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Background: Breast cancer is the most common cancer in women worldwide and second most common after cervical cancer in India. Sonographic demonstration of suspicious findings appropriately leads to further evaluation with core needle biopsy, even when mammographic findings are negative. Present study was aimed to correlate ultrasound categorised breast lesions with FNAC findings in diagnosing benign & malignant breast lesions at a tertiary hospital. Material and Methods: Present study was single-center, prospective, observational study, conducted female patients of age > 18 years had suspicious lesions on mammography. All patients underwent BIRAD score interpretation by USG & FNAC procedure. Results: In present study, 233 women were included. Majority were from 31-60 years age group, with mean age of 47.22 ± 11.43 years. Common radiological characteristics were microlobulated (35.6 %) & Circumscribed (29.6 %) margins of lesion, hypoechoic (60.9 %) & hyperechoic (17.6 %) echogenicity of lesion. Posterior features of lesion were Post acoustic Enhancement (27.9 %) & Shadowing (51.5 %). In present study common BIRAD grade were high suspicion of malignancy (IVc) (23.60 %) followed by benign (II) (19.74 %), highly suggestive of malignancy (V) (18.88 %), moderate suspicion of malignancy (IVb) (13.73 %), probably benign (III) (13.30 %), low suspicion of malignancy (IVa) (7.73 %) & negative (I) (3 %). USG-FNAC correlation was concordant in 222 patients (95.3 %). Sensitivity and Specificity of BIRAD ≥ IVb was highest (90.9 % & 89.1 % respectively) followed by BIRAD ≥ IVa (88.6 % & 75.3 % respectively) & BIRAD ≥ IVc (71.2 % & 95.1 % respectively). Conclusion: Combined ultrasonography (gray scale & colour Doppler) and FNAC offers the best approach in diagnosing a breast pathologies / lesion. [ABSTRACT FROM AUTHOR]

Breast cancer is among the most common cancers diagnosed in women globally. To help the breast cancer diagnosis, an important step is to accurately segment the breast lesion. To support clinicians in this important step, we analyze the performance of a semi-automated segmentation method based on the Local Graph Cut technique in the Image Segmenter application. Local graph cuts algorithm has the ability to segment more complicated shape by converting the image into a graph representation. It employs seed points set by the user and a cost function. The user identifies certain pixels as foreground and background. The region properties are identified from these pixels and they allow to specify the probability of a pixel belonging to the background or foreground. The graph cut formulation assigns each pixel to a node in the graph and incorrectly segmented pixels are re-assigned until the desired segmentation is completed. To evaluate the segmentation results, the Dice similarity coefficient and Fréchet distance were calculated between the ground truth images and the segmented images. Results show a Dice score of 0.7754 for malignant lesions and 0.8842 for benign lesions. The average Fréchet distance values were 303.28 for malignant and 290.80 for benign lesions, respectively. The experimental results show that the method achieves the best performance and gets the higher Dice score and Fréchet distance for breast benign lesions against malignant lesions. [ABSTRACT FROM AUTHOR]

Breast ultrasonography is a useful modality in patients undergoing diagnostic and screening breast imaging. However, breast ultrasound has a high false positive rate and can be time-consuming to perform. The purpose of this study was to evaluate the clinical impact of incidental axillary findings found on diagnostic breast ultrasounds at a single multi-site institution that has a standard protocol of scanning the axilla for all breast ultrasound exams. All diagnostic breast ultrasounds were retrospectively reviewed from January 2017 to September 2019. Follow-up imaging, relevant clinical history, and pathology results were also reviewed. All positive axillary findings were divided into incidental or non-incidental findings depending on whether there was a direct clinical indication to scan the axilla. Descriptive statistics were performed with a 5% level of significance. Of the 19,695 diagnostic ultrasounds performed during this timeframe, there were 91 (0.5%) incidental axillary findings given a BIRADS category 3 or 4, and none of these findings resulted in the diagnosis of an occult breast cancer. One biopsy-proven SLL/CLL lymphoma was diagnosed that was otherwise clinically occult. Routine axillary scanning in all patients undergoing a diagnostic breast ultrasound at a large multi-site institution yields a low rate of incidental findings and has minimal impact on detection of cancer. • A single cancer (SLL/CLL lymphoma) and no otherwise occult breast cancers were incidentally found during a two-year period consistenting of 19,695 diagnostic breast ultrasounds that included axillary scanning as part of routine protocol. • Abnormal interpretation rate of incidental findings in the axilla was low (0.05%). Low abnormal interpretation rate of incidentail findings in the axilla may be related to radiologist and technologist experience and classification of mildly prominent lymph nodes as benign. [ABSTRACT FROM AUTHOR]

