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This study aimed to investigate the effect of ultrasound combined with highland barley dietary fiber (HBDF) on the quality of reduced‐salt chicken breast myofibrillar protein (MP) gel. The molecular forces maintaining gel structure, the gelling formation process, and gel microstructure of different groups, two control groups (2% sodium chloride [NaCl] group, 1% NaCl group), and four treatment groups (0.3% HBDF+U5, 0.3% HBDF+U10, 0.5% HBDF+U5, and 0.5% HBDF+U10) were examined. Results indicated significant improvements (p < 0.05) in gel properties such as water‐holding capacity, textural characteristics, and color of the MP gel of the four treatment groups compared to Control 2 (1% NaCl) group. Furthermore, the second structural alterations were characterized by increase β‐sheet, β‐turn, and random coil structure contents in treatment groups, especially in 0.3% HBDF+U5 and 0.5% HBDF+U5 groups; in addition, the exposure of more hydrophobic groups and the formation of disulfide bonds and hydrogen bonds were promoted in treatment groups, thus enhancing protein aggregation and gel quality. Finally, compared to Control 2 (1% NaCl) group, more compact and uniform gel network structures and pores inside the composite gels were observed in treatment groups. In conclusion, the findings demonstrated that the application of ultrasound in combination with HBDF improved the gelling characteristics of reduced‐salt chicken breast MP gel, especially 0.3% HBDF+U5 and 0.5% HBDF+U5 groups. [ABSTRACT FROM AUTHOR]

Breast cancer is the most common cancer in women. Breast masses are one of the distinctive signs for diagnosing breast cancer, and ultrasound is widely used for screening as a non-invasive and effective method for breast examination. In this study, we used the Mendeley and BUSI datasets, comprising 250 images (100 benign, 150 malignant) and 780 images (133 normal, 487 benign, 210 malignant), respectively. The datasets were split into 80% for training and 20% for validation. The accurate measurement and characterization of different breast tumors play a crucial role in guiding clinical decision-making. The area and shape of the different breast tumors detected are critical for clinicians to make accurate diagnostic decisions. In this study, a deep learning method for mass segmentation in breast ultrasound images is proposed, which uses densely connected U-net with attention gates (AGs) as well as channel attention modules and scale attention modules for accurate breast tumor segmentation.The densely connected network is employed in the encoding stage to enhance the network's feature extraction capabilities. Three attention modules are integrated in the decoding stage to better capture the most relevant features. After validation on the Mendeley and BUSI datasets, the experimental results demonstrate that our method achieves a Dice Similarity Coefficient (DSC) of 0.8764 and 0.8313, respectively, outperforming other deep learning approaches. The source code is located at github.com/zhanghaoCV/plos-one. [ABSTRACT FROM AUTHOR]

This is a summary of a consensus statement on the introduction of "Ultrasound microvasculomics" produced by The Chinese Artificial Intelligence Alliance for Thyroid and Breast Ultrasound. The evaluation of microvessels is a very important part for the assessment of diseases. Super-resolution ultrasound (SRUS) microvascular imaging surpasses traditional ultrasound imaging in the morphological and functional analysis of microcirculation. SRUS microvascular imaging relies on contrast microbubbles to gain sensitivity to microvessels and improves the spatial resolution of ultrasound blood flow imaging for a more detailed depiction of vascular structures and hemodynamics. This method has been applied in preclinical animal models and pilot clinical studies, involving areas including neurology, oncology, nephrology, and cardiology. However, the current quantitative parameters of SRUS images are not enough for precise evaluation of microvessels. Therefore, by employing omics methods, more quantification indicators can be obtained, enabling a more precise and personalized assessment of microvascular status. Ultrasound microvasculomics - a high-throughput extraction of image features from SRUS images - is one novel approach that holds great promise but needs further validation in both bench and clinical settings. Clinical relevance statement: Super-resolution Ultrasound (SRUS) blood flow imaging improves spatial resolution. Ultrasound microvasculomics is possible to acquire high-throughput information of features from SRUS images. It provides more precise and abundant micro-blood flow information in clinical medicine. Key Points: This consensus statement reviews the development and application of super-resolution ultrasound (SRUS). The shortcomings of the current quantification indicators of SRUS and strengths of the omics methodology are addressed. "Ultrasound microvasculomics" is introduced for a high-throughput extraction of image features from SRUS images. [ABSTRACT FROM AUTHOR]

Objectives: To evaluate the performance of ultrasound-based deep learning (DL) models in distinguishing breast phyllodes tumours (PTs) from fibroadenomas (FAs) and their clinical utility in assisting radiologists with varying diagnostic experiences. Methods: We retrospectively collected 1180 ultrasound images from 539 patients (247 PTs and 292 FAs). Five DL network models with different structures were trained and validated using nodule regions annotated by radiologists on breast ultrasound images. DL models were trained using the methods of transfer learning and 3-fold cross-validation. The model demonstrated the best evaluation index in the 3-fold cross-validation was selected for comparison with radiologists' diagnostic decisions. Two-round reader studies were conducted to investigate the value of DL model in assisting 6 radiologists with different levels of experience. Results: Upon testing, Xception model demonstrated the best diagnostic performance (area under the receiver-operating characteristic curve: 0.87; 95% CI, 0.81-0.92), outperforming all radiologists (all P < .05). Additionally, the DL model enhanced the diagnostic performance of radiologists. Accuracy demonstrated improvements of 4%, 4%, and 3% for senior, intermediate, and junior radiologists, respectively. Conclusions: The DL models showed superior predictive abilities compared to experienced radiologists in distinguishing breast PTs from FAs. Utilizing the model led to improved efficiency and diagnostic performance for radiologists with different levels of experience (6-25 years of work). Advances in knowledge: We developed and validated a DL model based on the largest available dataset to assist in diagnosing PTs. This model has the potential to allow radiologists to discriminate 2 types of breast tumours which are challenging to identify with precision and accuracy, and subsequently to make more informed decisions about surgical plans. [ABSTRACT FROM AUTHOR]

