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The CoAtNet deep neural model has been shown to achieve state-of-the-art performance by stacking convolutional and self-attention layers. In particular, the initial layers of CoAtNet apply efficient convolutions for extracting local features out of the input image and the initial fine-resolution feature maps. In turn, the final layers apply more cumbersome Transformers in order to extract global features from the coarse-resolution feature maps. The model's outcome directly depends on those final global features. This paper proposes an extension of the original CoAtNet model based on the introduction of a dual stream of convolution and self-attention blocks applied at the final layers of CoAtNet. In this way, those final layers automatically aggregate both local and global features extracted from the initial feature maps. Two dual-stream topologies have been proposed and evaluated. This Dual-Stream CoAtNet model exhibits a significant improvement on the segmentation accuracy of breast ultrasound images, thus contributing to the development of more robust tumor detection methods. [ABSTRACT FROM AUTHOR]

Breast cancer is a leading cause of mortality among women globally, necessitating precise classification of breast ultrasound images for early diagnosis and treatment. Traditional methods using CNN architectures such as VGG, ResNet, and DenseNet, though somewhat effective, often struggle with class imbalances and subtle texture variations, leading to reduced accuracy for minority classes such as malignant tumors. To address these issues, we propose a methodology that leverages EfficientNet-B7, a scalable CNN architecture, combined with advanced data augmentation techniques to enhance minority class representation and improve model robustness. Our approach involves fine-tuning EfficientNet-B7 on the BUSI dataset, implementing RandomHorizontalFlip, RandomRotation, and ColorJitter to balance the dataset and improve model robustness. The training process includes early stopping to prevent overfitting and optimize performance metrics. Additionally, we integrate Explainable AI (XAI) techniques, such as Grad-CAM, to enhance the interpretability and transparency of the model's predictions, providing visual and quantitative insights into the features and regions of ultrasound images influencing classification outcomes. Our model achieves a classification accuracy of 99.14%, significantly outperforming existing CNN-based approaches in breast ultrasound image classification. The incorporation of XAI techniques enhances our understanding of the model's decision-making process, thereby increasing its reliability and facilitating clinical adoption. This comprehensive framework offers a robust and interpretable tool for the early detection and diagnosis of breast cancer, advancing the capabilities of automated diagnostic systems and supporting clinical decision-making processes. [ABSTRACT FROM AUTHOR]

Nipple adenoma, an uncommon benign tumor, typically appears between the fourth and fifth decades of life, occasionally manifesting in accessory breasts. The embryonic mammary ridges give rise to accessory breasts along the milk line, and the lack of involution results in supernumerary breasts, including accessory nipples. This study aims to report a case of adenoma originating from an accessory breast nipple. The present study describes the case of a 23-year-old female patient who presented with right axillary discomfort and swelling. A physical examination revealed a tender, non-inflamed lump. A breast ultrasonography revealed features compatible with an accessory nipple, confirmed by excisional biopsy revealing adenoma. Throughout the 4-month follow-up period, no signs of recurrence were observed. Although rare, accessory breast tissue can harbor neoplasms, with nipple adenoma posing a diagnostic challenge often confounded with Paget's disease. Histologically, nipple adenomas exhibit diverse patterns, emphasizing the importance of a precise diagnosis. Adenoma of the accessory nipples is a rare condition that necessitates proper clinical assessment. Surgical excision remains the standard, yielding favorable outcomes. [ABSTRACT FROM AUTHOR]

Ultrasound‐based elastography techniques are increasingly available on modern ultrasound systems. The application of elastography extends the utility of ultrasound, yielding new information about the impact of disease on tissue biomechanics. By modifying the B‐mode imaging pulse, an Acoustic Radiation Force Impulse produces shear waves in selected tissue. Quantifying the speed of the resultant shear wave provides an indirect assessment of tissue stiffness, with stiffer tissue producing faster shear wave movement than softer tissue. Two main types of ultrasound shear wave elastography are currently in use. Point shear wave elastography uses one or two pulses to measure shear wave speeds over a small fixed area. Alternatively, two‐dimensional shear‐wave elastography provides a colour elastogram map over a larger area, allowing the operator to select specific regions of interest to measure shear wave speed. This article will discuss the essential physics principles, and clinical uses of two‐dimensional shear wave elastography, aiming to provide sonographers with a basic understanding of the technology and current applications. [ABSTRACT FROM AUTHOR]

AbstractBreast cancer (BC) is among the most commonly diagnosed cancers. Besides mammography, breast ultrasonography and the routinely monitored protein markers, the variations of small molecular metabolites in blood may be of great diagnostic value. This study aimed to quantify specific metabolite markers with potential application in BC detection. The study enrolled 50 participants, 25 BC patients and 25 healthy controls (CTRL). Dried blood spots (DBS) were utilized as biological media and were quantified via a simplified liquid chromatography tandem mass spectrometry (LC-MS/MS) method, used in expanded newborn screening. The targeted metabolomic analysis included 12 amino acids and 32 acylcarnitines. Statistical analysis revealed a significant variation of metabolic profiles between BC patients and CTRL. Among the 44 metabolites, 18 acylcarnitines and 10 amino acids remained significant after Bonferroni correction, showing increase or decrease and enabled classification of BC patients and CTRL. The well-established LC-MS/MS protocol could provide results within few minutes. Therefore, the combination of an easy-to-handle material-DBS and LC-MS/MS protocol could facilitate BC screening/diagnosis and in the next step applied to other cancer patients, as well. [ABSTRACT FROM AUTHOR]

Background: Although DBT is the standard initial imaging modality for women with focal breast symptoms, the importance of ultrasound has grown rapidly in the past decades. Therefore, the Breast UltraSound Trial (BUST) focused on assessing the diagnostic value of ultrasound and digital breast tomosynthesis (DBT) for the evaluation of breast symptoms by reversing the order of breast imaging; first performing ultrasound followed by DBT. This side-study of the BUST evaluates patients' perceptions of ultrasound and DBT in a reversed setting. Methods: After imaging, 1181/1276 BUST participants completed a survey consisting of open and closed questions regarding both exams (mean age 47.2, ±11.74). Additionally, a different subset of BUST participants (n = 29) participated in six focus group interviews 18–24 months after imaging to analyze their imaging experiences in depth. Results: A total of 55.3% of women reported reluctance to undergoing DBT, primarily due of pain, while the vast majority also find bilateral DBT reassuring (87.3%). Thematic analysis identified themes related to 1) imaging reluctance (pain/burden, result, and breast harm) and 2) ultrasound and DBT perceptions. Regarding the latter, the theme comfort underscores DBT as burdensome and painful, while ultrasound is largely perceived as non-burdensome. Ultrasound is also particularly valued for its interactive nature, as highlighted in the theme interaction. Perceived effectiveness reflects women's interest in bilateral breast evaluation with DBT and the visibility of lesions, while they express more uncertainty about the reliability of ultrasound. Emotional impact portrays DBT as reassuring for many women, whereas opinions on the reassurance provided by ultrasound are more diverse. Additional themes include costs, protocols and privacy. Conclusions: Ultrasound is highly tolerated, and particularly valued is the interaction with the radiologist. Nearly half of women express reluctance towards DBT; nevertheless, a large portion report feeling more confident after undergoing bilateral DBT, reassuring them of the absence of abnormalities. Understanding patients' perceptions of breast imaging examinations is of great value when optimizing diagnostic pathways. [ABSTRACT FROM AUTHOR]

Tumor detection in automated breast ultrasound (ABUS) images is critical for computer-aided diagnosis. A single deep learning based network is prone to make overconfident prediction. In this paper, a DML-YOLOX model based on deep mutual learning (DML) is proposed. To alleviate the overconfidence of a single network, the dual-model collaborative training strategy is designed based on YOLOX baseline. To implement the interaction between the dual models, the exploration loss is developed and combined with the consistency loss for supervision. The exploration loss aims at encouraging the models to explore and learn different feature representations in the feature extraction stage. The consistency loss aims at constraining the models to have consistent output representations for the same input. According to the ellipse-like characteristic of ABUS tumors, a rotation augmentation method is designed, which can decrease the overestimate of the bounding box. Besides, in order to reduce the regression error caused by large angle rotation, a rotation discriminant (RD) measurement is developed. The proposed method is verified on two ABUS datasets, one of which is the private ABUS dataset with 68 normal volumes and 68 tumor volumes, including 43,248 slices, and the other is the public ABUS dataset. On the private ABUS dataset, it achieves a promising detection result with a sensitivity of 0.90 and the false positives per slice (FPs/S) at 0.15. [ABSTRACT FROM AUTHOR]