Introduction: Ultrasound is a non-invasive, low-cost technique that does not use ionising radiation and provides a "real-time" image, and for these reasons, this method is ideal in several situations. Purpose: To demonstrate breast ultrasound evaluation as a first-line diagnostic method and to evaluate the variation of breast characteristics with age. Material and Methods: A total of 105 women with a mean age of 30 years participated and were divided into three age groups: 18–39, 40–59, and 60–79 years, excluding participants subject to mastectomy. After completing the informed consent, all participants answered personal and sociodemographic questions, such as personal and family history, menstrual cycle, pregnancy, ultrasound, and mammography, among others. They were then submitted to a bilateral breast ultrasound examination. Subsequently, all the images and their data were analysed, and a technical report of the examination was given to all the participants. Results: A total of 105 women with a mean age of 30 years participated, 58 of whom underwent the examination for the first time. In 31, changes (of which only 7 were known) were diagnosed. It was verified that, according to age group, the density of the breast stroma varied; older women have less breast density. Conclusions: Ultrasound is a good method for breast evaluation and can be considered important for the early evaluation of breast pathology and follow-up of the pathology. [ABSTRACT FROM AUTHOR]

OBJECTIVES: To determine diagnostic accuracy of resistive index measured through colour Doppler ultrasound of the breast lumps for diagnosing breast cancer keeping histopathology as a gold standard. STUDY DESIGN: Cross sectional validation study. SETTING: Department of radiology Quaid-e-Azam international hospital islamabad from 11-02-2021 to 10-08-2021. METHODOLOGY: A total of 215 patients were included in this study. In all the patients breast lumps were examined by ultrasonography on Grey scale using Xario200 Toshiba ultrasound machine with high linear probe frequency probe of 11MHz.Then with the same probe Doppler ultrasound of the lesion was performed using either colour or power Doppler. RESULTS: Overall sensitivity, specificity and diagnostic accuracy of Resistive Index was 86.7%, 78.9% and 86.0%, respectively. Positive predictive value and negative predictive value of Resistive Index was 97.7% and 36.6%, respectively. CONCLUSION: This study proves diagnostic accuracy of resistance index measured through colour Doppler ultrasound of the breast lumps for diagnosing breast cancer in patients avoiding the need for biopsy. [ABSTRACT FROM AUTHOR]

Early detection of breast lesions and distinguishing between malignant and benign lesions are critical for breast cancer (BC) prognosis. Breast ultrasonography (BU) is an important radiological imaging modality for the diagnosis of BC. This study proposes a BU image-based framework for the diagnosis of BC in women. Various pre-trained networks are used to extract the deep features of the BU images. Ten wrapper-based optimization algorithms, including the marine predator algorithm, generalized normal distribution optimization, slime mold algorithm, equilibrium optimizer (EO), manta-ray foraging optimization, atom search optimization, Harris hawks optimization, Henry gas solubility optimization, path finder algorithm, and poor and rich optimization, were employed to compute the optimal subset of deep features using a support vector machine classifier. Furthermore, a network selection algorithm was employed to determine the best pre-trained network. An online BU dataset was used to test the proposed framework. After comprehensive testing and analysis, it was found that the EO algorithm produced the highest classification rate for each pre-trained model. It produced the highest classification accuracy of 96.79%, and it was trained using only a deep feature vector with a size of 562 in the ResNet-50 model. Similarly, the Inception-ResNet-v2 had the second highest classification accuracy of 96.15% using the EO algorithm. Moreover, the results of the proposed framework are compared with those in the literature. [ABSTRACT FROM AUTHOR]

