Interoperator variability in the reproducibility of breast lesions found by handheld ultrasound (HHUS) can significantly interfere with clinical care. This study analyzed the features associated with breast mass position differences during HHUS. The ability of operators to reproduce the position of small masses and the time required to generate annotations with and without a computer-assisted scanning device (DEVICE) were also evaluated. This prospective study included 28 patients with 34 benign or probably benign small breast masses. Two operators generated manual and automated position annotations for each mass. The probe and body positions were systematically varied during scanning with the DEVICE, and the features describing mass movement were used in three logistic regression models trained to discriminate small from large breast mass displacements (cutoff: 10 mm). All models successfully discriminated small from large breast mass displacements (areas under the curve: 0.78 to 0.82). The interoperator localization precision was 6.6 ± 2.8 mm with DEVICE guidance and 19.9 ± 16.1 mm with manual annotations. Computer-assisted scanning reduced the time to annotate and reidentify a mass by 33 and 46 s on average, respectively. The results demonstrated that breast mass location reproducibility and exam efficiency improved by controlling operator actionable features with computer-assisted HHUS.

UNASSIGNED: To compare the medical image interpretation\'s time between the conventional and automated methods of breast ultrasound in patients with breast lesions. Secondarily, to evaluate the agreement between the two methods and interobservers.
UNASSIGNED: This is a cross-sectional study with prospective data collection. The agreement\'s degrees were established in relation to the breast lesions\'s ultrasound descriptors. To determine the accuracy of each method, a biopsy of suspicious lesions was performed, considering the histopathological result as the diagnostic gold standard.
UNASSIGNED: We evaluated 27 women. Conventional ultrasound used an average medical time of 10.77 minutes (± 2.55) greater than the average of 7.38 minutes (± 2.06) for automated ultrasound (p<0.001). The degrees of agreement between the methods ranged from 0.75 to 0.95 for researcher 1 and from 0.71 to 0.98 for researcher 2. Among the researchers, the degrees of agreement were between 0.63 and 1 for automated ultrasound and between 0.68 and 1 for conventional ultrasound. The area of the ROC curve for the conventional method was 0.67 (p=0.003) for researcher 1 and 0.72 (p<0.001) for researcher 2. The area of the ROC curve for the automated method was 0. 69 (p=0.001) for researcher 1 and 0.78 (p<0.001) for researcher 2.
UNASSIGNED: We observed less time devoted by the physician to automated ultrasound compared to conventional ultrasound, maintaining accuracy. There was substantial or strong to perfect interobserver agreement and substantial or strong to almost perfect agreement between the methods.

Several new ultrasound tools have been developed to further evaluate breast lesions detected on B-mode ultrasound. Strain elastography (SRE) was developed to assess the likelihood of malignancy of lesions based on their stiffness. This has been incorporated into the latest edition of the American College of Radiology (ACR) Breast Imaging Reporting and Data System (BI-RADS) lexicon and atlas. However, no agreed cut-off stiffness values have been established to distinguish benign from malignant lesions making the translation into routine clinical practice difficult. Superb microvascular imaging (SMI) was developed to better evaluate the vascularity within sonographic lesions and assess their likelihood of malignancy. However, there is also no agreed cut-off value for vascular index (VI) to distinguish between benign and malignant lesions. MicroPure was developed to better visualize and evaluate calcifications seen on ultrasound. Its effective use in breast screening and evaluating the calcifications detected for likelihood of malignancy have not been established. This article describes the original intended uses of these applications and reviews the studies evaluating them, showing the varying success of the translation of these tools into routine clinical practice. Also described are some other uses of these tools for which they were not originally intended. This illustrates the importance of being perceptive to alternative uses of imaging tools in their translation from bench to bedside.

