Contrast-enhanced Ultrasound Videos Research Articles

Do as Sonographers Think: Contrast-enhanced Ultrasound for Thyroid Nodules Diagnosis via Microvascular Infiltrative Awareness.

Dynamic contrast-enhanced ultrasound (CEUS) imaging can reflect the microvascular distribution and blood flow perfusion, thereby holding clinical significance in distinguishing between malignant and benign thyroid nodules. Notably, CEUS offers a meticulous visualization of the microvascular distribution surrounding the nodule, leading to an apparent increase in tumor size compared to gray-scale ultrasound (US). In the dual-image obtained, the lesion size enlarged from gray-scale US to CEUS, as the microvascular appeared to be continuously infiltrating the surrounding tissue. Although the infiltrative dilatation of microvasculature remains ambiguous, sonographers believe it may promote the diagnosis of thyroid nodules. We propose a deep learning model designed to emulate the diagnostic reasoning process employed by sonographers. This model integrates the observation of microvascular infiltration on dynamic CEUS, leveraging the additional insights provided by gray-scale US for enhanced diagnostic support. Specifically, temporal projection attention is implemented on time dimension of dynamic CEUS to represent the microvascular perfusion. Additionally, we employ a group of confidence maps with flexible Sigmoid Alpha Functions to aware and describe the infiltrative dilatation process. Moreover, a self-adaptive integration mechanism is introduced to dynamically integrate the assisted gray-scale US and the confidence maps of CEUS for individual patients, ensuring a trustworthy diagnosis of thyroid nodules. In this retrospective study, we collected a thyroid nodule dataset of 282 CEUS videos. The method achieves a superior diagnostic accuracy and sensitivity of 89.52% and 93.75%, respectively. These results suggest that imitating the diagnostic thinking of sonographers, encompassing dynamic microvascular perfusion and infiltrative expansion, proves beneficial for CEUS-based thyroid nodule diagnosis.

Applying machine-learning models to differentiate benign and malignant thyroid nodules classified as C-TIRADS 4 based on 2D-ultrasound combined with five contrast-enhanced ultrasound key frames.

To apply machine learning to extract radiomics features from thyroid two-dimensional ultrasound (2D-US) combined with contrast-enhanced ultrasound (CEUS) images to classify and predict benign and malignant thyroid nodules, classified according to the Chinese version of the thyroid imaging reporting and data system (C-TIRADS) as category 4. This retrospective study included 313 pathologically diagnosed thyroid nodules (203 malignant and 110 benign). Two 2D-US images and five CEUS key frames ("2nd second after the arrival time" frame, "time to peak" frame, "2nd second after peak" frame, "first-flash" frame, and "second-flash" frame) were selected to manually label the region of interest using the "Labelme" tool. A total of 7 images of each nodule and their annotates were imported into the Darwin Research Platform for radiomics analysis. The datasets were randomly split into training and test cohorts in a 9:1 ratio. Six classifiers, namely, support vector machine, logistic regression, decision tree, random forest (RF), gradient boosting decision tree and extreme gradient boosting, were used to construct and test the models. Performance was evaluated using a receiver operating characteristic curve analysis. The area under the curve (AUC), sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy (ACC), and F1-score were calculated. One junior radiologist and one senior radiologist reviewed the 2D-US image and CEUS videos of each nodule and made a diagnosis. We then compared their AUC and ACC with those of our best model. The AUC of the diagnosis of US, CEUS and US combined CEUS by junior radiologist and senior radiologist were 0.755, 0.750, 0.784, 0.800, 0.873, 0.890, respectively. The RF classifier performed better than the other five, with an AUC of 1 for the training cohort and 0.94 (95% confidence interval 0.88-1) for the test cohort. The sensitivity, specificity, accuracy, PPV, NPV, and F1-score of the RF model in the test cohort were 0.82, 0.93, 0.90, 0.85, 0.92, and 0.84, respectively. The RF model with 2D-US combined with CEUS key frames achieved equivalent performance as the senior radiologist (AUC: 0.94 vs. 0.92, P = 0.798; ACC: 0.90 vs. 0.92) and outperformed the junior radiologist (AUC: 0.94 vs. 0.80, P = 0.039, ACC: 0.90 vs. 0.81) in the test cohort. Our model, based on 2D-US and CEUS key frames radiomics features, had good diagnostic efficacy for thyroid nodules, which are classified as C-TIRADS 4. It shows promising potential in assisting less experienced junior radiologists.