This prospective study was aimed at evaluating the role of automated breast ultrasound (ABUS) and contrast-enhanced ultrasound (CEUS) in the early prediction of treatment response to neoadjuvant chemotherapy (NAC) in patients with breast cancer. Forty-three patients with pathologically confirmed invasive breast cancer treated with NAC were included. The standard for evaluation of response to NAC was based on surgery within 21 d of completing treatment. The patients were classified as having a pathological complete response (pCR) and a non-pCR. All patients underwent CEUS and ABUS 1 wk before receiving NAC and after two treatment cycles. The rising time (RT), time to peak (TTP), peak intensity (PI), wash-in slope (WIS) and wash-in area under the curve (Wi-AUC) were measured on the CEUS images before and after NAC. The maximum tumor diameters in the coronal and sagittal planes were measured on ABUS, and the tumor volume (V) was calculated. The difference (∆) in each parameter between the two treatment time points was compared. Binary logistic regression analysis was used to identify the predictive value of each parameter. ∆ V , ∆TTP and ∆PI were independent predictors of pCR. The CEUS–ABUS model achieved the highest AUC (0.950), followed by those based on CEUS (0.918) and ABUS (0.891) alone. The CEUS–ABUS model could be used clinically to optimize the treatment of patients with breast cancer. [ABSTRACT FROM AUTHOR]

To introduce a novel computer-aided classification (CAC) system and investigate the feasibility of characterising and diagnosing breast masses on ultrasound (US). A total of 246 breast masses were included. US features and the final assessment categories of the breast masses were analysed by a radiologist and the CAC system according to the Breast Imaging Reporting and Data System (BI-RADS) lexicon. The CAC system evaluated the BI-RADS assessment from the fusion of multi-view and colour Doppler US images without (SmartBreast) or with combining clinical variables (m-CAC system). The diagnostic performance and agreement of US characteristics between the radiologist and the CAC system were compared. The agreement between the radiologist and the CAC system was substantial for mass shape (κ = 0.673), orientation (κ = 0.682), margin (κ = 0.622), posterior features (κ = 0.629), calcifications in a mass (κ = 0.709) and vascularity (κ = 0.745), fair for echo pattern (κ = 0.379), and moderate for BI-RADS assessment (κ = 0.575). With BI-RADS 4a as the cut-off value, the specificity (52.5% versus 25%, p <0.0001) and accuracy (73.98% versus 62.6%, p =0.0002) of the m-CAC system were improved without significant loss of sensitivity (94.44% versus 98.41%, p =0.1250) compared with the SmartBreast. The m-CAC system showed similar specificity (52.5% versus 45.83%, p =0.2430) and accuracy (73.98% versus 73.58%, p =1.0000) as the radiologist, but a lower sensitivity (94.44% versus 100%, p =0.0156). The CAC system showed an acceptable agreement with the radiologist for characterisation of breast lesions. It has the potential to mimic the decision-making behaviour of radiologists for the classification of breast lesions. • A novel CAC system was introduced. • The CAC system can characterise and diagnose breast masses on ultrasound. • The CAC system can mimic the decision-making behavior of radiologists. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) applications in mammography have gained significant popular attention; however, AI has the potential to revolutionize other aspects of breast imaging beyond simple lesion detection. AI has the potential to enhance risk assessment by combining conventional factors with imaging and improve lesion detection through a comparison with prior studies and considerations of symmetry. It also holds promise in ultrasound analysis and automated whole breast ultrasound, areas marked by unique challenges. AI's potential utility also extends to administrative tasks such as MQSA compliance, scheduling, and protocoling, which can reduce the radiologists' workload. However, adoption in breast imaging faces limitations in terms of data quality and standardization, generalizability, benchmarking performance, and integration into clinical workflows. Developing methods for radiologists to interpret AI decisions, and understanding patient perspectives to build trust in AI results, will be key future endeavors, with the ultimate aim of fostering more efficient radiology practices and better patient care. [ABSTRACT FROM AUTHOR]