Background: Several image-based diagnostic methods have been developed to examine the features of breast lesions among women, while the value of combining palpation imaging and ultrasound by a bimodal breast examination system is still unknown. Methods: A real-world study was conducted among 424 patients who visited Fujian Maternal and Child Health Hospital and Fujian Obstetrics and Gynecology Hospital, and used the Bimodal Breast Exam (BBE) systems which combines palpation imaging and ultrasound imaging. Among them, 97 patients had additional ultrasound, mammogram, or pathological examination. These patients were used to evaluate the consistency and efficacy of the BBE in interpreting the features of breast lesions as compared to results of ultrasound, mammogram, and pathological examinations. Results: The BBE system detected 1517 lesions with palpation imaging, 1126 lesions with ultrasound examination (950 solid lesions and 176 cysts), and 391 non mass lesions. Among them, 404 patients were diagnosed as benign and 20 were diagnosed as malignant tumor. However, 12, 9 and 4 cases were diagnosed as malignant tumors by ultrasound, mammogram and pathological examination, respectively. Compared with the integrative results of ultrasound, mammogram and pathology, the sensitivity of BBE is 55.6%, and the specificity is 90.9%, with a kappa coefficient of 0.387 (0.110, 0.665), indicating moderate consistency. Conclusions: In clinical practice, BBE can be used to evaluate features of breast lesions with a high specificity. The diagnostic efficacy is comparable to the integrative results of ultrasound, mammography, and pathological examination. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Objectives: To assess the diagnostic performance of 3D automated breast ultrasound (3D-ABUS) in breast cancer screening in a clinical setting. Materials and methods: All patients who had 3D-ABUS between January 2014 and January 2022 for screening were included in this retrospective study. The images were reported by 1 of 6 breast radiologists based on the Breast Imaging Reporting and Data Systems (BI-RADS). The 3D-ABUS was reviewed together with the digital breast tomosynthesis (DBT). Recall rate, biopsy rate, positive predictive value (PPV) and cancer detection yield were calculated. Results: In total, 3616 studies were performed in 1555 women (breast density C/D 95.5% (n = 3455/3616), breast density A/B 4.0% (n = 144/3616), density unknown (0.5% (n = 17/3616)). A total of 259 lesions were detected on 3D-ABUS (87.6% (n = 227/259) masses and 12.4% (n = 32/259) architectural distortions). The recall rate was 5.2% (n = 188/3616) (CI 4.5–6.0%) with only 36.7% (n = 69/188) cases recalled to another date. Moreover, recall declined over time. There were 3.4% (n = 123/3616) biopsies performed, with 52.8% (n = 65/123) biopsies due to an abnormality detected in 3D-ABUS alone. Ten of 65 lesions were malignant, resulting in a positive predictive value (PPV) of 15.4% (n = 10/65) (CI 7.6–26.5%)). The cancer detection yield of 3D-ABUS is 2.77 per 1000 screening tests (CI 1.30–5.1). Conclusion: The cancer detection yield of 3D-ABUS in a real clinical screening setting is comparable to the results reported in previous prospective studies, with lower recall and biopsy rates. 3D-ABUS also may be an alternative for screening when mammography is not possible or declined. Clinical relevance statement: 3D automated breast ultrasound screening performance in a clinical setting is comparable to previous prospective studies, with better recall and biopsy rates. Key Points: • 3D automated breast ultrasound is a reliable and reproducible tool that provides a three-dimensional representation of the breast and allows image visualisation in axial, coronal and sagittal. • The diagnostic performance of 3D automated breast ultrasound in a real clinical setting is comparable to its performance in previously published prospective studies, with improved recall and biopsy rates. • 3D automated breast ultrasound is a useful adjunct to mammography in dense breasts and may be an alternative for screening when mammography is not possible or declined. [ABSTRACT FROM AUTHOR]

The Ugandan Ministry of Health adopted BI-RADS as standard of care in 2016. The authors performed a medical audit of breast ultrasound practices at four tertiary-level hospitals to assess interpretive performance. The authors also determined the effect of a low-cost navigation program linking breast imaging and pathology on the percentage of patients completing diagnostic care. The authors retrieved 966 consecutive diagnostic breast ultrasound reports, with complete data, for studies performed on women aged >18 years presenting with symptoms of breast cancer between 2018 and 2020 from participating hospitals. Ultrasound results were linked to tumor registries and patient follow-up. A medical audit was performed according to the ACR's BI-RADS Atlas, fifth edition, and results were compared with those of a prior audit performed in 2013. At Mulago Hospital, an intervention was piloted on the basis of patient navigation, cost sharing, and same-day imaging, tissue sampling, and pathology. In total, 888 breast ultrasound examinations (91.9%) were eligible for inclusion. Compared with 2013, the postintervention cancer detection rate increased from 38 to 148.7 cancers per 1,000 examinations, positive predictive value 2 from 29.6% to 48.9%, and positive predictive value 3 from 62.7% to 79.9%. Specificity decreased from 90.5% to 87.7% and sensitivity from 92.3% to 81.1%. The mean time from tissue sampling to receipt of a diagnosis decreased from 60 to 7 days. The intervention increased the percentage of patients completing diagnostic care from 0% to 100%. Efforts to establish a culture of continuous quality improvement in breast ultrasound require robust data collection that links imaging results to pathology and patient follow-up. Interpretive performance met BI-RADS benchmarks for palpable masses, except sensitivity. This resource-appropriate strategy linking imaging, tissue sampling, and pathology interpretation decreased time to diagnosis and rates of loss to follow-up and improved the precision of the audit. [ABSTRACT FROM AUTHOR]

Automatic breast ultrasound image segmentation plays an important role in medical image processing. However, current methods for breast ultrasound segmentation suffer from high computational complexity and large model parameters, particularly when dealing with complex images. In this paper, we take the Unext network as a basis and utilize its encoder-decoder features. And taking inspiration from the mechanisms of cellular apoptosis and division, we design apoptosis and division algorithms to improve model performance. We propose a novel segmentation model which integrates the division and apoptosis algorithms and introduces spatial and channel convolution blocks into the model. Our proposed model not only improves the segmentation performance of breast ultrasound tumors, but also reduces the model parameters and computational resource consumption time. The model was evaluated on the breast ultrasound image dataset and our collected dataset. The experiments show that the SC-Unext model achieved Dice scores of 75.29% and accuracy of 97.09% on the BUSI dataset, and on the collected dataset, it reached Dice scores of 90.62% and accuracy of 98.37%. Meanwhile, we conducted a comparison of the model's inference speed on CPUs to verify its efficiency in resource-constrained environments. The results indicated that the SC-Unext model achieved an inference speed of 92.72 ms per instance on devices equipped only with CPUs. The model's number of parameters and computational resource consumption are 1.46M and 2.13 GFlops, respectively, which are lower compared to other network models. Due to its lightweight nature, the model holds significant value for various practical applications in the medical field. [ABSTRACT FROM AUTHOR]

Despite the widely reported potential of deep neural networks for automated breast tumor classification and detection, these models are vulnerable to adversarial attacks, which leads to significant performance degradation on different datasets. In this paper, we introduce a novel adversarial attack approach under the decision-based black-box setting, where the attack does not have access to the model parameters, and the returned information from querying the target model consists of only the final class label prediction (i.e., hard-label attack). The proposed attack approach has two major components: adaptive binary search and semantic-aware search. The adaptive binary search utilizes a coarse-to-fine strategy that applies adaptive tolerance values in different searching stages to reduce unnecessary queries. The proposed semantic mask-aware search crops the search space by using breast anatomy, which significantly avoids invalid searches. We validate the proposed approach using a dataset of 3378 breast ultrasound images and compare it with another state-of-the-art method by attacking five deep learning models. The results demonstrate that the proposed approach generates imperceptible adversarial samples at a high success rate (between 99.52% and 100%), and dramatically reduces the average and median queries by 23.96% and 31.79%, respectively, compared with the state-of-the-art approach. [ABSTRACT FROM AUTHOR]