Monitoring of neoadjuvant chemotherapy response of breast cancer with ultrasound localization microscopy

Neoadjuvant chemotherapy (NAC) is the standard treatment for high-risk breast cancer, but less than 30% of patients exhibit a complete response, necessitating better preselection and monitoring. Ultrasound localization microscopy (ULM) offers potential improvements by providing super-resolution images of vasculature and functional features. To assess its efficacy, we applied ULM to breast cancer patients undergoing NAC, comparing their responses based on ULM features. Patients were evaluated before their first, second, and fourth NAC cycles, assessing tumor volume and employing contrast-enhanced ultrasound (CEUS) in split-screen mode. CEUS videos underwent postprocessing, involving MB localization and tracking, and motion compensation. ULM images, derived from tracked MBs, generated parameter maps for analyzing vascular changes and differences between responders and non-responders. Preliminary results of 14 patients revealed that during the first three cycles of NAC, the tumor volume as well as vessel coverage (i.e., ratio of vessel to tumor surface) of responders noticeably dropped. In contrast, non-responders showed only minor tumor size reduction and overall no decrease in coverage. Furthermore, the mean distance to the closest track was noticeably higher in non-responders. These findings, though preliminary, hint at ULM's potential for enhancing preselection and monitoring of NAC patients.

A Dual-Branch Cross-Modality-Attention Network for Thyroid Nodule Diagnosis Based on Ultrasound Images and Contrast-Enhanced Ultrasound Videos.

Contrast-enhanced ultrasound (CEUS) has been extensively employed as an imaging modality in thyroid nodule diagnosis due to its capacity to visualise the distribution and circulation of micro-vessels in organs and lesions in a non-invasive manner. However, current CEUS-based thyroid nodule diagnosis methods suffered from: 1) the blurred spatial boundaries between nodules and other anatomies in CEUS videos, and 2) the insufficient representations of the local structural information of nodule tissues by the features extracted only from CEUS videos. In this paper, we propose a novel dual-branch network with a cross-modality-attention mechanism for thyroid nodule diagnosis by integrating the information from tow related modalities, i.e., CEUS videos and ultrasound image. The mechanism has two parts: US-attention-from-CEUS transformer (UAC-T) and CEUS-attention-from-US transformer (CAU-T). As such, this network imitates the manner of human radiologists by decomposing the diagnosis into two correlated tasks: 1) the spatio-temporal features extracted from CEUS are hierarchically embedded into the spatial features extracted from US with UAC-T for the nodule segmentation; 2) the US spatial features are used to guide the extraction of the CEUS spatio-temporal features with CAU-T for the nodule classification. The two tasks are intertwined in the dual-branch end-to-end network and optimized with the multi-task learning (MTL) strategy. The proposed method is evaluated on our collected thyroid US-CEUS dataset. Experimental results show that our method achieves the classification accuracy of 86.92%, specificity of 66.41%, and sensitivity of 97.01%, outperforming the state-of-the-art methods. As a general contribution in the field of multi-modality diagnosis of diseases, the proposed method has provided an effective way to combine static information with its related dynamic information, improving the quality of deep learning based diagnosis with an additional benefit of explainability.

A multimodal breast cancer diagnosis method based on Knowledge-Augmented Deep Learning

Breast cancer is a worldwide medical challenge that requires Early diagnosis. While there are numerous diagnostic methods for breast cancer, many primarily focus on network structure, neglecting the guidance of professional medical knowledge. Moreover, these methods limit their analysis to 2-dimensional B-mode ultrasound images and rarely consider the potential insights provided by Contrast-Enhanced Ultrasound (CEUS) videos, which offer more detailed dynamic pathological information. Therefore, how to effectively utilize prior medical knowledge to achieve a precise diagnosis of breast cancer based on CEUS videos has emerged as a pressing issue. To address this challenge, we propose a multimodal breast cancer diagnostic method based on Knowledge-Augmented Deep Learning named KAMnet. This method integrates three types of prior knowledge into deep neural networks through different integration strategies. First, we devise a temporal segment selection strategy guided by Gaussian sampling through data-level integration, guiding the model to focus on keyframes. Second, we construct a feature fusion network for architecture-level integration and achieve collaborative inference through decision-level integration, facilitating multimodal information exchange. Finally, a spatial attention-guided loss function through training-level integration helps the model target lesion regions. We validate our model on our breast cancer video dataset consisting of 332 cases. The result shows that our model can achieve a sensitivity of 90.91% and an accuracy of 88.238%. Extensive ablation experiments demonstrate the effectiveness of our knowledge enhancement modules. The code is released at https://github.com/tobenan/BCCAD_torch.