Extramedullary plasmacytoma is a rare neoplasm, and involvement of the breast is extremely uncommon, with approximately 83 cases reported since 1928. We present the case of a 63-year-old patient with a history of multiple myeloma, who presented with a one-month history of a rapidly progressing, painless, soft mass measuring 4.5 cm in the right breast. Breast ultrasound showed a Bi-RADS 4 lesion, and due to suspicion of malignancy, a biopsy was performed and the patient was referred to the mastology department. Basic pathological and immunohistochemical studies concluded that the injury corresponded to an extramedullary plasmacytoma. After multiple lines of oncological treatment, the breast plasmacytoma showed an excellent clinical and imaging response. The case is described, and a literature review is presented. [ABSTRACT FROM AUTHOR]

It is possible to appropriately diagnose non-mass abnormalities by elucidating ultrasound non-mass abnormality findings and sharing the concept. If non-mass abnormalities can be diagnosed early, the number of curable cases could increase, leading to fewer breast cancer deaths. The Japan Society of Ultrasonics in Medicine (JSUM) Terminology/Diagnostic Criteria Committee has classified non-mass abnormalities into five subtypes: hypoechoic area in the mammary gland, abnormalities of the ducts, architectural distortion, multiple small cysts, and echogenic foci without a hypoechoic area. We herein define the findings for each of these subtypes and present a summary of the JSUM guidelines on non-mass abnormalities of the breast generated based on those findings. [ABSTRACT FROM AUTHOR]

Breast cancer is known to be the most common malignancy in women worldwide, the spectrum of the histological type varies from common types as invasive ductal carcinoma, of no special type to a rare variety as apocrine breast carcinoma. This study reports a case of apocrine carcinoma in a 60-year-old female patient, exploring the diagnosis strategies and management of this case. [ABSTRACT FROM AUTHOR]

Myeloid sarcoma (MS) is a rare extramedullary solid tumor arising most often in patients with current or subsequent acute myeloid leukemia (AML). Patients of all ages may present with involvement of the skin, lymph nodes, intestinal tract, bone, and/or central nervous system. Isolated involvement of the breast is rare, and only a small number of cases have been described in the literature. Breast MS may present as a palpable mass on clinical evaluation. In this broad literature review from 2010 to 2022, the most common findings on mammography are either solitary or multiple masses, followed by architectural distortion and, less commonly, no discrete findings. Sonography may demonstrate hypoechoic or mixed echogenicity mass(es) with circumscribed or indistinct, not discrete margins. Myeloid sarcoma may present as an enhancing mass or nonmass enhancement on breast MRI and is typically moderately radiotracer avid on 18F-fluorodeoxyglucose-PET. At histopathology, MS is characterized by myeloid blasts in varying stages of granulocytic or neutrophilic maturation; diagnosis typically requires immunophenotyping. There is no consensus for treatment of MS, although systemic chemotherapy for AML is often used as MS is considered the tissue equivalent of AML. This article will discuss and illustrate imaging and pathology findings when the breast is involved by MS. [ABSTRACT FROM AUTHOR]

Introduction: There are multiple management modalities for idiopathic granulomatous mastitis, but the treatment of choice is still under debate. This study aims to evaluate the diagnosis and outcomes of different management modalities in patients with idiopathic granulomatous mastitis and to identify the risk factors associated with recurrence. Method: This is a single-group cohort study that included those patients who had idiopathic granulomatous mastitis. Ultrasonography was conducted for all of the cases using LOGIQ E9 with an ML6-15 transducer (5–15 MHz). A core needle biopsy was conducted to take samples from the cases for histopathological examination. The patients were put on steroid therapy. Whenever the cases did not respond to the steroid therapy, treatment with a combination of low-dose steroids and methotrexate was started. In the lack of response to conservative treatments, surgical interventions were started. Results: Sixty-three cases with a confirmed histopathological diagnosis of granulomatous mastitis were included. The mean age of patients was 35.7 years. The history of more than one childbirth was positive in a large portion of the cases (82.5%). The lesion side was unilateral in 58.7% of the cases. A large proportion of the lesions were classified as BIRADS category 2. The best treatment outcome was yielded by a combination of low-dose steroids and incision and drainage. The factors of age, lesion area (cm2), skin thickening, and white blood cell count enhanced the chance of recurrence. Conclusion: Incision and drainage in combination with a low dose of steroids can give an acceptable outcome with a low rate of recurrence. [ABSTRACT FROM AUTHOR]