The aim of this meta-analysis was to assess the diagnostic performance of deep learning (DL) and ultrasound in breast cancer diagnosis. Additionally, we categorized the included studies into two subgroups: B-mode ultrasound diagnostic subgroup and multimodal ultrasound diagnostic subgroup, and compared the performance differences of DL algorithms in breast cancer diagnosis using only B-mode ultrasound or multimodal ultrasound. We conducted a comprehensive search for relevant studies published from January 01, 2017 to July 31, 2023 in the MEDLINE and EMBASE databases. The quality of the included studies was evaluated using the QUADAS-2 tool and radiomics quality scores (RQS). Meta-analysis was performed using R software. Inter-study heterogeneity was assessed by I^2 values and Q-test P-values, with sources of heterogeneity analyzed through a random effects model based on test results. Summary receiver operating characteristics (SROC) curves were used for meta-analysis across multiple trials, while combined sensitivity, specificity, and AUC were calculated to quantify prediction accuracy. Subgroup analysis and sensitivity analyses were also conducted to identify potential sources of study heterogeneity. Publication bias was assessed using the funnel plot method. (PROSPERO identifier: CRD42024545758). The 20 studies included a total of 14,955 cases, with 4197 cases used for model testing. Among these cases were 1582 breast cancer patients and 2615 benign or other breast lesions. The combined sensitivity, specificity, and AUC values across all studies were found to be 0.93, 0.90, and 0.732, respectively. In subgroup analysis, the multimodal subgroup demonstrated superior performance with combined sensitivity, specificity, and AUC values of 0.93, 0.88, and 0.787, respectively; whereas the combined sensitivity, specificity, and AUC value for the model B subgroup was at a level of 0.92, 0.91, and 0.642, respectively. The integration of DL with ultrasound demonstrates high accuracy in the adjunctive diagnosis of breast cancer, while the fusion of DL and multimodal breast ultrasound exhibits superior diagnostic efficacy compared to B-mode ultrasound alone. • The application of deep learning in the diagnosis of breast cancer is promising. • Deep learning-based computer-aided detection demonstrates high accuracy. • Multimodal breast ultrasound is expected to enhance the diagnostic accuracy. [ABSTRACT FROM AUTHOR]

To assess the capabilities of large language models (LLMs), including Open AI (GPT-4.0) and Microsoft Bing (GPT-4), in generating structured reports, the Breast Imaging Reporting and Data System (BI-RADS) categories, and management recommendations from free-text breast ultrasound reports. In this retrospective study, 100 free-text breast ultrasound reports from patients who underwent surgery between January and May 2023 were gathered. The capabilities of Open AI (GPT-4.0) and Microsoft Bing (GPT-4) to convert these unstructured reports into structured ultrasound reports were studied. The quality of structured reports, BI-RADS categories, and management recommendations generated by GPT-4.0 and Bing were evaluated by senior radiologists based on the guidelines. Open AI (GPT-4.0) was better than Microsoft Bing (GPT-4) in terms of performance in generating structured reports (88% vs. 55%; p < 0.001), giving correct BI-RADS categories (54% vs. 47%; p = 0.013) and providing reasonable management recommendations (81% vs. 63%; p < 0.001). As the ability to predict benign and malignant characteristics, GPT-4.0 performed significantly better than Bing (AUC, 0.9317 vs. 0.8177; p < 0.001), while both performed significantly inferior to senior radiologists (AUC, 0.9763; both p < 0.001). This study highlights the potential of LLMs, specifically Open AI (GPT-4.0), in converting unstructured breast ultrasound reports into structured ones, offering accurate diagnoses and providing reasonable recommendations. [ABSTRACT FROM AUTHOR]

We investigate the predictive value of a comprehensive model based on preoperative ultrasound radiomics, deep learning, and clinical features for pathological complete response (pCR) after neoadjuvant chemotherapy (NAC) for the breast cancer. We enrolled 155 patients with pathologically confirmed breast cancer who underwent NAC. The patients were randomly divided into the training set and the validation set in the ratio of 7:3. The deep learning and radiomics features of pre-treatment ultrasound images were extracted, and the random forest recursive elimination algorithm and the least absolute shrinkage and selection operator were used for feature screening and DL-Score and Rad-Score construction. According to multifactorial logistic regression, independent clinical predictors, DL-Score, and Rad-Score were selected to construct the comprehensive prediction model DLRC. The performance of the model was evaluated in terms of its predictive effect, and clinical practicability. Compared to the clinical, radiomics (Rad-Score), and deep learning (DL-Score) models, the DLRC accurately predicted the pCR status, with an area under the curve (AUC) of 0.937 (95%CI: 0.895–0.970) in the training set and 0.914 (95%CI: 0.838–0.973) in the validation set. Moreover, decision curve analysis confirmed that the DLRC had the highest clinical value among all models. The comprehensive model DLRC based on ultrasound radiomics, deep learning, and clinical features can effectively and accurately predict the pCR status of breast cancer after NAC, which is conducive to assisting clinical personalized diagnosis and treatment plan. [ABSTRACT FROM AUTHOR]

Summary Imaging research pathways focus increasingly on the development of individualised approaches to breast cancer detection, diagnosis and management. Detection of breast cancer with X‐ray mammography may fail in some cancer subtypes with limited changes in morphology/tissue density and in women with dense breasts. International organisations offer recommendations for contrast‐enhanced breast imaging, as it provides superior sensitivity for screening, local staging and assessment of neoadjuvant treatment response, when compared with standard X‐ray mammography (including tomosynthesis) and breast ultrasound. Arguably, the evidence base is stronger for contrast‐enhanced MRI (CE‐MRI). Unfortunately, patient access to breast MRI in rural and remote areas is limited by practical limitations and equipment licensing restrictions. Moreover, breast MRI is an expensive test, likely to be out of reach for many women. Contrast‐enhanced mammography (CEM) offers an attractive alternative to improve patient access to functional breast imaging. It is a new type of digital, dual energy X‐ray mammography that can be performed on most modern units, following a relatively inexpensive hard‐ and software upgrade. In this paper, we review the rapidly accumulating evidence that CEM can provide similar diagnostic accuracy to CE‐MRI, though at a significantly lower cost and offering greater comfort to the patient. The adoption of CEM can help meet the anticipated increased demand for CE‐MRI. [ABSTRACT FROM AUTHOR]