Application Value of Ultrasound Elastography Combined With Contrast-Enhanced Ultrasound (CEUS) Quantitative Analysis in Differentiation of Nodular Fibrocystic Changes of the Breast From Invasive Ductal Carcinoma.

This study aimed to compare the value of ultrasound elastography combined with contrast-enhanced ultrasound (CEUS) quantitative analysis in the differentiation of nodular fibrocystic breast change (FBC) from breast invasive ductal carcinoma (BIDC). We selected 50 patients each with nodular FBC and BIDC, who were admitted to the Affiliated Hospital of Zunyi Medical University from January 2018 to December 2021. Their ultrasonic elastic images and CEUS videos were collected, their ultrasound elastography scores and the ratio of strain rate (SR) of the lesions were determined, and the exported DICOM format videos of CEUS were quantitatively analyzed using VueBox software to obtain quantitative perfusion parameters. The differences between the ultrasound elastography score and SR while comparing nodular FBC and BIDC cases were statistically significant (p < .05). The sensitivity, specificity, and accuracy of ultrasound elastography scores in the differential diagnoses of nodular FBC and BIDC were 74%, 88%, and 81%, respectively. Additionally, the sensitivity, specificity, and accuracy of SR in the differential diagnosis of nodular FBC and BIDC were 94%, 78%, and 86%, respectively. Statistically significant differences were observed in the CEUS quantitative perfusion parameters PE, AUC (WiAUC, WoAUC, WiWoAUC), and WiPI in both nodular FBC and BIDC according to the VueBox software (p < .05). The sensitivity, specificity, and accuracy of CEUS quantitative analysis in the differential diagnoses of nodular FBC and BIDC were 66%, 82%, and 74%, respectively. Using the pathological findings as the gold standard, ROC curves were established, and the area under the curve (AUC) of the CEUS quantitative analysis, elasticity score, SR, and ultrasound elastography combined with CEUS quantitative analysis were 0.731, 0.838, and 0.892, as well as 0.945, respectively. Ultrasound elasticity scoring, SR and CEUS quantitative analysis have certain application value for differentiating nodular FBC cases from BIDC; however, ultrasound elasticity imaging combined with CEUS quantitative analysis can help in improving the differential diagnostic efficacy of nodular FBC cases from BIDC.

Intraoperative contrast-enhanced ultrasound evaluates the hemodynamic change of cerebral revascularization in adult moyamoya disease: Preliminary results

PurposeThis study aimed to determine the feasibility of using intraoperative contrast-enhanced ultrasound (CEUS) to assess subcortical hemodynamic changes in moyamoya disease patients who had undergone combined revascularization. It also aimed to investigate the correlation between quantitative CEUS parameters and post-operative neural functional recovery. MethodsThis study included seventeen patients diagnosed with moyamoya disease, confirmed through cerebral angiography, who underwent combined revascularization. Intraoperative subcortical hemodynamics were assessed using CEUS before and immediately after cerebrovascular bypass. Hemodynamic parameters were calculated by analyzing the time-intensity curve generated from a defined region of interest in each CEUS video. Post-operative neurological symptoms, computed tomography records, and short-term modified Rankin Scale (mRS) scores were also recorded. ResultsDuring the post-operative follow-up, 29.4 % of the patients experienced new neurological symptoms, including epilepsy, dyskinesia, and language impairment. Overall, CEUS quantitative parameters suggested that blood supply improved after surgery. And certain hemodynamic parameters exhibited a stronger correlation with the mRS scores, with correlation coefficients ranging up to 0.73 (p < 0.01). However, no statistically significant differences were observed among the different functional groups. The advanced parameter of wash-out intensity (δ WOI), which signifies the rate of change at the point when blood flow is completely washed out, demonstrated a significant difference between two functional groups (p = 0.02) and exhibited a correlation with mRS scores (correlation coefficients: 0.55, p = 0.02). ConclusionsThis preliminary study suggests that quantitative intraoperative CEUS can measure subcortical hemodynamic changes. It provides a novel non-invasive method that may predict the short-term post-operative neural functional recovery of combined revascularization in moyamoya disease patients.

Dynamic assessment of brain perfusion in a middle cerebral artery occlusion rat model by contrast-enhanced ultrasound imaging: a pilot study.