Background  Radial breast ultrasound scanning (r-US) and commonly used meander-like ultrasound scanning (m-US) have recently been shown to be equally sensitive and specific with regard to the detection of breast malignancies. As patient satisfaction has a strong influence on patient compliance and thus on the quality of health care, we compare here the two US scanning techniques with regard to patient comfort during breast ultrasound (BUS) and analyze whether the patient has a preference for either scanning technique. Materials and Methods  Symptomatic and asymptomatic women underwent both m-US and r-US scanning by two different examiners. Patient comfort and preference were assessed using a visual analog scale-based (VAS) questionnaire and were compared using a Mann-Whitney U test. Results  Analysis of 422 VAS-based questionnaires showed that perceived comfort with r-US (r-VAS 8 cm, IQR [5.3, 9.1]) was significantly higher compared to m-US (m-VAS 5.6 cm, IQR [5.2, 7.4]) (p < 0.001). 53.8% of patients had no preference, 44.3% of patients clearly preferred r-US, whereas only 1.9% of patients preferred m-US. Conclusion: Patients experience a higher level of comfort with r-US and favor r-US over m-US. As the diagnostic accuracy of r-US has been shown to be comparable to that of m-US and the time required for examination is shorter, a switch from m-US to r-US in routine clinical practice might be beneficial. R-US offers considerable potential to positively affect patient compliance but also to save examination time and thus costs.

Vascular tumors of the breast are rare, but benign hemangiomas are the most common type. Capillary hemangiomas are a subset of benign vascular tumors that involve smaller vessel sizes. They are difficult to diagnose with mammography and ultrasound, as they lack pathognomonic features and are frequently not seen. MRI is the most sensitive imaging tool. The lesions appear similar to angiosarcoma or ductal carcinoma in situ on imaging, which further complicates the diagnosis. A biopsy of the lesions is required for a definitive diagnosis. In this report, a 49-year-old female with newly diagnosed breast cancer is incidentally found to have a capillary hemangioma on staging breast MRI that was confirmed with a biopsy and excised along with the primary breast cancer with a partial mastectomy. The imaging findings of breast hemangioma on mammography, ultrasound, and MRI are also reviewed and described in this report.

UNASSIGNED: Accurate classification of breast nodules into benign and malignant types is critical for the successful treatment of breast cancer. Traditional methods rely on subjective interpretation, which can potentially lead to diagnostic errors. Artificial intelligence (AI)-based methods using the quantitative morphological analysis of ultrasound images have been explored for the automated and reliable classification of breast cancer. This study aimed to investigate the effectiveness of AI-based approaches for improving diagnostic accuracy and patient outcomes.
UNASSIGNED: In this study, a quantitative analysis approach was adopted, with a focus on five critical features for evaluation: degree of boundary regularity, clarity of boundaries, echo intensity, and uniformity of echoes. Furthermore, the classification results were assessed using five machine learning methods: logistic regression (LR), support vector machine (SVM), decision tree (DT), naive Bayes, and K-nearest neighbor (KNN). Based on these assessments, a multifeature combined prediction model was established.
UNASSIGNED: We evaluated the performance of our classification model by quantifying various features of the ultrasound images and using the area under the receiver operating characteristic (ROC) curve (AUC). The moment of inertia achieved an AUC value of 0.793, while the variance and mean of breast nodule areas achieved AUC values of 0.725 and 0.772, respectively. The convexity and concavity achieved AUC values of 0.988 and 0.987, respectively. Additionally, we conducted a joint analysis of multiple features after normalization, achieving a recall value of 0.98, which surpasses most medical evaluation indexes on the market. To ensure experimental rigor, we conducted cross-validation experiments, which yielded no significant differences among the classifiers under 5-, 8-, and 10-fold cross-validation (P>0.05).
UNASSIGNED: The quantitative analysis can accurately differentiate between benign and malignant breast nodules.

Capturing global contextual information plays a critical role in breast ultrasound (BUS) image classification. Although convolutional neural networks (CNNs) have demonstrated reliable performance in tumor classification, they have inherent limitations for modeling global and long-range dependencies due to the localized nature of convolution operations. Vision Transformers have an improved capability of capturing global contextual information but may distort the local image patterns due to the tokenization operations. In this study, we proposed a hybrid multitask deep neural network called Hybrid-MT-ESTAN, designed to perform BUS tumor classification and segmentation using a hybrid architecture composed of CNNs and Swin Transformer components. The proposed approach was compared to nine BUS classification methods and evaluated using seven quantitative metrics on a dataset of 3,320 BUS images. The results indicate that Hybrid-MT-ESTAN achieved the highest accuracy, sensitivity, and F1 score of 82.7%, 86.4%, and 86.0%, respectively.