Background: To assess the reliability and comprehensibility of breast radiology reports simplified by artificial intelligence using the large language model (LLM) ChatGPT-4o. Methods: A radiologist with 20 years' experience selected 21 anonymized breast radiology reports, 7 mammography, 7 breast ultrasound, and 7 breast magnetic resonance imaging (MRI), categorized according to breast imaging reporting and data system (BI-RADS). These reports underwent simplification by prompting ChatGPT-4o with "Explain this medical report to a patient using simple language". Five breast radiologists assessed the quality of these simplified reports for factual accuracy, completeness, and potential harm with a 5-point Likert scale from 1 (strongly agree) to 5 (strongly disagree). Another breast radiologist evaluated the text comprehension of five non-healthcare personnel readers using a 5-point Likert scale from 1 (excellent) to 5 (poor). Descriptive statistics, Cronbach's α, and the Kruskal–Wallis test were used. Results: Mammography, ultrasound, and MRI showed high factual accuracy (median 2) and completeness (median 2) across radiologists, with low potential harm scores (median 5); no significant group differences (p ≥ 0.780), and high internal consistency (α > 0.80) were observed. Non-healthcare readers showed high comprehension (median 2 for mammography and MRI and 1 for ultrasound); no significant group differences across modalities (p = 0.368), and high internal consistency (α > 0.85) were observed. BI-RADS 0, 1, and 2 reports were accurately explained, while BI-RADS 3–6 reports were challenging. Conclusion: The model demonstrated reliability and clarity, offering promise for patients with diverse backgrounds. LLMs like ChatGPT-4o could simplify breast radiology reports, aid in communication, and enhance patient care. Relevance statement: Simplified breast radiology reports generated by ChatGPT-4o show potential in enhancing communication with patients, improving comprehension across varying educational backgrounds, and contributing to patient-centered care in radiology practice. Key Points: AI simplifies complex breast imaging reports, enhancing patient understanding. Simplified reports from AI maintain accuracy, improving patient comprehension significantly. Implementing AI reports enhances patient engagement and communication in breast imaging. [ABSTRACT FROM AUTHOR]

Background: Breast cancer (BC) is the most common malignancy in women and the second cause of cancer death. In recent years, there has been a strong development in artificial intelligence (AI) applications in medical imaging for several tasks. Our aim was to evaluate the potential of transfer learning with convolutional neural networks (CNNs) in discriminating suspicious breast lesions on ultrasound images. Methods: Transfer learning performances of five different CNNs (Inception V3, Xception, Densenet121, VGG 16, and ResNet50) were evaluated on a public and on an institutional dataset (526 and 392 images, respectively), customizing the top layers for the specific task. Institutional images were contoured by an expert radiologist and processed to feed the CNNs for training and testing. Postimaging biopsies were used as a reference standard for classification. The area under the receiver operating curve (AUROC) was used to assess diagnostic performance. Results: Networks performed very well on the public dataset (AUROC 0.938–0.996). The direct generalization to the institutional dataset resulted in lower performances (max AUROC 0.676); however, when tested on BI-RADS 3 and BI-RADS 5 only, results were improved (max AUROC 0.792). Good results were achieved on the institutional dataset (AUROC 0.759–0.818) and, when selecting a threshold of 2% for classification, a sensitivity of 0.983 was obtained for three of five CNNs, with the potential to spare biopsy in 15.3%–18.6% of patients. Conclusion: In conclusion, transfer learning with CNNs may achieve high sensitivity and might be used as a support tool in managing suspicious breast lesions on ultrasound images. Relevance statement: Transfer learning is a powerful technique to exploit the performances of well-trained CNNs for image classification. In a clinical scenario, it might be useful for the management of suspicious breast lesions on breast ultrasound, potentially sparing biopsy in a non-negligible number of patients. Key Points: Properly trained CNNs with transfer learning are highly effective in differentiating benign and malignant lesions on breast ultrasound. Setting clinical thresholds increased sensitivity. CNNs might be useful as support tools in managing suspicious lesions on breast ultrasound. [ABSTRACT FROM AUTHOR]

Introduction: Cancers of unknown primary (CUPs) present a diagnostic challenge as their origin is unidentified at diagnosis. Massive pleural effusion (MPE), indicative of lung metastasis in CUP, categorizes the condition into an unfavorable subset with a poor prognosis. Patients in this subset may exhibit a lower response to specific therapies. Case Presentation: A 62-year-old woman presented with cough, severe dyspnea, and MPE in the left lung. Thoracocentesis was performed, extracting 1,200 mL of hemorrhagic fluid, followed by the placement of an indwelling pleural catheter. The cytological examination of the pleural effusion indicated an adenocarcinoma, with immunohistochemistry revealing positive CK7 and negative CK20, Napsin A, and TTF-1. Additionally, elevated levels of Ca-125 (1,605 U/mL) and Ca 15-3 (242 U/mL) raised suspicion of gynecological malignancy. Thorax and abdominal CT scans, breast and thyroid ultrasounds showed no signs of malignancy, leading to the diagnosis of CUP. The patient’s performance status according to the Eastern Cooperative Oncology Group (ECOG) score was 4. Initial carboplatin 5 AUC and paclitaxel 175 mg/m2 administration resulted in improvement in performance status with ECOG score of 1, alleviation of dyspnea, reduction in pleural effusion 1 week after chemotherapy, with minimal effusion observed at 3 weeks, and Ca-125 levels decreased to 33.6 U/mL thereafter. Discussion: Empiric chemotherapy using carboplatin and paclitaxel is a feasible option for managing CUP with MPE mimicking gynecological malignancies with elevated Ca-125 and Ca 15-3 markers; initiating chemotherapy in poor performance status patients is beneficial with proper clinical judgment. [ABSTRACT FROM AUTHOR]

Background: It is challenging to correctly identify and diagnose breast nonmass lesions. This study aimed to explore the multimodal ultrasound features associated with malignant breast nonmass lesions (NMLs), and evaluate their combined diagnostic performance. Methods: This retrospective analysis was conducted on 573 breast NMLs, including 309 were benign and 264 were malignant, their multimodal ultrasound features (B-mode, color Doppler and strain elastography) were assessed by two experienced radiologists. Univariate and multivariate logistic regression analysises were used to explore multimodal ultrasound features associated with malignancy, and a nomogram was developed. Diagnostic performance and clinical utility were evaluated and validated by the receiver operating characteristic (ROC) curve, calibration curve and decision curve in the training and validation cohorts. Results: Multimodal ultrasound features including linear (odds ratio [OR] = 4.69) or segmental distribution (OR = 7.67), posterior shadowing (OR = 3.14), calcification (OR = 7.40), hypovascularity (OR = 0.38), elasticity scored 4 (OR = 7.00) and 5 (OR = 15.77) were independent factors associated with malignant breast NMLs. The nomogram based on these features exhibited diagnostic performance in the training and validation cohorts were comparable to that of experienced radiologists, with superior specificity (89.4%, 89.5% vs. 81.2%) and positive predictive value (PPV) (89.2%, 90.4% vs. 82.4%). The nomogram also demonstrated good calibration in both training and validation cohorts (all P > 0.05). Decision curve analysis indicated that interventions guided by the nomogram would be beneficial across a wide range of threshold probabilities (0.05-1 in the training cohort and 0.05–0.93 in the validation cohort). Conclusions: The combined use of linear or segmental distribution, posterior shadowing, calcification, hypervascularity and high elasticity score, displayed as a nomogram, demonstrated satisfied diagnostic performance for malignant breast NMLs, which may contribute to the imaging interpretation and clinical management of tumors. [ABSTRACT FROM AUTHOR]