The middle cerebral artery occlusion model (MCAo) is a commonly used animal model for cerebral ischemia studies but lacks accessible imaging techniques for the assessment of hemodynamic changes of the model. The study aims to explore the value of contrast-enhanced ultrasound (CEUS) in evaluating brain perfusion in the early stages after MCAo surgery. In total, 18 adult male Sprague-Dawley rats were subjected to right MCAo using an intraluminal filament model, and CEUS was performed at the three following timepoints: before (T0), immediately after (T1), and 6 h after permanent MCAo (T2). Twelve rats successfully completed the study, and their brains were removed and stained using 2, 3, 5-triphenyltetrazolium chloride (TTC). CEUS video images were visualized offline, and the time-intensity curves (TICs) were analyzed. Different cerebrovascular patterns and manifestations of the contrast enhancement in rat ischemic hemispheres were observed. Semi-quantitative parameters of TICs in ischemic areas (ROIi) and the surrounding normal- or hypo-perfused areas (ROIn) were calculated and compared between T0, T1, and T2, and also between ROIi and ROIn. A significant correlation was found between the lesion volume (%) determined by TTC and CEUS parameters (r = -0.691, P = 0.013 for peak intensity; r = -0.742, P = 0.006 for area under the curve) at T2. After the same occlusion, there were differences in contrast perfusion in each group. This study suggests that CEUS could be an effective imaging tool for studying cerebral ischemia and perfusion in small animals as long as the transcranial acoustic window allows it.

A Two-stage Diagnostic Framework for Post-ablation Treatment Response Assessment in Patients with Hepatocellular Carcinoma.

Contrast-enhanced ultrasound (CEUS) video plays an important role in post-ablation treatment response assessment in patients with hepatocellular carcinoma (HCC). However, the assessment of treatment response using CEUS video is challenging due to issues such as high inter-frame data repeatability, small ablation area and poor imaging quality of CEUS video. To address these issues, we propose a two-stage diagnostic framework for post-ablation treatment response assessment in patients with HCC using CEUS video. The first stage is a location stage, which is used to locate the ablation area. At this stage, we propose a Yolov5-SFT to improve the location results of the ablation area and a similarity comparison module (SCM) to reduce data repeatability. The second stage is an assessment stage, which is used for the evaluation of postoperative efficacy. At this stage, we design an EfficientNet-SK to improve assessment accuracy. The Experimental results on the self-collected data show that the proposed framework outperforms other selected algorithms, and can effectively assist doctors in the assessment of post-ablation treatment response.

Quantitative analysis using intraoperative contrast-enhanced ultrasound in adult-type diffuse gliomas with isocitrate dehydrogenase mutations: association between hemodynamics and molecular features.

The relationship between contrast-enhanced ultrasound (CEUS) hemodynamics and the molecular biomarkers of adult-type diffuse gliomas, particularly isocitrate dehydrogenase (IDH), remains unclear. This study was conducted to provide a comprehensive description of the vascularization of adult-type diffuse gliomas using quantitative indicators. Additionally, it was designed to identify any variables with the potential to intraoperatively predict IDH mutation status. This prospective study enrolled patients with adult-type diffuse gliomas between November 2021 and September 2022. Intraoperative CEUS was performed, and CEUS videos were recorded for 90-second periods. Hemodynamic parameters, including the peak enhancement (PE) difference, were calculated based on the time-intensity curve of the region of interest. A differential analysis was performed on the CEUS parameters with respect to molecular biomarkers and grades. Receiver operating characteristic curves for various parameters were analyzed to evaluate the ability of those parameters to predict IDH mutation status. Sixty patients with adult-type diffuse gliomas were evaluated. All hemodynamic parameters, apart from rising time, demonstrated significant differences between IDH-mutant and IDH-wildtype adult-type diffuse gliomas. The PE difference emerged as the optimal indicator for differentiating between IDH-wildtype and IDH-mutant gliomas, with an area under the curve of 0.958 (95% confidence interval, 0.406 to 0.785). Additionally, the hemodynamic parameters revealed significant differences across both grades and types of adult-type diffuse gliomas. Hemodynamic parameters can be used intraoperatively to effectively distinguish between IDHwildtype and IDH-mutant adult-type diffuse gliomas. Additionally, quantitative CEUS equips neurosurgeons with dynamic perfusion information for various types and grades of adult-type diffuse gliomas.