Background: Strategies to minimize the impact of the COVID-19 pandemic led to a reduction in diagnostic testing. It is important to assess the magnitude and duration of this impact to plan ongoing care and avoid long-lasting impacts of the pandemic. Objective: We examined the association between the COVID-19 pandemic and the rate of diagnostic tests for breast, cervical, and colorectal cancer in Manitoba, Canada. Design and Participants: A population-based, cross-sectional study design with an interrupted time series analysis was used that included diagnostic tests from January 1, 2015 until August 31, 2022. Setting: Manitoba, Canada. Main Outcomes: Outcomes included mammogram, breast ultrasound, colposcopy, and colonoscopy rates per 100,000. Cumulative and percent cumulative differences between the fitted and counterfactual number of tests were estimated. Mean, median, and 90th percentile number of days from referral to colonoscopy date by referral type (elective, semiurgent, urgent) were determined. Results: In April 2020, following the declaration of the COVID-19 public health emergency, bilateral mammograms decreased by 77%, unilateral mammograms by 70%, breast ultrasounds by 53%, colposcopies by 63%, and colonoscopies by 75%. In Winnipeg (the largest urban center in the province), elective and semiurgent colonoscopies decreased by 76% and 39%, respectively. There was no decrease in urgent colonoscopies. As of August 2022, there were an estimated 7270 (10.7%) fewer bilateral mammograms, 2722 (14.8%) fewer breast ultrasounds, 836 (3.3%) fewer colposcopies, and 11 600 (13.8%) fewer colonoscopies than expected in the absence of COVID-19. As of December 2022, in Winnipeg, there were an estimated 6030 (23.9%) fewer elective colonoscopies, 313 (2.6%) fewer semiurgent colonoscopies, and 438 (27.3%) more urgent colonoscopies. Conclusions: In Manitoba, the COVID-19 pandemic was associated with sizable decreases in diagnostic tests for breast, colorectal, and cervical cancer. Two and a half years later, there remained large cumulative deficits in bilateral mammograms, breast ultrasounds, and colonoscopies. [ABSTRACT FROM AUTHOR]

Simple Summary: Tumor marking before neoadjuvant systemic therapy (NAST) is an essential part of early breast cancer treatment, aiming to enable adequate surgical excision of the tumor bed in cases with complete clinical regression. Standard surgical techniques for targeted excision generally rely on the invasive insertion of various markers (radiopaque clips, radioactive seeds, silver wire, etc.) into or around the tumor. These techniques are burdened by technical complexity, the risk of complications (hematoma, infection, marker migration, etc.), and limited precision in the insertion and later identification of markers. Herein, we present our original technique of non-invasive three-dimensional tumor localization before and repeatedly during NAST and the preliminary results of its application. Multiple repetitions (monitoring the occasionally eccentric pattern of tumor regression) without the risk of complications suggest a possible advantage over invasive marking techniques. Application in surgical centers lacking trained staff and equipment for invasive tumor marking may impact the quality improvement of post-NAST breast cancer surgery in those centers. Background: We present a detailed description and the preliminary results of our original technique for non-invasive three-dimensional tumor localization in the breast, which was created as an alternative to standard invasive tumor marking before neoadjuvant systemic therapy (NAST), aiming to enable adequate surgery after complete tumor regression. Methods: A detailed description of the technique is provided in the main text. The technique's feasibility and precision were assessed in a single-arm, prospective study based on the histological parameters of the adequacy and rationality of the excision of completely regressed tumor beds. Results: Out of 94 recruited patients, 15 (16%) were deemed unsuitable, mainly due to the tumors' inadequate ultrasound visibility. Among the 79 processed patients, 31 (39%) had complete clinical regression after NAST and were operated on using our technique. The histological parameters of surgical precision (signs of tumor regression: 24/31; microscopic cancer residues: 7/31) were verified in all excised specimens (100% precision). There were no positive margins in seven cases with microscopic residues, indicating our technique's capacity to enable oncologically safe post-NAST surgery. Conclusions: The proposed technique is feasible and satisfactorily accurate in determining the location of regressed tumors, thus representing an alternative to invasive tumor marking, especially in surgical centers lacking trained staff and equipment for invasive marking. The technique's limitations are mainly related to the inadequate ultrasound visibility of the tumor. [ABSTRACT FROM AUTHOR]

Medical datasets often have a skewed class distribution and a lack of high-quality annotated images. However, deep learning methods require a large amount of labeled data for classification. In this study, we present a few-shot learning approach for the classification of ultrasound breast cancer images using meta-learning methods. We used prototypical networks and model agnostic meta-learning (MAML) algorithms as meta-learning methods. The breast ultrasound images (BUSI) dataset, which has three classes and is difficult to use in meta-learning, was used for meta-testing in a cross-domain approach along with other datasets for meta-training. Our proposed approach yielded an accuracy range of 0.882–0.889, achieved by implementing the ResNet50 backbone with ProtoNet in a 10-shot setting. These results represent a significant improvement ranging from 6.27 to 7.10% over the baseline accuracy of 0.831. The results showed that ProtoNet outperformed the MAML method for all k-shot settings. In addition, the use of ResNet models as the backbone network for feature extraction was found to be more successful than the use of a four-layer convolutional model. Our proposed method is the first attempt to apply meta-learning for few-shot classification in the BUSI dataset while providing higher accuracy compared to deep learning methods for medical images with small-scale datasets and few classes. The methodology used in this study can be adapted to other datasets with similar problems. [ABSTRACT FROM AUTHOR]

Breast lymphomas are rare, malignant breast neoplasms with a heterogeneous pattern of clinical symptoms. Burkitt's lymphoma is a rare, highly aggressive, and rapidly growing B-cell non-Hodgkin lymphoma. We report about a 27-year-old woman diagnosed as having secondary breast Burkitt's lymphoma, probably originating from the stomach, with multiple distant metastases. Breast ultrasonography revealed multiple, variable sized, heterogeneous masses with posterior acoustic enhancement and echogenic rims. These imaging findings may sometimes overlap with those of other breast malignancies. However, unlike other breast malignancies, lymphoma can be diagnosed by biopsy and does not require surgical excision. To avoid unnecessary treatment, radiologists and clinicians should be aware of the characteristic imaging features of breast lymphomas. [ABSTRACT FROM AUTHOR]

Objectives: To generate and validate a prediction model based on imaging features for cancer risk of non-mass lesions (NMLs) detected on breast ultrasound (US). Methods: In this single-center study, consecutive women with 503 NMLs detected on breast US between 2012 and 2019 were retrospectively identified. The lesions were randomly assigned to the training or testing dataset with a 70/30 split. Age, symptoms, lesion size, and US features were collected. Multivariate analyses were employed to identify risk factors associated with malignancy. The predictive model was developed by using conditional inference trees (CTREE). Results: There were 498 patients (50.9 ± 13.29 years; range, 22–88 years) with 503 NMLs with histopathologic results or > 2-year follow-up, including 224 (44.5%) benign and 279 (55.5%) malignant lesions. At multivariate analysis, age (odds ratio (OR) = 1.08, 95% confidence interval (CI), 1.06–1.11, p < 0.001), NMLs with focal mass effect (OR = 3.03, 95% CI, 1.59–5.81, p = 0.001), indistinct glandular–fat interface (GFI) (OR = 4.23, 95% CI, 2.31–7.73, p < 0.001), geographic (OR = 3.47, 95% CI, 1.20–10.8, p = 0.022) and mottled (OR = 3.67, 95% CI, 1.32–10.21, p = 0.013) patterns, and calcifications (OR = 2.15, 95% CI, 1.16–4.01, p = 0.016) were associated with malignancy. The GFI status, architectural patterns, general morphology, and calcifications were consistently identified as the strongest US predictors of malignancy using CTREE analysis. Based on these factors, individuals were stratified into six risk groups. The predictive model showed an area under the curve of 0.797 in the testing dataset. Conclusion: The CTREE model efficiently aids in interpreting and managing ultrasound-detected breast NMLs, overcoming BI-RADS limitations by refining cancer risk stratification. Clinical relevance statement: The CTREE model allows for the reclassification of BI-RADS categories into subgroups with varying malignancy probabilities, thus providing a valuable enhancement to the BI-RADS assessment for the diagnosis of ultrasound-detected NMLs, with the potential to minimize unnecessary biopsies. Key Points: • The indistinct glandular–fat interface (GFI) status, NML with focal mass effect, geographic or mottled patterns, and calcifications are the strongest imaging predictors of malignant non-mass lesions (NMLs) detected on breast US. • A practical system has been created to categorize NMLs found in breast US; each classification is associated with a degree of diagnostic certainty. • The model may contribute to patient stratification by determining the relative likelihood of malignancy and thus support clinical decision-making and evidence-based management. [ABSTRACT FROM AUTHOR]

This study seeks to enhance the classification performance of breast ultrasound images, addressing the challenges of difficult and costly collection of breast ultrasound datasets as well as the discrepancies in feature distribution of the collected datasets. Performance is enhanced by using a mix of generative adversarial networks (GAN) and domain adaptive networks. First, an adaptive layer is first added to the basic model of the gradually vanishing bridge (GVB), to better match the feature distributions of the source and target domains of the dataset. The multi-kernel maximum mean discrepancy (MK-MMD), which is the most efficient of existing adaptive approaches, is implemented in the fully connected layer of the original model's feature extraction network. Finally, through the process of fine-tuning, the model that has the highest level of overall performance is determined. In experiments, the proposed method surpassed the conventional unsupervised domain adaptation (DDC) and adversarial domain adaptation (MK_DAAN, GVB) in performance, achieving 85.11% accuracy, 97.48% recall, and 0.92 F1-score. [ABSTRACT FROM AUTHOR]