Simple Summary: The aim of this study was to assess the impact of various factors—such as age, age at menarche, age at first childbirth, number of births, body mass index (BMI), living environment (rural vs. urban), and other factors later discussed—on the likelihood of developing breast cancer. This article's original research was based on data gathered from 687 breast biopsies, which were analyzed through comparative analysis and regression analysis to identify possible predictors for malignancy and tumor size. The comparative analysis and regression models in this study provide deeper insights into the roles of various variables, particularly positive hormonal receptor status, in malignancy. Introduction: Breast cancer has become one of the most serious and widespread public health concerns globally, affecting an increasing number of women—and, in rare cases, men—across the world. It is the most common cancer among women across all countries. In this study, we aimed to evaluate the influence of demographic factors, medical and reproductive history, diagnostic techniques, and hormone receptor status on the development and progression of breast cancer. Materials and Methods: A total of 687 female patients from Romania underwent standard breast examination techniques, including clinical breast examination, mammography, ultrasonography, and, ultimately, breast biopsy. Statistical analysis was performed using the R programming language and RStudio software. The study included a comparative analysis and a prediction analysis for malignancy and tumor size (cumulative histological dimension) through logistic and linear regression models. Results: The comparative analysis identified several variables associated with malignancy: older age (p < 0.001), non-vulnerability (p = 0.04), no daily physical activity (p = 0.002), no re-biopsy (p < 0.001), immunohistochemistry use (p < 0.001), use of larger gauge needles (p < 0.001), ultrasound-guided biopsy (p < 0.001), and vacuum biopsy (p < 0.001). The hormone receptor statuses—estrogen receptor (ER), progesterone receptor (PR), and androgen receptor (AR)—showed statistically significant differences in distribution across breast cancer B classifications. Logistic regression analysis identified ER, PR, and age as significant predictors of malignancy. Linear regression analysis revealed histopathological results, living environment, geographical region, vulnerability, prior breast examination, and the number of histological fragments as significant predictors of cumulative histological dimension. Conclusions: Our predictive models demonstrate the impact of demographic factors, medical history, diagnostic techniques, and hormone receptor status on breast cancer development and progression, accounting for a significant portion of the variance in malignancy and cumulative histological dimension. [ABSTRACT FROM AUTHOR]

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]

To investigate the conventional ultrasound (US), contrast-enhanced ultrasound (CEUS) manifestations and the corresponding histopathological characteristics of patients diagnosed with breast encapsulated papillary carcinoma (EPC) and to explore the value of CEUS in diagnosis of EPC.The clinical, pathological, US, and CEUS features of 16 patients (17 lesions) with EPC confirmed by postoperative histopathology were retrospectively analyzed.EPC was prevalent in the postmenopausal women. The majority of conventional US images of EPC showed complex cystic and solid masses with circumscribed margins (70.6%), enhanced posterior echo (94.1%), no sonographic calcification (88.2%), rich blood flow in the solid components within lesions (70.6%) on Color Doppler flow imaging, and high resistance index of blood flow (94.1%). Moreover, CEUS showed mainly centripetal hyperenhancement of the solid components within the lesions with irregular outline, and the enhancement area of the whole masses was essentially the same as the B-mode US area.EPC typically presents as a complex cystic and solid mass. CEUS is helpful to clarify the extent of the solid component and facilitate preoperative core-needle biopsy. A comprehensive evaluation by CEUS is valuable for diagnosing EPC and combining it with clinical features are helpful to further improve the diagnosis of this rare kind of breast cancer. [ABSTRACT FROM AUTHOR]

Rosai–Dorfman disease (RDD) is a rare subtype of non–Langerhans cell histiocytosis. It is a benign disease with variable presentations, with breast involvement being an uncommon manifestation. In this paper, we present a case of breast RDD in a 59‐year‐old Chinese woman who presented with a painless, enlarging mass in her right breast, accompanied by skin changes and no palpable axillary lymph node. A mammogram and breast ultrasonography revealed a progressively enlarging lesion with a suspicious axillary lymph node, leading to an upgrade from Breast Imaging Reporting and Data System (BIRADS) score of 3 to 4A on interval imaging. Core biopsy of the breast mass and cytology of the right axillary lymph node confirmed extranodal RDD. After a 5‐year period of observation, excision was performed due to persistent symptoms. Local recurrence of RDD, with skin discolouration and thickening, was confirmed by skin biopsy 1 year and 9 months after the operation. To the best of our knowledge, this is the first reported case of breast RDD in Hong Kong. [ABSTRACT FROM AUTHOR]

Background: In this study, we wanted to evaluate the performance of sonomammography in distinguishing between benign and malignant breast cancers and to confirm the findings through histology. An attempt was made to diagnose and categorize the lesions using BI-RADS terminology in ultrasonography. Methods: This was a hospital based prospective study conducted among 100 female patients with symptoms affecting the breast who underwent sonomammographic imaging, at Shri B.M Patil Medical College, Hospital and Research Centre, over a period of 2 years from September 2022 to April 2024, after obtaining clearance from institutional ethics committee and written informed consent from the study participants. Results: 100 patients were selected for analysis, presenting with 118 masses. Out of them, 36 patients had malignancy, and 82 were benign. USG successfully differentiated between benign and malignant masses when the BI-RADS lexicon was incorporated with a sensitivity of 100% and a specificity of 97.56% for malignant masses. Conclusion: Good sensitivity and specificity are demonstrated by sonomammography(SMG) in identifying all breast lesions. As such, it may be deemed a more appropriate method of inquiry than mammography. For individuals under 40 years of age, it can serve as the primary screening method for breast lumps. Breast lesions can be better understood and managed with the help of BI-RADS classification. The diagnostic accuracy of SMG can be further improved by combining it with complementary imaging modalities. Multimodality approach is more sensitive than any single modality alone. Unnecessary interventions can be avoided in unequivocally benign lesions on SMG. USG can be used as a good screening test for the detection and differentiation of benign and malignant masses. Hence, it helps in early detection, early treatment and favorable outcome. [ABSTRACT FROM AUTHOR]