Domain knowledge-guided adversarial adaptive fusion of hybrid breast ultrasound data

Contrast-enhanced ultrasound (CEUS), which provides more detailed microvascular information about the tumor, is always taken by radiologists in clinic diagnosis along with B-mode ultrasound (B-mode US). However, automatically analyzing breast CEUS is challenging due to the difference between the CEUS video and the natural video, e.g., sports or action videos, where the CEUS video has no positional displacements. Additionally, most existing methods rarely use the Time Intensity Curve (TIC) information of CEUS and non-imaging clinical (NIC) data. To address these issues, we propose a novel breast cancer diagnosis framework that learns the complementarity and correlation across hybrid modal data, including CEUS, B-mode US, and NIC data, by an adversarial adaptive fusion method. Furthermore, to fully exploit the CEUS information, the proposed method, inspired by the clinical processing of radiologists, first extracts the TIC parameters of CEUS. Then, we select a clip from CEUS using a frame screening strategy and finally get spatio-temporal features from these clips through a critical frame attention network. To our knowledge, this is the first AI system to use TIC parameters, NIC data, and ultrasound imaging in diagnoses. We have validated our method on a dataset collected from 554 patients. The experimental results demonstrate the excellent performance of the proposed method. The result shows that our method can achieve an accuracy of 87.73%, which is higher than that of uni-modal approaches by nearly 5%.

BP-Net: Boundary and perfusion feature guided dual-modality ultrasound video analysis network for fibrous cap integrity assessment.

Ultrasonography is one of the main imaging methods for monitoring and diagnosing atherosclerosis due to its non-invasiveness and low-cost. Automatic differentiation of carotid plaque fibrous cap integrity by using multi-modal ultrasound videos has significant diagnostic and prognostic value for cardiovascular and cerebrovascular disease patients. However, the task faces several challenges, including high variation in plaque location and shape, the absence of analysis mechanism focusing on fibrous cap, the lack of effective mechanism to capture the relevance among multi-modal data for feature fusion and selection, etc. To overcome these challenges, we propose a new target boundary and perfusion feature guided video analysis network (BP-Net) based on conventional B-mode ultrasound and contrast-enhanced ultrasound videos for assessing the integrity of fibrous cap. Based on our previously proposed plaque auto-tracking network, in our BP-Net, we further introduce the plaque edge attention module and reverse mechanism to focus the dual video analysis on the fiber cap of plaques. Moreover, to fully explore the rich information on the fibrous cap and inside/outside of the plaque, we propose a feature fusion module for B-mode and contrast video to filter out the most valuable features for fibrous cap integrity assessment. Finally, multi-head convolution attention is proposed and embedded into transformer-based network, which captures semantic features and global context information to obtain accurate evaluation of fibrous caps integrity. The experimental results demonstrate that the proposed method has high accuracy and generalizability with an accuracy of 92.35% and an AUC of 0.935, which outperforms than the state-of-the-art deep learning based methods. A series of comprehensive ablation studies suggest the effectiveness of each proposed component and show great potential in clinical application.

Predicting the Efficacy of Neoadjuvant Chemotherapy for Pancreatic Cancer Using Deep Learning of Contrast-Enhanced Ultrasound Videos.

Contrast-enhanced ultrasound (CEUS) is a promising imaging modality in predicting the efficacy of neoadjuvant chemotherapy for pancreatic cancer, a tumor with high mortality. In this study, we proposed a deep-learning-based strategy for analyzing CEUS videos to predict the prognosis of pancreatic cancer neoadjuvant chemotherapy. Pre-trained convolutional neural network (CNN) models were used for binary classification of the chemotherapy as effective or ineffective, with CEUS videos collected before chemotherapy as the model input, and with the efficacy after chemotherapy as the reference standard. We proposed two deep learning models. The first CNN model used videos of ultrasound (US) and CEUS (US+CEUS), while the second CNN model only used videos of selected regions of interest (ROIs) within CEUS (CEUS-ROI). A total of 38 patients with strict restriction of clinical factors were enrolled, with 76 original CEUS videos collected. After data augmentation, 760 and 720 videos were included for the two CNN models, respectively. Seventy-six-fold and 72-fold cross-validations were performed to validate the classification performance of the two CNN models. The areas under the curve were 0.892 and 0.908 for the two models. The accuracy, recall, precision and F1 score were 0.829, 0.759, 0.786, and 0.772 for the first model. Those were 0.864, 0.930, 0.866, and 0.897 for the second model. A total of 38.2% and 40.3% of the original videos could be clearly distinguished by the deep learning models when the naked eye made an inaccurate classification. This study is the first to demonstrate the feasibility and potential of deep learning models based on pre-chemotherapy CEUS videos in predicting the efficacy of neoadjuvant chemotherapy for pancreas cancer.