Background: The study aims to evaluate the diagnostic efficacy of contrast-enhanced ultrasound (CEUS) and shear-wave elastography (SWE) in detecting small malignant breast nodules in an effort to inform further refinements of the Breast Imaging Reporting and Data System (BI-RADS) classification system. Methods: This study retrospectively analyzed patients with breast nodules who underwent conventional ultrasound, CEUS, and SWE at Gongli Hospital from November 2015 to December 2019. The inclusion criteria were nodules ≤ 2 cm in diameter with pathological outcomes determined by biopsy, no prior treatments, and solid or predominantly solid nodules. The exclusion criteria included pregnancy or lactation and low-quality images. Imaging features were detailed and classified per BI-RADS. Diagnostic accuracy was assessed using receiver operating characteristic curves. Results: The study included 302 patients with 305 breast nodules, 113 of which were malignant. The diagnostic accuracy was significantly improved by combining the BI-RADS classification with CEUS and SWE. The combined approach yielded a sensitivity of 88.5%, specificity of 87.0%, positive predictive value of 80.0%, negative predictive value of 92.8%, and accuracy of 87.5% with an area under the curve of 0.877. Notably, 55.8% of BI-RADS 4A nodules were downgraded to BI-RADS 3 and confirmed as benign after pathological examination, suggesting the potential to avoid unnecessary biopsies. Conclusion: The integrated use of the BI-RADS classification, CEUS, and SWE enhances the accuracy of differentiating benign and malignant small breast nodule, potentially reducing the need for unnecessary biopsies. [ABSTRACT FROM AUTHOR]

Objective Evaluate surgical utilization of SCOUT reflectors placed at breast biopsy. Methods Consent was waived for this retrospective IRB-approved, HIPAA-compliant study. Breast biopsy examinations that reported the term "SCOUT" between January 2021 and June 2022 were identified using an institutional search engine. Cases were included if a SCOUT reflector was placed at time of breast biopsy and excluded if lesion pathology was already known. Analysis was performed at the lesion level. A multivariate-regression analysis evaluated 6 variables with potential impact on SCOUT utilization. Results One hundred twenty-one lesions in 112 patients met inclusion criteria. Biopsy yielded 93% (113/121) malignant, 3% (4/121) elevated risk, 2% (2/121) benign-discordant, and 2% (2/121) benign-concordant results. Two cases lost to follow-up were excluded. SCOUT reflectors were utilized for lumpectomy (58%, 69/119 lesions) and excisional biopsy (6%, 7/119 lesions). SCOUTs were not utilized due to mastectomy (23%, 27/119), subsequent wire localization (2%, 2/119), and nonsurgical cases (12%, 14/119). Reflector placement utilization was 52% higher for findings less than 3.5 cm in size (P <.001), 33% higher in patients without prior treated breast cancer (P = .012), and 19% higher in patients with no suspicious ipsilateral lymph node (P = .048). Conclusion SCOUT reflector placement at time of biopsy was utilized for surgery 64% (76/119) of the time, although most (98%, 119/121) biopsies were malignant, elevated risk, or benign-discordant. Factors increasing reflector utilization include smaller lesion size, no suspicious ipsilateral lymph node, and no prior treated breast cancer. [ABSTRACT FROM AUTHOR]

Systemic lupus erythematosus (SLE) and sarcoidosis are two of the most well-recognized, chronically diagnosed conditions in the United States, with a plethora of known multisystem manifestations. With regard to breast pathology, lupus mastitis is a relatively uncommon manifestation of SLE, commonly involving both the mammary gland and subcutaneous soft tissues of the breast. Sarcoidosis in the breast is a similarly, exceedingly rare manifestation of this multi-system disorder, classically presenting with non-caseating granulomas. Both present with non-specific mammographic and sonographic features. We present a 62-year-old female with known diagnosis of discoid lupus and Graves’ disease who presented initially with an abnormal screening mammogram, ultimately undergoing mammographic work-up and subsequent biopsy demonstrating lupus mastitis, including vasculitis, panniculitis, and fibrosis with chronic inflammation. The patient was also found to have small non-caseating granulomas, some in a perivascular distribution, classically seen in sarcoidosis. Given the rarity of both manifestations, our case explores the coexistence of these autoimmune processes and this atypical presentation.

We aimed to determine the value of standalone and supplemental automated breast ultrasound (ABUS) in detecting cancers in an opportunistic screening setting with digital breast tomosynthesis (DBT) and compare this combined screening method to DBT and ABUS alone in women older than 39 years with BI‐RADS B‐D density categories. In this prospective opportunistic screening study, 3466 women aged 39 or older with BI‐RADS B‐D density categories and with a mean age of 50 were included. The screening protocol consisted of DBT mediolateral‐oblique views, 2D craniocaudal views, and ABUS with three projections for both breasts. ABUS was evaluated blinded to mammography findings. Statistical analysis evaluated diagnostic performance for DBT, ABUS, and combined workflows. Twenty‐nine cancers were screen‐detected. ABUS and DBT exhibited the same cancer detection rates (CDR) at 7.5/1000 whereas DBT + ABUS showed 8.4/1000, with ABUS contributing an additional CDR of 0.9/1000. Standalone ABUS outperformed DBT in detecting 12.5% more invasive cancers. DBT displayed better accuracy (95%) compared to ABUS (88%) and combined approach (86%). Sensitivities for DBT and ABUS were the same (84%), with DBT + ABUS showing a higher rate (94%). DBT outperformed ABUS in specificity (95% vs. 88%). DBT + ABUS exhibited a higher recall rate (14.89%) compared to ABUS (12.38%) and DBT (6.03%) (p <.001). Standalone ABUS detected more invasive cancers compared to DBT, with a higher recall rate. The combined approach showed a higher CDR by detecting one additional cancer per thousand. [ABSTRACT FROM AUTHOR]

Objective: Triple-negative breast cancer (TNBC) has an aggressive clinical behaviour, with advanced stages at initial diagnostic evaluation, early recurrences and poor survival, so the purpose was to determine the clinical and radiological manifestations associated with TNBC. Materials and methods: A case-control study in women diagnosed with breast cancer from January 2015 to August 2022 at the 'Instituto Regional de Enfermedades Neoplásicas del Norte'. We classified cases (Triple Negative subtype) and controls (Luminal A, Luminal B and HER2) according to immunohistochemistry ical analysis. Bivariate and multivariate logistic regression models were used to calculate the odds ratio (OR) with their respective 95% confidence intervals (CIs). Results: The medical reports of 88 cases and 236 controls were reviewed. Cases were more likely to report pain (p = 0.001), nodules on ultrasound (p = 0.01) and mammography (p = 0.003), superior median size (p < 0.05), posterior enhancement (p = 0.001) and moderate density (p = 0.003). Multivariate analysis identified that TNBC was more likely to have a nodular type lesion by ultrasound (OR: 9.73, 95% CI: 1.10--86.16; p = 0.04), ultrasound lesion larger than 36 mm (OR: 4.99, 95% CI: 1.75-14.17; p = 0.003) and moderate density (OR: 3.83, 95% CI: 1.44-10.14; p = 0.007). Conclusion: There are particular clinical and imaging manifestations of TNBC, showing that radiological lesions that presented characteristics in ultrasound as nodular type lesions larger than 36 mm and in mammography moderate grade density, were associated with this subtype of breast tumours in a Peruvian population. [ABSTRACT FROM AUTHOR]

Intensity inhomogeneity often appears in medical images and causes great difficulties in image segmentation. Most active contour models perform poorly when applied to intensity inhomogeneous images because their energy functions use local intensity information in a fixed-size domain, causing the contour to evolve in the wrong direction. To overcome the difficulties caused by intensity inhomogeneity, we propose an adaptive multi-scale Gaussian kernel function based on image local entropy, which can determine the appropriate scale for each local region. Choosing the small scale and large scale for inhomogeneous and homogeneous areas respectively make the contour move toward the target boundary accurately. We also propose three adaptive multi-scale (AMS) models, AMS-region scalable fitting (AMS-RSF) model, AMS-local image fitting (AMS-LIF) model, AMS-local and global intensity fitting (AMS-LGIF) model, to segment medical images with intensity inhomogeneity and noise, including left atrial MR images and breast ultrasound images. The experimental results show that the adaptive multi-scale Gaussian kernel function enables the active contour model to effectively segment intensity inhomogeneous images and has a certain robustness to the initial contour and noise, which achieves good performance on MR left atrial images and ultrasound images of breast cancer. The AMS-LGIF model obtained the highest DICE coefficient of 0.9532, which was better than the 0.9429 obtained by the second-ranked LGIF model to segment left atrial MR images. For segmenting breast ultrasound images, the DICE coefficient is increased by 16% than that of the U-Net++ model. • An adaptive multi-scale Gaussian kernel function based on image local entropy is proposed. • Adaptive multi-scale RSF, LIF, and LGIF models are proposed. • The proposed adaptive multi-scale model is robust to initial contours and noise. • Numerical results such as DICE, JS, Precision, and Recall values are given. • The experimental results show that our adaptive multi-scale model is better than the other methods. [ABSTRACT FROM AUTHOR]

Background: Deep convolutional neural networks have garnered considerable attention in numerous machine learning applications, particularly in visual recognition tasks such as image and video analyses. There is a growing interest in applying this technology to diverse applications in medical image analysis. Automated three-dimensional Breast Ultrasound is a vital tool for detecting breast cancer, and computer-assisted diagnosis software, developed based on deep learning, can effectively assist radiologists in diagnosis. However, the network model is prone to overfitting during training, owing to challenges such as insufficient training data. This study attempts to solve the problem caused by small datasets and improve model detection performance. Methods: We propose a breast cancer detection framework based on deep learning (a transfer learning method based on cross-organ cancer detection) and a contrastive learning method based on breast imaging reporting and data systems (BI-RADS). Results: When using cross organ transfer learning and BIRADS based contrastive learning, the average sensitivity of the model increased by a maximum of 16.05%. Conclusion: Our experiments have demonstrated that the parameters and experiences of cross-organ cancer detection can be mutually referenced, and contrastive learning method based on BI-RADS can improve the detection performance of the model.