Background/aim: Breast cancer is the leading cause of death from cancer for women in age group of 20-59 years. It is responsible for 15% of the cancer related deaths in women. Our study attempts to assess the axillary lymph node status in carcinoma breast using combined clinical and radiological examination. Materials and methods: A prospective study was conducted using 82 patients with proven cases of carcinoma breast over a period of 2 years. Patients were subjected to clinical examination and ultrasonography of axilla. Then the histopathological reports of the axillary lymph nodes were studied following surgery. The accuracy of clinical and radiological examination have been assessed with the histopathological reports of the lymph nodes. Results: It was noted that clinical examination has a sensitivity of 53.52% and specificity of 100% in detecting axillary lymph node metastasis, with a PPV of 100%, NPV of 25%, chi square value of 10.792 and a p -value of 0.001 which is statistically significant. However, ultrasonography of axilla has a sensitivity of 77.46% and specificity of 100% in detecting lymph node metastasis, with a PPV of 100%, NPV of 40.74%, chi square value of 25.879 and a p-value of 0.001 which is statistically significant. Conclusion: Ultasonography is a non invasive, safe, easily available, less expensive and a reliable method of assessing the lymphatic spread of carcinoma breast to the axilla. Axillary lymph node status assessment is an important step in staging and management of carcinoma breast. In our study, it is highly specific and has got good sensitivity. Further studies have to be conducted on a larger scale to set axillary ultrasound as the standard investigation in assessing the axillary nodal involvement in carcinoma breast. [ABSTRACT FROM AUTHOR]

Background: In India, breast carcinoma is the 2nd most common malignancy in women. Incidence increases every year which ranges approximately 20-32%. Incidence of breast cancer shows "AGE SHIFT" over the past twenty years in India that is incidence increased from 6 to 17% in the age group of 30-40 years. The 'Modified Triple Test' utilizes-Physical Examination, Ultrasonography of the breast as the radiological method, and FNAC for the diagnosis of palpable breast lumps. It is gaining acceptance with the recent advances in technology and refinements in the interpretative criteria of sonographically characterized masses. Methods A prospective cross sectional study of 50 female patients attending the out patient department of General Surgery, K.R. Hospital, Mysore, between March 2022 to January 2024, with the complaint of a palpable lump/lumps in the breast was undertaken. Results Most commonly affected age group being 30-39 years. Incidence increased from 7 to 16% between the age group of 30 and 40 years. The calculated P - value for parity study is 0.017 and it is shown that malignancy is common among nulliparous. For the menstrual status, the calculated p-value is 0.044 is significant. The breast tumor is common at left side account for about overall 54% age. Fibroadenoma is the most common benign tumor. It was inferred from the study that clinical examination has a sensitivity of 87.5% which means that around 12.5% of the breast carcinoma cases are missed clinically. Results of Modified Triple Test were Sensitivity: 100%, Specificity: 97.37%, PPV: 91.67%, NPV: 100%. Hence the modified triple test can be used as a reliable and fairly accurate means of diagnosis of breast carcinoma. Conclusion Our results show that the diagnostic accuracy of physical examination breast USG and FNA/CNB combined is comparable to that of histological examination. Ultrasound replacing mammography serve as an effective imaging modality in palpable breast lumps and is also more comprehensive. CNB is a suitable alternative when FNA is inconclusive, also offers additional information. Thus the use of MTT to complement findings in differential diagnosis of a lesion in a symptomatic women seeking medical care deserves acceptance and further evalution. [ABSTRACT FROM AUTHOR]

Objective: To determine whether adding elastography strain ratio (SR) and a deep learning based computer-aided diagnosis (CAD) system to breast ultrasound (US) can help reclassify Breast Imaging Reporting and Data System (BI-RADS) 3 and 4a-c categories and avoid unnecessary biopsies. Methods: This prospective, multicentre study included 1049 masses (691 benign, 358 malignant) with assigned BI-RADS 3 and 4a-c between 2020 and 2022. CAD results was dichotomized possibly malignant vs. benign. All patients underwent SR and CAD examinations and histopathological findings were the standard of reference. Reduction of unnecessary biopsies (biopsies in benign lesions) and missed malignancies after reclassified (new BI-RADS 3) with SR and CAD were the outcome measures. Results: Following the routine conventional breast US assessment, 48.6% (336 of 691 masses) underwent unnecessary biopsies. After reclassifying BI-RADS 4a masses (SR cut-off <2.90, CAD dichotomized possibly benign), 25.62% (177 of 691 masses) underwent an unnecessary biopsies corresponding to a 50.14% (177 vs. 355) reduction of unnecessary biopsies. After reclassification, only 1.72% (9 of 523 masses) malignancies were missed in the new BI-RADS 3 group. Conclusion: Adding SR and CAD to clinical practice may show an optimal performance in reclassifying BI-RADS 4a to 3 categories, and 50.14% masses would be benefit by keeping the rate of undetected malignancies with an acceptable value of 1.72%. Advances in knowledge: Leveraging the potential of SR in conjunction with CAD holds immense promise in substantially reducing the biopsy frequency associated with BI-RADS 3 and 4A lesions, thereby conferring substantial advantages upon patients encompassed within this cohort. [ABSTRACT FROM AUTHOR]

High-Intensity Focused Ultrasound (HIFU) as a promising and impactful modality for breast tumor ablation, entails the precise focalization of high-intensity ultrasonic waves onto the tumor site, culminating in the generation of extreme heat, thus ablation of malignant tissues. In this paper, a comprehensive three-dimensional (3D) Finite Element Method (FEM)-based numerical procedure is introduced, which provides exceptional capacity for simulating the intricate multiphysics phenomena associated with HIFU. Furthermore, the application of numerical procedures to an anatomically realistic breast phantom (ARBP) has not been explored before. The integrity of the present numerical procedure has been established through rigorous validation, incorporating comparative assessments with previous two-dimensional (2D) simulations and empirical data. For ARBP ablation, the administration of a 0.1 MPa pressure input pulse at a frequency of 1.5 MHz, sustained at the focal point for 10 seconds, manifests an ensuing temperature elevation to 80°C. It is noteworthy that, in contrast, the prior 2D simulation using a 2D phantom geometry reached just 72°C temperature under the identical treatment regimen, underscoring the insufficiency of 2D models, ascribed to their inherent limitations in spatially representing acoustic energy, which compromises their overall effectiveness. To underscore the versatility of this numerical platform, a simulation of a more clinically relevant HIFU therapy procedure has been conducted. This scenario involves the repositioning of the ultrasound focal point to three separate lesions, each spaced at 3 mm intervals, with ultrasound exposure durations of 6 seconds each and a 5-second interval for movement between focal points. This approach resulted in a more uniform high-temperature distribution at different areas of the tumour, leading to the ablation of almost all parts of the tumour, including its verges. In the end, the effects of different abnormal tissue shapes are investigated briefly as well. For solid mass tumors, 67.67% was successfully ablated with one lesion, while rim-enhancing tumors showed only 34.48% ablation and non-mass enhancement tumors exhibited 20.32% ablation, underscoring the need for multiple lesions and tailored treatment plans for more complex cases. [ABSTRACT FROM AUTHOR]