Long-term liver lesion tracking in contrast-enhanced ultrasound videos via a siamese network with temporal motion attention.

Propose: Contrast-enhanced ultrasound has shown great promises for diagnosis and monitoring in a wide range of clinical conditions. Meanwhile, to obtain accurate and effective location of lesion in contrast-enhanced ultrasound videos is the basis for subsequent diagnosis and qualitative treatment, which is a challenging task nowadays. Methods: We propose to upgrade a siamese architecture-based neural network for robust and accurate landmark tracking in contrast-enhanced ultrasound videos. Due to few researches on it, the general inherent assumptions of the constant position model and the missing motion model remain unaddressed limitations. In our proposed model, we overcome these limitations by introducing two modules into the original architecture. We use a temporal motion attention based on Lucas Kanade optic flow and Karman filter to model the regular movement and better instruct location prediction. Moreover, we design a pipeline of template update to ensure timely adaptation to feature changes. Results: Eventually, the whole framework was performed on our collected datasets. It has achieved the average mean IoU values of 86.43% on 33 labeled videos with a total of 37,549 frames. In terms of tracking stability, our model has smaller TE of 19.2 pixels and RMSE of 27.6 with the FPS of 8.36 ± 3.23 compared to other classical tracking models. Conclusion: We designed and implemented a pipeline for tracking focal areas in contrast-enhanced ultrasound videos, which takes the siamese network as the backbone and uses optical flow and Kalman filter algorithm to provide position prior information. It turns out that these two additional modules are helpful for the analysis of CEUS videos. We hope that our work can provide an idea for the analysis of CEUS videos.

Conventional ultrasound and contrast-enhanced ultrasound radiomics in breast cancer and molecular subtype diagnosis.

This study aimed to explore the value of conventional ultrasound (CUS) and contrast-enhanced ultrasound (CEUS) radiomics to diagnose breast cancer and predict its molecular subtype. A total of 170 lesions (121 malignant, 49 benign) were selected from March 2019 to January 2022. Malignant lesions were further divided into six categories of molecular subtype: (non-)Luminal A, (non-)Luminal B, (non-)human epidermal growth factor receptor 2 (HER2) overexpression, (non-)triple-negative breast cancer (TNBC), hormone receptor (HR) positivity/negativity, and HER2 positivity/negativity. Participants were examined using CUS and CEUS before surgery. Regions of interest images were manually segmented. The pyradiomics toolkit and the maximum relevance minimum redundancy algorithm were utilized to extract and select features, multivariate logistic regression models of CUS, CEUS, and CUS combined with CEUS radiomics were then constructed and evaluated by fivefold cross-validation. The accuracy of the CUS combined with CEUS model was superior to CUS model (85.4% vs. 81.3%, p<0.01). The accuracy of the CUS radiomics model in predicting the six categories of breast cancer is 68.2% (82/120), 69.3% (83/120), 83.7% (100/120), 86.7% (104/120), 73.5% (88/120), and 70.8% (85/120), respectively. In predicting breast cancer of Luminal A, HER2 overexpression, HR-positivity, and HER2 positivity, CEUS video improved the predictive performance of CUS radiomics model [accuracy=70.2% (84/120), 84.0% (101/120), 74.5% (89/120), and 72.5% (87/120), p<0.01]. CUS radiomics has the potential to diagnose breast cancer and predict its molecular subtype. Moreover, CEUS video has auxiliary predictive value for CUS radiomics.

An Automated Method for Classifying Liver Lesions in Contrast-Enhanced Ultrasound Imaging Based on Deep Learning Algorithms.

