkers of a new high-definition microvessel imaging (HDMI) were evaluated for discrimination of benign from malignant thyroid nodules. Without the help of contrast agents, this new ultrasound-based quantitative technique utilizes processing methods including clutter filtering, denoising, vessel enhancement filtering, morphological filtering, and vessel segmentation to resolve tumor microvessels at size scales of a few hundred microns and enables the extraction of vessel morphological features as new tumor biomarkers. We evaluated quantitative HDMI on 92 patients with 92 thyroid nodules identified in ultrasound. A total of 12 biomarkers derived from vessel morphological parameters were associated with pathology results. Using the Wilcoxon rank-sum test, six of the twelve biomarkers were significantly different in distribution between the malignant and benign nodules (all p < 0.01). A support vector machine (SVM)-based classification model was trained on these six biomarkers, and the receiver operating characteristic curve (ROC) showed an area under the curve (AUC) of 0.9005 (95% CI: [0.8279,0.9732]) with sensitivity, specificity, and accuracy of 0.7778, 0.9474, and 0.8929, respectively. When additional clinical data, namely TI-RADS, age, and nodule size were added to the features, model performance reached an AUC of 0.9044 (95% CI: [0.8331,0.9757]) with sensitivity, specificity, and accuracy of 0.8750, 0.8235, and 0.8400, respectively. Our findings suggest that tumor vessel morphological features may improve the characterization of thyroid nodules.