Background: Assessment of breast development by physical examination can be difficult in the early stages and in overweight girls. Objective: To investigate ultrasonography (US) for evaluation of early breast development. Materials and methods: In a prospective study, 125 girls (age 7.1 ± 1.5 years) with breast development before 8 years underwent US breast staging, breast volume, and elastography, in addition to clinical/hormonal evaluation for precocious puberty. Accuracy of US for determining breast development and predicting progression to central precocious puberty was investigated. Results: Physical examination revealed glandular breast enlargement in 100 and predominantly lipomastia in 25. Breast US in the former confirmed glandular breast development in 92 (group 1, physical examination and US positive), but not in 8 (group 2, physical examination positive, US negative). Comparison of the two groups demonstrated lower Tanner and US staging, bone age/chronological age, basal luteinizing hormone (LH), breast volume, and uterine volume in group 2. In the 25 lipomastia patients, US demonstrated no breast tissue in 19 (group 3, physical examination and US negative), but US stage ≥ II in 6 (group 4, physical examination negative, US positive) without differences in clinical parameters. After follow-up of 19.8 ± 4.2 months, 46/125 subjects were diagnosed with precocious puberty. US stage, total breast volume, and shear-wave speeds were significantly higher in these 46 patients. Multivariate analyses demonstrated breast volume > 3.4 cc had odds ratio of 11.0, sensitivity of 62%, and specificity of 89, in predicting progression to precocious puberty, being second only to stimulated LH for all variables. Conclusion: Breast US is a useful predictive tool for diagnosis of precocious puberty in girls. Higher US stages and higher breast volume on US increased the likelihood of eventual diagnosis of precocious puberty. [ABSTRACT FROM AUTHOR]

Background: Breast tumor is a fatal threat to the health of women. Ultrasound (US) is a common and economical method for the diagnosis of breast cancer. Breast imaging reporting and data system (BI‐RADS) category 4 has the highest false‐positive value of about 30% among five categories. The classification task in BI‐RADS category 4 is challenging and has not been fully studied. Purpose: This work aimed to use convolutional neural networks (CNNs) for breast tumor classification using B‐mode images in category 4 to overcome the dependence on operator and artifacts. Additionally, this work intends to take full advantage of morphological and textural features in breast tumor US images to improve classification accuracy. Methods: First, original US images coming directly from the hospital were cropped and resized. In 1385 B‐mode US BI‐RADS category 4 images, the biopsy eliminated 503 samples of benign tumor and left 882 of malignant. Then, K‐means clustering algorithm and entropy of sliding windows of US images were conducted. Considering the diversity of different characteristic information of malignant and benign represented by original B‐mode images, K‐means clustering images and entropy images, they are fused in a three‐channel form multi‐feature fusion images dataset. The training, validation, and test sets are 969, 277, and 139. With transfer learning, 11 CNN models including DenseNet and ResNet were investigated. Finally, by comparing accuracy, precision, recall, F1‐score, and area under curve (AUC) of the results, models which had better performance were selected. The normality of data was assessed by Shapiro‐Wilk test. DeLong test and independent t‐test were used to evaluate the significant difference of AUC and other values. False discovery rate was utilized to ultimately evaluate the advantages of CNN with highest evaluation metrics. In addition, the study of anti‐log compression was conducted but no improvement has shown in CNNs classification results. Results: With multi‐feature fusion images, DenseNet121 has highest accuracy of 80.22 ± 1.45% compared to other CNNs, precision of 77.97 ± 2.89% and AUC of 0.82 ± 0.01. Multi‐feature fusion improved accuracy of DenseNet121 by 1.87% from classification of original B‐mode images (p < 0.05). Conclusion: The CNNs with multi‐feature fusion show a good potential of reducing the false‐positive rate within category 4. The work illustrated that CNNs and fusion images have the potential to reduce false‐positive rate in breast tumor within US BI‐RADS category 4, and make the diagnosis of category 4 breast tumors to be more accurate and precise. [ABSTRACT FROM AUTHOR]

Background: Locally advanced breast cancer (LABC) can be caused by a variety of breast neoplasms. The use of mammography with sonography to estimate pathological complete response (PCR) after neoadjuvant chemotherapy (NAC) has a good degree of precision. Aim of work: To evaluate the precision of breast sonomammography in determining LABC response to neoadjuvant chemotherapy. Subjects and methods: A total of 54 female patients with locally advanced breast cancer were included in the study, which was conducted prospectively from August 2019 to May 2020, they were sent to the Zagazig University Radiology Department and hospitals by the General Surgery and Oncology Departments. A thorough medical history (personal, past, present, and family) was collected, as well as a thorough physical examination. All subjects had bilateral digital mammography as well as a traditional B-mode ultrasonography examination. Before neoadjuvant treatment, a sonomammographic evaluation of the tumour size and morphometric criteria was performed. Biopsy processes were done for suspected breast masses. after finishing the NAC 1:4 weeks previous to surgery, a sonomammographic evaluation of the tumour size and morphological criteria was conducted. Results: There was significant association between post chemotherapy diameters and different breast subtypes in radiological assessment. HR-/HER2-were the most subtype to show radiological response followed by HR+/HER2-while none of HR+/HER2+ show response to NAC. Conclusion: In patients with LABC, mammography and breast ultrasonography are regarded more reliable techniques for assessing tumour size and nodal staging of breast cancer subtypes prior to or after treatment. [ABSTRACT FROM AUTHOR]

Secretory carcinoma of the breast (SCB) is a rare and specific type of breast cancer. Owing to its rarity, the number of SCB reports available is limited, with most of them focusing on clinical and pathological characteristics but no reports on its multimodal ultrasound (US) features. Thus, we present a rare case of SCB, retrospectively analyzing manifestations of US and contrast-enhanced US, as well as its pathological basis, aiming to enhance the understanding of US image features of SCB and provide more valuable information for clinical diagnosis. Moreover, the treatment strategy adopted for this patient may serve as a template for future management of SCB. [ABSTRACT FROM AUTHOR]

Breast cancer is a medical condition characterized by the uncontrolled proliferation of cells in the breast tissue. Breast cancer can originate from various parts of the breast, and methods such as Breast ultrasound, Diagnostic mammogram, Breast magnetic resonance imaging, and Biopsy are currently used for its diagnosis. However, these methods have certain limitations, and their size can be a hindrance. To overcome this, low-power, flexible antennas can be designed for bio-communication between medical equipment and external instrumentation. Flexible and wearable antennas have advantages such as affordability, ease of fabrication, and high gain. In this article, a microstrip patch antenna operating at 2.45 GHz and made of polyamide material is designed using High Frequency Structure Simulator software. The simulation results show the patch antenna has a gain of 1, −14.81 dB return loss at 2.45 GHz based on |S11| ≤ −10 dB. The directive radiation pattern with axial ratio of 63.39 dB and voltage standing wave ratio ≤ 3. Furthermore, the hardware development of proposed antenna with polyamide substrate provides the resonance frequency nearing to simulation results as 2.318 GHz with return loss of −28.19 dB. Based on mathematical analysis, simulation and hardware results, the proposed antenna is a superior option for breast cancer detection. [ABSTRACT FROM AUTHOR]