Contrast-enhanced ultrasound (CEUS) is an important imaging modality in the diagnosis of liver tumors. By using contrast agent, a more detailed image is obtained. Time-intensity curves (TIC) can be extracted using a specialized software, and then the signal can be analyzed for further investigations. The purpose of the study was to build an automated method for extracting TICs and classifying liver lesions in CEUS liver investigations. The cohort contained 50 anonymized video investigations from 49 patients. Besides the CEUS investigations, clinical data from the patients were provided. A method comprising three modules was proposed. The first module, a lesion segmentation deep learning (DL) model, handled the prediction of masks frame-by-frame (region of interest). The second module performed dilation on the mask, and after applying colormap to the image, it extracted the TIC and the parameters from the TIC (area under the curve, time to peak, mean transit time, and maximum intensity). The third module, a feed-forward neural network, predicted the final diagnosis. It was trained on the TIC parameters extracted by the second model, together with other data: gender, age, hepatitis history, and cirrhosis history. For the feed-forward classifier, five classes were chosen: hepatocarcinoma, metastasis, other malignant lesions, hemangioma, and other benign lesions. Being a multiclass classifier, appropriate performance metrics were observed: categorical accuracy, F1 micro, F1 macro, and Matthews correlation coefficient. The results showed that due to class imbalance, in some cases, the classifier was not able to predict with high accuracy a specific lesion from the minority classes. However, on the majority classes, the classifier can predict the lesion type with high accuracy. The main goal of the study was to develop an automated method of classifying liver lesions in CEUS video investigations. Being modular, the system can be a useful tool for gastroenterologists or medical students: either as a second opinion system or a tool to automatically extract TICs.

Ultrasound-based radiomics analysis for differentiating benign and malignant breast lesions: From static images to CEUS video analysis.

BackgroundContinuous contrast-enhanced ultrasound (CEUS) video is a challenging direction for radiomics research. We aimed to evaluate machine learning (ML) approaches with radiomics combined with the XGBoost model and a convolutional neural network (CNN) for discriminating between benign and malignant lesions in CEUS videos with a duration of more than 1 min.MethodsWe gathered breast CEUS videos of 109 benign and 81 malignant tumors from two centers. Radiomics combined with the XGBoost model and a CNN was used to classify the breast lesions on the CEUS videos. The lesions were manually segmented by one radiologist. Radiomics combined with the XGBoost model was conducted with a variety of data sampling methods. The CNN used pretrained 3D residual network (ResNet) models with 18, 34, 50, and 101 layers. The machine interpretations were compared with prospective interpretations by two radiologists. Breast biopsies or pathological examinations were used as the reference standard. Areas under the receiver operating curves (AUCs) were used to compare the diagnostic performance of the models.ResultsThe CNN model achieved the best AUC of 0.84 on the test cohort with the 3D-ResNet-50 model. The radiomics model obtained AUCs between 0.65 and 0.75. Radiologists 1 and 2 had AUCs of 0.75 and 0.70, respectively.ConclusionsThe 3D-ResNet-50 model was superior to the radiomics combined with the XGBoost model in classifying enhanced lesions as benign or malignant on CEUS videos. The CNN model was superior to the radiologists, and the radiomics model performance was close to the performance of the radiologists.

Multimodal ultrasound fusion network for differentiating between benign and malignant solid renal tumors.

Objective: We aim to establish a deep learning model called multimodal ultrasound fusion network (MUF-Net) based on gray-scale and contrast-enhanced ultrasound (CEUS) images for classifying benign and malignant solid renal tumors automatically and to compare the model’s performance with the assessments by radiologists with different levels of experience. Methods: A retrospective study included the CEUS videos of 181 patients with solid renal tumors (81 benign and 100 malignant tumors) from June 2012 to June 2021. A total of 9794 B-mode and CEUS-mode images were cropped from the CEUS videos. The MUF-Net was proposed to combine gray-scale and CEUS images to differentiate benign and malignant solid renal tumors. In this network, two independent branches were designed to extract features from each of the two modalities, and the features were fused using adaptive weights. Finally, the network output a classification score based on the fused features. The model’s performance was evaluated using five-fold cross-validation and compared with the assessments of the two groups of radiologists with different levels of experience. Results: For the discrimination between benign and malignant solid renal tumors, the junior radiologist group, senior radiologist group, and MUF-Net achieved accuracy of 70.6%, 75.7%, and 80.0%, sensitivity of 89.3%, 95.9%, and 80.4%, specificity of 58.7%, 62.9%, and 79.1%, and area under the receiver operating characteristic curve of 0.740 (95% confidence internal (CI): 0.70–0.75), 0.794 (95% CI: 0.72–0.83), and 0.877 (95% CI: 0.83–0.93), respectively. Conclusion: The MUF-Net model can accurately classify benign and malignant solid renal tumors and achieve better performance than senior radiologists. Key points: The CEUS video data contain the entire tumor microcirculation perfusion characteristics. The proposed MUF-Net based on B-mode and CEUS-mode images can accurately distinguish between benign and malignant solid renal tumors with an area under the receiver operating characteristic curve of 0.877, which surpasses senior radiologists’ assessments by a large margin.