Nonmass lesions in breast ultrasound (US) are areas of altered echogenicity without definite margins or mass effect. However, these lesions may show calcifications, associated architectural distortion, or shadowing just like masses. They vary in their echogenicity, distribution, ductal or nonductal appearance and the associated features that can be seen in variety of benign and malignant pathologies. With no uniform definition or classification system, there is no standardized approach in further risk categorization and management strategies of these lesions. Malignant nonmass lesions are not uncommon and few sonographic features can help in differentiating benign and malignant pathologies. US-guided tissue sampling or lesion localization can be preferred in the nonmass lesions identified on second look US after magnetic resonance imaging or mammography. This article aims to describe various imaging patterns and attempts to provide an algorithmic approach to nonmass findings on breast US. [ABSTRACT FROM AUTHOR]

Granular cell tumors of breast are rare neoplasms, majority of which are benign. Their imaging appearances are often indistinguishable from breast cancer. They may demonstrate a few differentiating features and unlike breast cancer, typically follow a benign course. Histopathology and immunochemistry form the cornerstone of diagnosis of granular cell tumor. In this article, we presented a series of three cases of granular cell tumors with variable presentations. Our goal is to increase familiarity for these neoplasms and for the readers to be able to distinguish them from the more common entity of breast cancer, as their prognosis and management differ. [ABSTRACT FROM AUTHOR]

Segmentation of tumors in the ultrasound (US) images of the breast is a critical problem in medical imaging. Due to the poor quality of the US images and varying specifications of the US machines, the segmentation and classification of the abnormalities present difficulties even for trained radiologists. Nevertheless, the US remains one of the most reliable and inexpensive tests. Recently, an artificial life (ALife) model based on tracing agents and fusion of the US and the elasticity images (F-ALife) has been proposed and analyzed. Under certain conditions, F-ALife outperforms state-of-the-art including the selected deep learning (DL) models, deformable models, machine learning, contour grouping and superpixels. Apart from the improved accuracy, F-ALife requires smaller training sets. The strongest competitors of the F-ALife are hybrids of the DL with conventional models. However, the current DL methods require a large amount of data (thousands of annotated images), which often is not available. Moreover, the hybrids require that the conventional model is properly integrated into the DL. Therefore, we offer a new DL-based hybrid with ALife. It is characterized by a high accuracy, requires a relatively small dataset, and is capable of handling previously unseen data. The new ideas include (1) a special image mask to guide ALife. The mask is generated using DL and the distance transform, (2) modification of ALife for segmentation of the US images providing a high accuracy. (These ideas are motivated by the "vehicles" of Braitenberg (Vehicles, experiments in synthetic psychology, MIT Press, Cambridge, 1984) and ALife proposed in Karunanayake et al. (Pattern Recognit 108838, 2022), (3) a two-level genetic algorithm which includes training by an individual image and by the entire set of images. The training employs an original categorization of the images based on the properties of the edge maps. The efficiency of the algorithm is demonstrated on complex tumors. The method combines the strengths of the DL neural networks with the speed and interpretability of ALife. The tests based on the characteristics of the edge map and complexity of the tumor shape show the advantages of the proposed DL-ALife. The model outperforms 14 state-of-the-art algorithms applied to the US images characterized by a complex geometry. Finally, the novel classification allows us to test and analyze the limitations of the DL for the processing of the unseen data. The code is applicable to breast cancer diagnostics (Automated Breast Ultra Sound), US-guided biopsies as well as to projects related to automatic breast scanners. A video demo is at https://tinyurl.com/3xthedff. [ABSTRACT FROM AUTHOR]

Objectives: To evaluate the use of a commercial artificial intelligence (AI)–based mammography analysis software for improving the interpretations of breast ultrasound (US)-detected lesions. Methods: A retrospective analysis was performed on 1109 breasts that underwent both mammography and US-guided breast biopsy. The AI software processed mammograms and provided an AI score ranging from 0 to 100 for each breast, indicating the likelihood of malignancy. The performance of the AI score in differentiating mammograms with benign outcomes from those revealing cancers following US-guided breast biopsy was evaluated. In addition, prediction models for benign outcomes were constructed based on clinical and imaging characteristics with and without AI scores, using logistic regression analysis. Results: The AI software had an area under the receiver operating characteristics curve (AUROC) of 0.79 (95% CI, 0.79–0.82) in differentiating between benign and cancer cases. The prediction models that did not include AI scores (non-AI model), only used AI scores (AI-only model), and included AI scores (integrated model) had AUROCs of 0.79 (95% CI, 0.75–0.83), 0.78 (95% CI, 0.74–0.82), and 0.85 (95% CI, 0.81–0.88) in the development cohort, and 0.75 (95% CI, 0.68–0.81), 0.82 (95% CI, 0.76–0.88), and 0.84 (95% CI, 0.79–0.90) in the validation cohort, respectively. The integrated model outperformed the non-AI model in the development and validation cohorts (p < 0.001 for both). Conclusion: The commercial AI-based mammography analysis software could be a valuable adjunct to clinical decision-making for managing US-detected breast lesions. Clinical relevance statement: The commercial AI-based mammography analysis software could potentially reduce unnecessary biopsies and improve patient outcomes. Key Points: • Breast US has high rates of false-positive interpretations. • A commercial AI-based mammography analysis software could distinguish mammograms having benign outcomes from those revealing cancers after US-guided breast biopsy. • A commercial AI-based mammography analysis software may improve interpretations for breast US-detected lesions. [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]