Purpose: Preventing positive margins is essential for ensuring favorable patient outcomes following breast-conserving surgery (BCS). Deep learning has the potential to enable this by automatically contouring the tumor and guiding resection in real time. However, evaluation of such models with respect to pathology outcomes is necessary for their successful translation into clinical practice. Methods: Sixteen deep learning models based on established architectures in the literature are trained on 7318 ultrasound images from 33 patients. Models are ranked by an expert based on their contours generated from images in our test set. Generated contours from each model are also analyzed using recorded cautery trajectories of five navigated BCS cases to predict margin status. Predicted margins are compared with pathology reports. Results: The best-performing model using both quantitative evaluation and our visual ranking framework achieved a mean Dice score of 0.959. Quantitative metrics are positively associated with expert visual rankings. However, the predictive value of generated contours was limited with a sensitivity of 0.750 and a specificity of 0.433 when tested against pathology reports. Conclusion: We present a clinical evaluation of deep learning models trained for intraoperative tumor segmentation in breast-conserving surgery. We demonstrate that automatic contouring is limited in predicting pathology margins despite achieving high performance on quantitative metrics. [ABSTRACT FROM AUTHOR]

Purpose: Primary Mucosa-associated lymphoid tissue (MALT) lymphoma is a rare diagnosis in the breast, and clinical diagnosis based on radiological features is often challenging. This study aimed to evaluate the clinicopathological, and radiological characteristics of the patients diagnosed with primary breast MALT lymphoma. Methods: This study examined 18 cases of primary MALT lymphoma of the breast diagnosed at a single tertiary center between January 2002 to December 2020. Medical charts, radiological imaging and original pathology slides were reviewed for each case. Results: All cases were female (gender assigned at birth) and presented with a palpable mass or an incidental imaging finding. Imaging presentation ranged from mammographic asymmetries, circumscribed masses, and ultrasound masses lacking suspicious features. Seventeen cases were biopsied under ultrasound; one received a diagnostic excision biopsy. Microscopic examination of the breast specimens demonstrated atypical small lymphocyte infiltration with plasmacytoid differentiation and rare lymphoepithelial lesions. Immunohistochemistry was performed in all cases and established the diagnosis. Most patients were treated with radiotherapy, and only three were treated with chemotherapy. The median follow-up period was 4 years and 7.5 months, and all patients were alive at the last follow-up. Conclusion: Primary MALT breast lymphomas are usually indolent and non-systemic, and local radiotherapy may effectively alleviate local symptoms. Radiological findings show overlap with benign morphological features, which can delay the diagnosis of this unusual etiology. Although further studies involving a larger cohort could help establish the clinical and radiological characteristics of primary breast MALT lymphomas, pathology remains the primary method of diagnosis. Trial registration number: University Health Network Ethics Committee (CAPCR/UHN REB number 19–5844), retrospectively registered. [ABSTRACT FROM AUTHOR]

Objectives: The coronal plane is the unique display mode of automated breast (AB) ultrasound (US), which has valuable features of showing the entire breast anatomy and providing additional diagnostic value for breast lesions. However, whether adding the coronal plane could improve the diagnostic performance in screening breast cancer remains uncertain. This study aimed to evaluate the value of adding the coronal plane in interpretation for AB US screening. Methods: In this retrospective study, AB US images from 644 women (396 in the no‐finding group, 143 with benign lesions, and 105 with malignant lesions) aged 40–70 years were collected between January 2016 and October 2020. Four novice radiologists (with 1–5 years of experience with breast US) and four experienced radiologists (with >5 years of experience with breast US) were assigned to read all AB US images in the transverse plane plus coronal plane (T + C planes) and transverse plane (T plane) alone in separate reading sessions. Diagnostic performance, lesion conspicuity, and reading time were compared using analysis of variance. Results: The mean reading time of all radiologists was significantly shorter in the T + C planes reading mode than in the T plane alone (115 ± 32 vs 128 ± 31 s, respectively; P <.05), and cancers had a higher conspicuity (odds ratio, 1.76; 95% confidence interval [CI], 1.00–3.08; P =.04). No significant differences were noted in the two reading modes (T + C planes vs T plane) in the sensitivity (82% [95% CI, 74–89%] vs 81% [95% CI, 74–88%], respectively; P =.68) and specificity (68% [95% CI, 62–75%] vs 70% [95% CI, 64–75%], respectively; P =.39) when Breast Imaging‐Reporting and Data System (BI‐RADS) 3 was set as the threshold. There were also no significant differences in the two reading modes (T + C planes vs T plane) in the sensitivity (70% [95% CI, 64–76%] vs 69% [95% CI, 63–75%], respectively; P =.39) and specificity (91% [95% CI, 87–96%] vs 91% [95% CI, 88–95%], respectively; P =.90) when BI‐RADS 4 was set as the threshold. In addition, the mean areas under the receiver operating characteristic curves of all radiologists in the two reading modes (T + C planes vs T plane) were not significantly different (0.84 [95% CI, 0.79–0.89] vs 0.83 [95% CI, 0.78–0.89], respectively; P =.61). Conclusions: Adding a coronal plane in the AB US screening setting saved the reading time and improved the conspicuity of breast cancers but not the diagnostic performance. [ABSTRACT FROM AUTHOR]

Objective: Non-lactational mastitis (NLM) is a benign inflammatory disease of the mammary gland, with pain, swelling and redness as the main clinical manifestations. There is no unified and effective standard treatment plan for this disease at present. In addition to breast cancer, non-lactational mastitis is also becoming a presenting complaint in an increasing number of outpatients at the authors' clinic. This case report summarises the treatment and management of a 35-year-old female patient with NLM complicated with multiple sinus wounds after surgery. Method: The patient was treated as follows, with: timely debridement according to the local condition of the wound, with manual compression to drain exudate from the sinus wound; selected wound dressings according to their performance and characteristics to fill the sinus tract for drainage and infection control; psychological care of the patient and their family to ensure that patients actively participate in the treatment; family support to the patient to deal with negative emotions; integrated traditional Chinese and Western medicine to prevent/manage infection; dietary care and control; posture management and health education to facilitate the patient's wound healing process. Results: After local management with systemic treatment and management using integrated traditional Chinese and Western medicine, the wound healed after 46 days, with no recurrence during a follow-up period of one year. Conclusion: As shown in this case report, the wound should be cut and drained as soon as possible in order to prevent obstruction of the sinus drainage. Modern wound dressings are selected for the 'external' treatment of local wounds. Integrated traditional Chinese and Western medicine may help in systemic therapy of the whole patient. [ABSTRACT FROM AUTHOR]

Breast cancer remains a critical global concern, underscoring the urgent need for early detection and accurate diagnosis to improve survival rates among women. Recent developments in deep learning have shown promising potential for computer-aided detection (CAD) systems to address this challenge. In this study, a novel segmentation method based on deep learning is designed to detect tumors in breast ultrasound images. Our proposed approach combines two powerful attention mechanisms: the novel Positional Convolutional Block Attention Module (PCBAM) and Shifted Window Attention (SWA), integrated into a Residual U-Net model. The PCBAM enhances the Convolutional Block Attention Module (CBAM) by incorporating the Positional Attention Module (PAM), thereby improving the contextual information captured by CBAM and enhancing the model's ability to capture spatial relationships within local features. Additionally, we employ SWA within the bottleneck layer of the Residual U-Net to further enhance the model's performance. To evaluate our approach, we perform experiments using two widely used datasets of breast ultrasound images and the obtained results demonstrate its capability in accurately detecting tumors. Our approach achieves state-of-the-art performance with dice score of 74.23% and 78.58% on BUSI and UDIAT datasets, respectively in segmenting the breast tumor region, showcasing its potential to help with precise tumor detection. By leveraging the power of deep learning and integrating innovative attention mechanisms, our study contributes to the ongoing efforts to improve breast cancer detection and ultimately enhance women's survival rates. The source code of our work can be found here: https://github.com/AyushRoy2001/DAUNet. [ABSTRACT FROM AUTHOR]