New perspectives for investigating muscular perfusion response after dietary supplement intake: an exploratory, randomized, double-blind, placebo-controlled crossover trial in healthy young athletes using contrast-enhanced ultrasound (CEUS)

ABSTRACTBackgroundVarious dietary supplements have been reported to enhance muscular perfusion in athletes practicing resistance training, especially through modulation of nitric oxide signaling.ObjectivesThe aim of this study was therefore to investigate selected ‘NO-boosting’ supplements in a real-life setting i) to generate novel hypotheses and perfusion estimates for power calculation in view of a definitive trial and ii) to assess the feasibility of the study design with particular focus on the use of contrast-enhanced ultrasound (CEUS) for perfusion quantification.MethodsThirty young male athletes (24 ± 4 years) regularly practicing resistance training were enrolled in this three-arm, placebo(PL)-controlled crossover trial with ingestion of two commercially available supplements: an amino acid combination (AA) (containing 3 g of L-arginine-hydrochloride and 8 g of L-citrulline-malate) and 300 mg of a specific green tea extract (GTE). After intake, CEUS examinations of the dominant biceps brachii muscle were performed under resting conditions and following standardized resistance exercising. Quantitative parameters of biceps perfusion (peak enhancement, PE; wash-in perfusion index, WiPI) and caliber were derived from corresponding CEUS video files. Additionally, subjective muscle pump was determined after exercise.ResultsFor PE, WiPI, and biceps caliber, the standard deviation (SD) of the within-subject differences between PL, AA, and GTE was determined, thereby allowing future sample size calculations. No significant differences between PL, AA, and GTE were observed for biceps perfusion, caliber, or muscle pump. When comparing resting with post-exercise measurements, the increase in biceps perfusion significantly correlated with the caliber increase (PE: r = 0.266, p = 0.0113; WiPI: r = 0.269, p = 0.0105). Similarly, the biceps perfusion correlated with muscle pump in the post-exercise conditions (PE: r = 0.354, p = 0.0006; WiPI: r = 0.350, p = 0.0007). A high participant adherence was achieved, and the acquisition of good quality CEUS video files was feasible. No adverse events occurred.ConclusionBased on our novel examination protocol, CEUS seems to be feasible following higher-load resistance exercising and may be used as a new method for high-resolution perfusion quantification to investigate the effects of pre-exercise dietary supplementation on muscle perfusion and related muscle size dynamics.

Deep Learning of Liver Contrast-Enhanced Ultrasound to Predict Microvascular Invasion and Prognosis in Hepatocellular Carcinoma.

Background and AimsMicrovascular invasion (MVI) is a well-known risk factor for poor prognosis in hepatocellular carcinoma (HCC). This study aimed to develop a deep convolutional neural network (DCNN) model based on contrast-enhanced ultrasound (CEUS) to predict MVI, and thus to predict prognosis in patients with HCC.MethodsA total of 436 patients with surgically resected HCC who underwent preoperative CEUS were retrospectively enrolled. Patients were divided into training (n = 301), validation (n = 102), and test (n = 33) sets. A clinical model (Clinical model), a CEUS video-based DCNN model (CEUS-DCNN model), and a fusion model based on CEUS video and clinical variables (CECL-DCNN model) were built to predict MVI. Survival analysis was used to evaluate the clinical performance of the predicted MVI.ResultsCompared with the Clinical model, the CEUS-DCNN model exhibited similar sensitivity, but higher specificity (71.4% vs. 38.1%, p = 0.03) in the test group. The CECL-DCNN model showed significantly higher specificity (81.0% vs. 38.1%, p = 0.005) and accuracy (78.8% vs. 51.5%, p = 0.009) than the Clinical model, with an AUC of 0.865. The Clinical predicted MVI could not significantly distinguish OS or RFS (both p > 0.05), while the CEUS-DCNN predicted MVI could only predict the earlier recurrence (hazard ratio [HR] with 95% confidence interval [CI 2.92 [1.1–7.75], p = 0.024). However, the CECL-DCNN predicted MVI was a significant prognostic factor for both OS (HR with 95% CI: 6.03 [1.7–21.39], p = 0.009) and RFS (HR with 95% CI: 3.3 [1.23–8.91], p = 0.011) in the test group.ConclusionsThe proposed CECL-DCNN model based on preoperative CEUS video can serve as a noninvasive tool to predict MVI status in HCC, thereby predicting poor prognosis.