Despite recent medical advancements, breast cancer remains one of the most prevalent and deadly diseases among women. Although machine learning-based Computer-Aided Diagnosis (CAD) systems have shown potential to assist radiologists in analyzing medical images, the opaque nature of the best-performing CAD systems has raised concerns about their trustworthiness and interpretability. This paper proposes MT-BI-RADS, a novel explainable deep learning approach for tumor detection in Breast Ultrasound (BUS) images. The approach offers three levels of explanations to enable radiologists to comprehend the decision-making process in predicting tumor malignancy. Firstly, the proposed model outputs the BI-RADS categories used for BUS image analysis by radiologists. Secondly, the model employs multitask learning to concurrently segment regions in images that correspond to tumors. Thirdly, the proposed approach outputs quantified contributions of each BI-RADS descriptor toward predicting the benign or malignant class using post-hoc explanations with Shapley Values.

Breast cancer, affecting both genders, but mostly females, exhibits shifting demographic patterns, with an increasing incidence in younger age groups. Early identification through mammography, clinical examinations, and breast self-exams enhances treatment efficacy, but challenges persist in low- and medium-income countries due to limited imaging resources. This review assesses the feasibility of employing breast ultrasound as the primary breast cancer screening method, particularly in resource-constrained regions. Following the PRISMA guidelines, this study examines 52 publications from the last five years. Breast ultrasound, distinct from mammography, offers advantages like radiation-free imaging, suitability for repeated screenings, and preference for younger populations. Real-time imaging and dense breast tissue evaluation enhance sensitivity, accessibility, and cost-effectiveness. However, limitations include reduced specificity, operator dependence, and challenges in detecting microcalcifications. Automatic breast ultrasound (ABUS) addresses some issues but faces constraints like potential inaccuracies and limited microcalcification detection. The analysis underscores the need for a comprehensive approach to breast cancer screening, emphasizing international collaboration and addressing limitations, especially in resource-constrained settings. Despite advancements, notably with ABUS, the primary goal is to contribute insights for optimizing breast cancer screening globally, improving outcomes, and mitigating the impact of this debilitating disease.

Limited access to breast cancer diagnosis globally leads to delayed treatment. Ultrasound, an effective yet underutilized method, requires specialized training for sonographers, which hinders its widespread use. Volume sweep imaging (VSI) is an innovative approach that enables untrained operators to capture high-quality ultrasound images. Combined with deep learning, like convolutional neural networks, it can potentially transform breast cancer diagnosis, enhancing accuracy, saving time and costs, and improving patient outcomes. The widely used UNet architecture, known for medical image segmentation, has limitations, such as vanishing gradients and a lack of multi-scale feature extraction and selective region attention. In this study, we present a novel segmentation model known as Wavelet_Attention_UNet (WATUNet). In this model, we incorporate wavelet gates and attention gates between the encoder and decoder instead of a simple connection to overcome the limitations mentioned, thereby improving model performance. Two datasets are utilized for the analysis: the public \'Breast Ultrasound Images\' dataset of 780 images and a private VSI dataset of 3818 images, captured at the University of Rochester by the authors. Both datasets contained segmented lesions categorized into three types: no mass, benign mass, and malignant mass. Our segmentation results show superior performance compared to other deep networks. The proposed algorithm attained a Dice coefficient of 0.94 and an F1 score of 0.94 on the VSI dataset and scored 0.93 and 0.94 on the public dataset, respectively. Moreover, our model significantly outperformed other models in McNemar\'s test with false discovery rate correction on a 381-image VSI set. The experimental findings demonstrate that the proposed WATUNet model achieves precise segmentation of breast lesions in both standard-of-care and VSI images, surpassing state-of-the-art models. Hence, the model holds considerable promise for assisting in lesion identification, an essential step in the clinical diagnosis of breast lesions.