Objective: To evaluate the diagnostic accuracy of supplemental 3D automated breast ultrasound (ABUS) in the diagnostic work-up of BI-RADS 0 recalls. We hypothesized that 3D ABUS may reduce the benign biopsy rate. Materials and methods: In this prospective multicenter diagnostic study, screening participants recalled after a BI-RADS 0 result underwent bilateral 3D ABUS supplemental to usual care: digital breast tomosynthesis (DBT) and targeted hand-held ultrasound (HHUS). Sensitivity, specificity, positive predictive value, and negative predictive value of 3D ABUS, and DBT plus HHUS, were calculated. New 3D ABUS findings and changes of management (biopsy or additional imaging) were recorded. Results: A total of 501 women (median age 55 years, IQR [51–64]) with 525 BI-RADS 0 lesions were included between April 2018 and March 2020. Cancer was diagnosed in 45 patients. 3D ABUS sensitivity was 72.1% (95% CI [57.2–83.4%]), specificity 84.4% (95% CI [80.8–87.4%]), PPV 29.2% (95% CI [21.4–38.5%]), and NPV 97.1% 95.0–98.4%). Sensitivity of DBT plus HHUS was 100% (95% CI [90.2–100%]), specificity 71.4% (95% CI [67.2–75.2%]), PPV 23.8% (95% CI [18.1–30.5%]) and NPV 100% (95% CI [98.7–100%]). Twelve out of 43 (27.9%) malignancies in BI-RADS 0 lesions were missed on 3D ABUS, despite being detected on DBT and/or HHUS. Supplemental 3D ABUS resulted in the detection of 57 new lesions and six extra biopsy procedures, all were benign. Conclusion: 3D ABUS in the diagnostic work-up of BI-RADS 0 recalls may miss over a quarter of cancers detected with HHUS and/or DBT and should not be used to omit biopsy. Supplemental 3D ABUS increases the benign biopsy rate. Trial registration: Dutch Trial Register, available via https://www.onderzoekmetmensen.nl/en/trial/29659 Critical relevance statement: Supplemental 3D automated breast ultrasound in the work-up of BI-RADS 0 recalls may miss over a quarter of cancers detected with other methods and should not be used to omit biopsy; ABUS findings did increase benign biopsy rate. Key Points: Automated breast ultrasound (ABUS) may miss over 25% of cancers detectable by alternative methods. Don't rely solely on 3D ABUS to assess indication for biopsy. New findings with supplemental 3D ABUS increase the benign biopsy rate. [ABSTRACT FROM AUTHOR]

Background: Automated Breast Ultrasound (AB US) has shown good application value and prospects in breast disease screening and diagnosis. The aim of the study was to explore the ability of AB US to detect and diagnose mammographically Breast Imaging Reporting and Data System (BI-RADS) category 4 microcalcifications. Methods: 575 pathologically confirmed mammographically BI-RADS category 4 microcalcifications from January 2017 to June 2021 were included. All patients also completed AB US examinations. Based on the final pathological results, analyzed and summarized the AB US image features, and compared the evaluation results with mammography, to explore the detection and diagnostic ability of AB US for these suspicious microcalcifications. Results: 250 were finally confirmed as malignant and 325 were benign. Mammographic findings including microcalcifications morphology (61/80 with amorphous, coarse heterogeneous and fine pleomorphic, 13/14 with fine-linear or branching), calcification distribution (189/346 with grouped, 40/67 with linear and segmental), associated features (70/96 with asymmetric shadow), higher BI-RADS category with 4B (88/120) and 4 C (73/38) showed higher incidence in malignant lesions, and were the independent factors associated with malignant microcalcifications. 477 (477/575, 83.0%) microcalcifications were detected by AB US, including 223 malignant and 254 benign, with a significantly higher detection rate for malignant lesions (x2 = 12.20, P < 0.001). Logistic regression analysis showed microcalcifications with architectural distortion (odds ratio [OR] = 0.30, P = 0.014), with amorphous, coarse heterogeneous and fine pleomorphic morphology (OR = 3.15, P = 0.037), grouped (OR = 1.90, P = 0.017), liner and segmental distribution (OR = 8.93, P = 0.004) were the independent factors which could affect the detectability of AB US for microcalcifications. In AB US, malignant calcification was more frequent in a mass (104/154) or intraductal (20/32), and with ductal changes (30/41) or architectural distortion (58/68), especially with the both (12/12). BI-RADS category results also showed that AB US had higher sensitivity to malignant calcification than mammography (64.8% vs. 46.8%). Conclusions: AB US has good detectability for mammographically BI-RADS category 4 microcalcifications, especially for malignant lesions. Malignant calcification is more common in a mass and intraductal in AB US, and tend to associated with architectural distortion or duct changes. Also, AB US has higher sensitivity than mammography to malignant microcalcification, which is expected to become an effective supplementary examination method for breast microcalcifications, especially in dense breasts. [ABSTRACT FROM AUTHOR]

• ABUS is a 3D volume imaging technique for screening dense breasts, with similar diagnostic accuracy as handheld Ultrasound. • Adequate training, systematic quality checks and a learning curve are crucial in improving scans quality and reading times. • Recognizing ABUS unique artefacts and pitfalls is key in delivering state of the art exams and reports. • Large multicentric trials are needed to establish the feasibility of ABUS as an additional screening tool for dense breasts. [ABSTRACT FROM AUTHOR]

Although ultrasound is a common tool for breast cancer screening, its accuracy is often operator-dependent. In this study, we proposed a new automated deep-learning framework that extracts video-based ultrasound data for breast cancer screening. Our framework incorporates DenseNet121, MobileNet, and Xception as backbones for both video- and image-based models. We used data from 3907 patients to train and evaluate the models, which were tested using video- and image-based methods, as well as reader studies with human experts. This study evaluated 3907 female patients aged 22 to 86 years. The results indicated that the MobileNet video model achieved an AUROC of 0.961 in prospective data testing, surpassing the DenseNet121 video model. In real-world data testing, it demonstrated an accuracy of 92.59%, outperforming both the DenseNet121 and Xception video models, and exceeding the 76.00% to 85.60% accuracy range of human experts. Additionally, the MobileNet video model exceeded the performance of image models and other video models across all evaluation metrics, including accuracy, sensitivity, specificity, F1 score, and AUC. Its exceptional performance, particularly suitable for resource-limited clinical settings, demonstrates its potential for clinical application in breast cancer screening. The level of expertise reached by the video models was greater than that achieved by image-based models. We have developed an artificial intelligence framework based on videos that may be able to aid breast cancer diagnosis and alleviate the shortage of experienced experts. [ABSTRACT FROM AUTHOR]

Breast cancer is a prominent contributor to mortality associated with cancer in the female population on a global scale. The timely identification and precise categorization of breast cancer are of utmost importance in enhancing patient prognosis. Nevertheless, the task of precisely categorizing breast cancer based on ultrasound imaging continues to present difficulties, primarily due to the presence of dense breast tissues and their inherent heterogeneity. This study presents a unique approach for breast cancer categorization utilizing the wavelet based vision transformer network. To enhance the neural network's receptive fields, we have incorporated the discrete wavelet transform (DWT) into the network input. This technique enables the capture of significant features in the frequency domain. The proposed model exhibits the capability to effectively capture intricate characteristics of breast tissue, hence enabling correct classification of breast cancer with a notable degree of precision and efficiency. We utilized two breast tumor ultrasound datasets, including 780 cases from Baheya hospital in Egypt and 267 patients from the UDIAT Diagnostic Centre of Sabadell in Spain. The findings of our study indicate that the proposed transformer network achieves exceptional performance in breast cancer classification. With an AUC rate of 0.984 and 0.968 on both datasets, our approach surpasses conventional deep learning techniques, establishing itself as the leading method in this domain. This study signifies a noteworthy advancement in the diagnosis and categorization of breast cancer, showcasing the potential of the proposed transformer networks to enhance the efficacy of medical imaging analysis. [ABSTRACT FROM AUTHOR]

Radiomics is a technology that extracts a large number of quantitative features from high-throughput medical images and has become a focus of research. It can help in disease diagnosis, therapy planning, and prognosis evaluation through Big Data analysis algorithms. Radiomics technology can extract hundreds or even tens of thousands of quantifiable data features from medical images, which can no longer fit into the memory of one machine. Therefore, we propose a distributed correlation analysis algorithm (DFCA) based on a MapReduce distributed computing framework for breast ultrasound radiomics feature datasets. Each compute node will produce massive intermediate data while the DFCA calculates the Pearson correlation coefficient of radiomics features. With the increase of feature data and dimensions, the data transmission cost will be in a square growth. To reduce the cost, we propose a distributed correlation estimation algorithm (DFCEA) for radiomics features based on DFCA. The DFCEA algorithm estimates the Pearson correlation coefficient using an iterative method, which can further reduce the I/O cost. The experiment proved that our algorithms are more effective compared to the algorithms in the literature. [ABSTRACT FROM AUTHOR]