Breast cancer, a prevalent and life-threatening disease, necessitates early detection for the effective intervention and the improved patient health outcomes. This paper focuses on the critical problem of identifying breast cancer using a model called Attention U-Net. The model is utilized on the Breast Ultrasound Image Dataset (BUSI), comprising 780 breast images. The images are categorized into three distinct groups: 437 cases classified as benign, 210 cases classified as malignant, and 133 cases classified as normal. The proposed model leverages the attention-driven U-Net's encoder blocks to capture hierarchical features effectively. The model comprises four decoder blocks which is a pivotal component in the U-Net architecture, responsible for expanding the encoded feature representation obtained from the encoder block and for reconstructing spatial information. Four attention gates are incorporated strategically to enhance feature localization during decoding, showcasing a sophisticated design that facilitates accurate segmentation of breast tumors in ultrasound images. It displays its efficacy in accurately delineating and segregating tumor borders. The experimental findings demonstrate outstanding performance, achieving an overall accuracy of 0.98, precision of 0.97, recall of 0.90, and a dice score of 0.92. It demonstrates its effectiveness in precisely defining and separating tumor boundaries. This research aims to make automated breast cancer segmentation algorithms by emphasizing the importance of early detection in boosting diagnostic capabilities and enabling prompt and targeted medical interventions. [ABSTRACT FROM AUTHOR]

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]

Current breast cancer screening relies on mammography, digital breast tomosynthesis and breast ultrasound. In select populations, breast MRI is also of great utility. However, multiple factors limit widespread use of breast MRI for screening. Efforts have been made to increase the availability of breast MRI for screening, in large part due to the increased cancer detection rate of breast MRI compared to mammography. Techniques include shortening standard breast MRI protocols with the potential for accommodating MRI screening in a higher number of patients. This review will explain the role of abbreviated breast MRI and ultrafast breast MRI in breast imaging, and detail how these approaches differ from standard dynamic contrast-enhanced breast MRI. In addition, limitations and advantages of these techniques will also be discussed. [ABSTRACT FROM AUTHOR]

Breast cancer is the most prevalent cancer among women worldwide. B-mode ultrasound (US) is essential for early detection, offering high sensitivity and specificity without radiation exposure. This study introduces a semi-automatic method to streamline breast US report generation, aiming to reduce the burden on radiologists. Our method synthesizes comprehensive breast US reports by combining the extracted information from radiologists' annotations during routine screenings with the analysis results from deep learning algorithms on multimodal US images. Key modules in our method include image classification using visual features (ICVF), type classification via deep learning (TCDL), and automatic report structuring and compilation (ARSC). Experiments showed that the proposed method reduced the average report generation time to 3.8 min compared to manual processes, even when using relatively low-spec hardware. Generated reports perfectly matched ground truth reports for suspicious masses without a single failure on our evaluation datasets. Additionally, the deep-learning-based algorithm, utilizing DenseNet-121 as its core model, achieved an overall accuracy of 0.865, precision of 0.868, recall of 0.847, F1-score of 0.856, and area under the receiver operating characteristics of 0.92 in classifying tissue stiffness in breast US shear-wave elastography (SWE-mode) images. These improvements not only streamline the report generation process but also allow radiologists to dedicate more time and focus on patient care, ultimately enhancing clinical outcomes and patient satisfaction. [ABSTRACT FROM AUTHOR]

Background: Neoadjuvant chemotherapy (NAC) is currently used for treating breast cancer in selected cases. Our study aims to evaluate the role of automated breast ultrasound (ABUS) in the assessment of response to NAC and compare the ABUS results with MRI. Methods: A total of 52 consecutive patients were included in this study. ABUS and MRI sensitivity (SE), specificity (SP), diagnostic accuracy (DA), positive predictive value (PPV), and negative predictive value (NPV) were calculated and represented using Area Under ROC Curve (ROC) analysis, searching for any significant difference (p < 0.05). The McNemar test was used searching for any significant difference in terms of sensitivity by comparing the ABUS and MRI results. The inter-observer agreement between the readers in evaluating the response to NAC for both MRI and ABUS was calculated using Cohen's kappa k coefficient. Results: A total of 35 cases of complete response and 17 cases of persistent disease were found. MRI showed SE, SP, DA, PPV, and NPV values of 100%, 88%, 92%, 81%, and 100%, respectively, with an AUC value of 0.943 (p < 0.0001). ABUS showed SE, SP, DA, PPV, and NPV values of 88%, 94%, 92%, 89%, and 94%, respectively, with an AUC of 0.913 (p < 0.0001). The McNemar test revealed no significant difference (p = 0.1250). The inter-observer agreement between the two readers in evaluating the response to NAC for MRI and ABUS was, respectively, 0.88 and 0.89. Conclusions: Automatic breast ultrasound represents a new accurate, tri-dimensional and operator-independent tool for evaluating patients referred to NAC. [ABSTRACT FROM AUTHOR]

Multi-task learning (MTL) methods are widely applied in breast imaging for lesion area perception and classification to assist in breast cancer diagnosis and personalized treatment. A typical paradigm of MTL is the shared-backbone network architecture, which can lead to information sharing conflicts and result in the decline or even failure of the main task's performance. Therefore, extracting richer lesion features and alleviating information-sharing conflicts has become a significant challenge for breast cancer classification. This study proposes a novel Multi-Feature Fusion Multi-Task (MFFMT) model to effectively address this issue. Firstly, in order to better capture the local and global feature relationships of lesion areas, a Contextual Lesion Enhancement Perception (CLEP) module is designed, which integrates channel attention mechanisms with detailed spatial positional information to extract more comprehensive lesion feature information. Secondly, a novel Multi-Feature Fusion (MFF) module is presented. The MFF module effectively extracts differential features that distinguish between lesion-specific characteristics and the semantic features used for tumor classification, and enhances the common feature information of them as well. Experimental results on two public breast ultrasound imaging datasets validate the effectiveness of our proposed method. Additionally, a comprehensive study on the impact of various factors on the model's performance is conducted to gain a deeper understanding of the working mechanism of the proposed framework. [ABSTRACT FROM AUTHOR]

Breast 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]

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]

Our study develops a computer-aided diagnosis (CAD) system for breast ultrasound by presenting an innovative frequency domain technique for extracting mass irregularity features, thereby significantly boosting tumor classification accuracy. The experimental data consists of 5252 ultrasound breast tumor images, including 2745 benign tumors and 2507 malignant tumors. A Support Vector Machine was employed to classify the tumor as either benign or malignant, and the effectiveness of the proposed features set in distinguishing malignant masses from benign ones was validated. For the constructed CAD system, the performance indices' accuracy, sensitivity, specificity, PPV, and NPV were 92.91%, 89.94%, 91.38%, 90.29%, and 91.45%, respectively, and the area index in the ROC analysis (AUC) was 0.924, demonstrating our method's superiority over traditional spatial gray level dependence (SGLD), the ratio of depth to width, the count of depressions, and orientation features. Therefore, the constructed CAD system with the proposed features will be able to provide a precise and quick distinction between benign and malignant breast tumors with minimal training time in clinical settings. [ABSTRACT FROM AUTHOR]