Breast cancer is still the primary reason for women's mortality. It will be necessary to detect the micro-neoplasm signs, to assess the presence or the absence of malignancy precisely. In this context, transfer learning is an efficient resolution to the difficulties of deep network training, through small medical datasets, which can be computationally expensive and time-consuming. So, by leveraging prior knowledge gained from generic data (imagenet), transfer learning enables the formation of more performant, faster, and more generalizable models for the classification of ultrasound breast cancer data. Transfer learning overcomes the unsatisfactory performance of traditional approaches, benefitting from the knowledge encoded in pre-trained models, significantly reducing the training time and resource requirements for the explored task, resulting in an accurate conception of a convolutional neural network. In this work, we adopt this paradigm to extend the abilities of three convolutional neural network architectures in six models, to classify tumoral ultrasound breast data. This paper proposes an ultrasound breast tumoral classification for computer-aided diagnosis by a new adaptive pre-trained convolutional neural network models through fine-tuned VGG19, VGG16, and Inception V3 to analyse two public datasets: Egyptian ultrasound breast cancer BUSI, and Spanish US data, to discriminate mammary neoplasms from two distinguished populations. This adaptation is reached by refining the cited neural architectures, using partial fine-tuning and the total one into the training phase, by substituting the fully connected layer with several ones, and adding layers as a regularization, for the model's stability. The attaints results show that inception V3 is the most efficient model compared to VGG16 (accuracy of 97.38% and an F1-score of 97%), with an accuracy of 98.73%, an F1-score of 98.54%, a global sensitivity of 98.22%, and a total specificity of 98.23% to distinguish malignant versus benign pathologies and normal tissues in BUSI data. The best scores obtained on US data over inception V3, for the binary classification (malignant and benign lesions) provide an accuracy of 98%, a specificity of 98.2%, a sensitivity of 98%, and an F1-score of 98.24%. [ABSTRACT FROM AUTHOR]

Background The application of photoacoustic imaging (PAI), utilizing laser-induced ultrasound, shows potential in assessing blood oxygenation in breast nodules. However, its effectiveness in distinguishing between malignant and benign nodules remains insufficiently explored. Purpose This study aims to develop nomogram models for predicting the benign or malignant nature of breast nodules using PAI. Method A prospective cohort study enrolled 369 breast nodules, subjecting them to PAI and ultrasound examination. The training and testing cohorts were randomly divided into two cohorts in a ratio of 3:1. Based on the source of the variables, three models were developed, Model 1: photoacoustic-BIRADS+BMI + blood oxygenation, Model 2: BIRADS+Shape+Intranodal blood (Doppler) + BMI, Model 3: photoacoustic-BIRADS+BIRADS+ Shape+Intranodal blood (Doppler) + BMI + blood oxygenation. Risk factors were identified through logistic regression, resulting in the creation of three predictive models. These models were evaluated using calibration curves, subject receiver operating characteristic (ROC), and decision curve analysis. Results The area under the ROC curve for the training cohort was 0.91 (95% confidence interval, 95% CI: 0.88–0.95), 0.92 (95% CI: 0.89–0.95), and 0.97 (95% CI: 0.96–0.99) for Models 1–3, and the ROC curve for the testing cohort was 0.95 (95% CI: 0.91–0.98), 0.89 (95% CI: 0.83–0.96), and 0.97 (95% CI: 0.95–0.99) for Models 1–3. Conclusions The calibration curves demonstrate that the model's predictions agree with the actual values. Decision curve analysis suggests a good clinical application. Key message What is already known on this topic—   The application of PAI, utilizing laser-induced ultrasound, shows potential in assessing blood oxygenation in breast nodules. What this study adds—   This study marks a significant advancement in breast cancer diagnostics by developing and validating nomograms that combine PAI with traditional ultrasound, enhancing the accuracy of breast nodule classification. How this study might affect research, practice or policy—   This breakthrough addresses a critical research gap and is poised to revolutionize early and precise detection of breast cancer, potentially influencing future research, clinical practices, and screening policies. [ABSTRACT FROM AUTHOR]

Background: Hormone receptor-positive tumors are unlikely to exhibit a complete pathological tumor response. The association of CDK 4/6 inhibitor plus hormone therapy has changed this perspective. Case presentation: In this study, we retrospectively reviewed the charts of patients with a diagnosis of luminal A/B advanced/metastatic tumors treated with a CDK 4/6 inhibitor-based therapy. In this part of the study, we present clinical and ultrasound evaluation. Eight female patients were considered eligible for the study aims. Three complete and five partial responses were reported, including a clinical tumor response of 50% or more in five out of nine assessed lesions (55%). All patients showed a response on ultrasound. The mean lesion size measured by ultrasound was 27.1 ± 15.02 mm (range, 6-47 mm) at the baseline; 16.08 ± 14.6 mm (range, 0-40 mm) after 4 months (T1); and 11.7 ± 12.9 mm (range, 0-30 mm) at the 6 months follow-up (T2). Two patients underwent surgery. The radiological complete response found confirmation in a pathological complete response, while the partial response matched a moderate residual disease. Conclusion: The evaluation of breast cancer by ultrasound is basically informative of response and may be an easy and practical tool to monitor advanced tumors, especially in advanced/unfit patients who are reluctant to invasive exams. [ABSTRACT FROM AUTHOR]

Breast cancer is the leading cause of cancer mortality in women, and it is on the rise in Iran. Therefore, an early-stage diagnosis of breast cancer is of critical importance. Because ultrasound is one of the available, inexpensive, and minimally invasive techniques for distinguishing malignant from benign masses, a comparison of conventional ultrasound, color Doppler, and spectral Doppler findings can be useful. The purpose of this study was to determine the diagnostic value of sonographic indices, specifically Doppler parameters, in identifying the nature of breast masses. This is a cross-sectional study, with diagnostic value analysis. Before undergoing a biopsy, 80 patients with breast masses underwent B-mode and Doppler breast ultrasound. The ultrasound findings were then compared to pathologic results to determine which groups were malignant or benign. The resulting data were analyzed using statistical tests and diagnostic values with SPSS 22 software. B-mode grey-scale ultrasound indices such as mass shape, mass margin, mass orientation, and posterior features, as well as Doppler indices such as vascularity, RI (Resistive Index), PI (Pulsatility Index), and PSV (Peak Systolic Velocity), were found to be statistically significant with pathological findings. Color Doppler revealed vascularity in 65% of benign and 84% of malignant masses. The diagnostic value results revealed that mass shape, mass margin, mass orientation, and posterior features all play a significant role in predicting lesion malignancy, with a sensitivity of 92%, 58%, 64%, 56%, and specificity of 59%, 66%, 82%, and 84%, respectively. The RI, PI, and PSV indices were significantly higher in malignant masses, and all of them had remarkable diagnostic values in predicting malignancy, with a (Area Under The Curve) AUC of 0.863, 0.882, 0.702, a sensitivity of 84% and 84%, 68%, and a specificity of 83%, 86%, and 62%, respectively, at the optimal cut-off points (0.65, 1.32, 12.40) obtained from the Receiver Operating Characteristics (ROC) curves. [ABSTRACT FROM AUTHOR]

Background: To assess the morphology of breast masses with the help of routine ultrasound and elastography. To evaluate the role of elastography and conventional B - mode ultrasonography in differentiation of benign and malignant breast masses. To correlate elastography and B - mode ultrasonography results with the pathologic findings. A prospective observational study was done over a sample of 80 cases with 100 clinically palpable breasts breast lumps of the females who had undergone B-mode ultrasound and elastography of the breast. The final diagnosis was compared with the baseline data, sonographic features, a modified color score and mean strain ratio. Ultrasound had shown a sensitivity of 88.9%, specificity of 95.23%, positive predictive value (PPV) of 94.42 % and negative predictive value (NPV) of 90.45% and overall diagnostic accuracy of 92.67 % was noted. New modified dual color score had shown sensitivity of 96.4%, specificity of 85.78%, positive predictive value of 85.92% and negative predictive value of 86.87 % and a diagnostic accuracy of 91.67%. The risk to miss a malignant case with new modified dual color score was 2.2%. Mean strain ratio had shown a sensitivity of 100%, specificity of 97.27%; positive predictive value being 96.78 % and negative predictive value is 99.67%, the diagnostic accuracy is 99.23 %. This study shows the definitive nature of identifying the possible breast malignancies thus preventing the invasive procedures which are not the required in the situation. [ABSTRACT FROM AUTHOR]

AIM: To determine the role of ultrasound elastography of breast mass lesions. Methods: A prospective research was conducted at the radiology department of Continental Hospitals in Nanakaramguda, Hyderabad. 50 women with breast edema verified by ultrasonography were part in this research. Results: Out of the 50 individuals in the research, 26 (52%) exhibited benign characteristics, whereas 24 (48%) had malignant characteristics. Out of the 50 patients in the research, 22 (44%) exhibited benign characteristics and 28 (56%) exhibited malignant characteristics as per elastography results. In a study of 50 patients, ultrasonography revealed malignancy signs in 24 patients (48%). Subsequent elastography conducted on these 24 patients indicated malignant features in 23 of them (95.8%). Out of 26 patients in the study, 52% exhibited benign characteristics on ultrasound. Among these 26 patients, 21 (80.7%) showed benign features on elastography. When comparing ultrasound and elastography findings in these 50 patients, the sensitivity for detecting malignant lesions was 82% and the specificity was 95%, resulting in an overall accuracy rate of 88%. Conclusion: We found that using elastography in conjunction with conventional B-mode ultrasonography enhances the diagnostic accuracy in differentiating between benign and malignant breast lesions, leading to a significant reduction in needless biopsies for benign lesions. [ABSTRACT FROM AUTHOR]