Objective To evaluate the clinical performance and financial costs of breast-specific gamma imaging (BSGI) as a biopsy-reducing problem-solving strategy in patients with inconclusive diagnostic imaging findings. Methods A retrospective analysis of all patients for whom BSGI was utilized for inconclusive imaging findings following complete diagnostic mammographic and sonographic evaluation between January 2013 and December 2018 was performed. Positive BSGI findings were correlated and biopsied with either US or stereotactic technique with confirmation by clip location and pathology. After a negative BSGI result, patients were followed for a minimum of 24 months or considered lost to follow-up and excluded (22 patients). Results of further imaging studies, biopsies, and pathology results were analyzed. Net savings of avoided biopsies were calculated based on average Medicare charges. Results Four hundred and forty female patients from 30 to 95 years (mean 55 years) of age were included in our study. BSGI demonstrated a negative predictive value (NPV) of 98.4% (314/319) and a positive predictive value for biopsy of 35.5% (43/121). The overall sensitivity was 89.6% (43/48), and the specificity was 80.1% (314/392). In total, 78 false positive but only 5 false negative BSGI findings were identified. Six hundred and twenty-one inconclusive imaging findings were analyzed with BSGI and a total of 309 biopsies were avoided. Estimated net financial savings from avoided biopsies were $646 897. Conclusion In the management of patients with inconclusive imaging findings on mammography or ultrasonography, BSGI is a problem-solving imaging modality with high NPV that helps avoid costs of image-guided biopsies. [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]

Nonmass lesions in breast ultrasound (US) are areas of altered echogenicity without definite margins or mass effect. However, these lesions may show calcifications, associated architectural distortion, or shadowing just like masses. They vary in their echogenicity, distribution, ductal or nonductal appearance and the associated features that can be seen in variety of benign and malignant pathologies. With no uniform definition or classification system, there is no standardized approach in further risk categorization and management strategies of these lesions. Malignant nonmass lesions are not uncommon and few sonographic features can help in differentiating benign and malignant pathologies. US-guided tissue sampling or lesion localization can be preferred in the nonmass lesions identified on second look US after magnetic resonance imaging or mammography. This article aims to describe various imaging patterns and attempts to provide an algorithmic approach to nonmass findings on breast US.

Core needle biopsy is a common diagnostic procedure in breast cancer patients, but it can occasionally lead to serious complications. We report a rare case of pseudoaneurysm formation following a core needle biopsy in a 54-year-old female patient diagnosed with breast cancer. Despite the routine nature of the procedure, the patient developed a palpable mass at the biopsy site, which prompted further diagnostic imaging and interventions. The pseudoaneurysm was effectively treated using a percutaneous approach with ultrasound-guided thrombin injection, demonstrating a minimally invasive solution that promptly addressed the complication without the need for surgical intervention. This case highlights the critical importance of detecting complications early in the biopsy process, as they have significant implications for disease staging and treatment initiation. It also underscores the importance of being prepared for immediate intervention in case of biopsy-related complications like pseudoaneurysms, to prevent severe consequences.

For high-risk women, breast magnetic resonance (MR) is the preferred supplemental imaging option, but spatial access differences may exacerbate disparities in breast care. This was a cross-sectional study examining distance between ZIP codes and the nearest breast imaging facility (MR, mammography, ultrasound) using 2023 data from the Food and Drug Administration and the American College of Radiology. Linear regression was used to assess distance differences controlling for Area Deprivation Index (ADI), urbanicity, and population size. Analyses were conducted in 2024. Among the 29,629 ZIP codes with an ADI and known urbanicity, unadjusted mean distance to breast MR was 23.2±25.1 miles (SD) compared with 8.2±8.3 for mammography and 22.2±25.0 for ultrasound. Hence, the average distance to breast MR facilities was 2.8 times further than to mammography facilities. ADI and urbanicity were associated with increased distance to the nearest breast imaging facility. The additional miles associated with the least advantaged areas compared with most advantaged areas was 12.2 (95%CI: 11.3, 13.2) for MR, 11.5 miles (95%CI: 10.6, 12.3) for ultrasound, and 2.4 (95%CI: 2.1, 2.7) for mammography. Compared with metropolitan areas, the additional miles to breast MR facilities was 23.2 (95%CI: 22.5, 24.0) for small/rural areas. Spatial access is substantially better for mammography sites compared with breast MR or ultrasound sites. Given these findings, consideration of options to mitigate the impact of differential access should be considered. For example, mammography sites could offer contrast-enhanced mammography. Future research should examine the feasibility and effectiveness of this and other options. [ABSTRACT FROM AUTHOR]

To assess the ability of the pressure gradient between breast lesions and adjacent normal tissue estimated by 3D subharmonic-aided pressure estimation (SHAPE) to characterize indeterminate breast lesions. This prospective study enrolled patients scheduled for ultrasound-guided needle biopsies of a breast lesion. Before the biopsy, 3D SHAPE data were collected from the breast lesion during the infusion of an ultrasound contrast agent (Definity) as well as after clearance of the agent. Direct, invasive pressure measurements in the lesion and adjacent normal tissue were then obtained using an intracompartmental pressure monitoring system (C2DX) before tissue sampling as part of the biopsy procedure. The mean SHAPE gradient and invasive measurement gradient between the lesion and adjacent normal tissue were compared to the biopsy results. The SHAPE gradients were also compared to the invasive pressure gradients. There were 8 malignant and 13 benign lesions studied. The SHAPE gradients and invasive pressure gradients were significantly different between the benign and malignant lesions (2.86 ± 3.24 vs. -0.03 ± 1.72 a.u.; p = 0.03 and 9.9 ± 8.5 vs. 20.9 ± 8.0 mmHg; p = 0.008, respectively). The area under the curves, specificities, and sensitivities for detecting malignancy by SHAPE gradients and invasive pressure gradients were 0.79 and 0.88, 77% and 92%, and 88% and 50%, respectively. A weak negative correlation was found between the SHAPE and invasive pressure gradients (r = -0.2). The pressure gradient between a breast lesion and adjacent normal tissue estimated by 3D SHAPE shows potential for characterizing indeterminate breast lesions. [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: 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]

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]

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]