U-Net and its variants show excellent performance in medical image segmentation. However, due to the information loss caused by down-sampling and the blurry boundary of the ultrasound images itself, it is hard to extract and recover boundary information. Additionally, due to the intensity similarity and low contrast between tumor and non-tumor regions in breast ultrasound images, obtaining discriminative features is difficult. So we propose an optimized U-Net breast ultrasound image segmentation model ECU-Net. Firstly, a multi-scale salient boundary detection module is proposed to enhance the model's perception of tumor boundaries. Secondly, a contrast feature enhancement module is proposed to highlight the otherness between tumor and normal tissues. Then we combine the two modules to optimize skip-connection through boundaries completion and contrast information enhancement. Exhaustive experiments are conducted on two publicly available datasets, and experimental results show our ECU-Net performs better than U-Net and its classic variants. [ABSTRACT FROM AUTHOR]

Abstract Background Proper assessment of mammographically detected asymmetries is essential to avoid unnecessary biopsies and missed cancers as they may be of a benign or malignant cause. According to ACR BIRADS atlas 2013, mammographically detected asymmetries are classified into asymmetry, focal asymmetry, global asymmetry, and developing asymmetry. We aimed to assess the diagnostic performance of artificial intelligence in mammographically detected asymmetries compared to breast ultrasound as well as combined mammography and ultrasound. Results This study was a prospective study that comprised 51 women with breast asymmetry found on screening as well as diagnostic mammography. All participants conducted full-field digital mammography and ultrasound. Then the obtained mammographic images were processed by the artificial intelligence software system. Mammography had a sensitivity of 100%, specificity of 73%, a positive predictive value of 56.52%, a negative predictive value of 100%, and diagnostic accuracy of 80%. The results of Ultrasound revealed a sensitivity of 100.00%, a specificity of 89.47%, a positive predictive value of 76.47%, a negative predictive value of 100.00%, and an accuracy of 92.16%. Combined mammography and breast ultrasound showed a sensitivity of 100.00%, a specificity of 86.84%, a positive predictive value of 72.22%, a negative predictive value of 100.00%, and an accuracy of 90.20%. Artificial intelligence results demonstrated a sensitivity of 84.62%, a specificity of 94.74%, a positive predictive value of 48.26%, a negative predictive value of 94.47%, and an accuracy of 92.16%. Conclusions Adding breast ultrasound in the assessment of mammographically detected asymmetries led to better characterization, so it reduced the false-positive results and improved the specificity. Also, Artificial intelligence showed better specificity compared to mammography, breast ultrasound, and combined Mammography and ultrasound, so AI can be used to decrease unnecessary biopsies as it increases confidence in diagnosis, especially in cases with no definite ultrasound suspicious abnormality.

Breast ultrasound (BUS) imaging is commonly used in the early detection of breast cancer as a portable, valuable, and widely available diagnosis tool. Automated BUS image classification and segmentation can assist radiologists in making accurate and fast decisions. Recent studies illustrate that tumor, peritumoral, and background regions of BUS images provide valuable information for BUS image segmentation or classification. However, few studies have investigated the influence of these three regions on multi-task learning. In this study, we propose an RMTL-Net to simultaneously segment tumor regions and classify tumors in BUS images into benign or malignant categories. To improve both segmentation and classification performance, we design a regional attention (RA) module that employs the predicted probability maps to automatically guide the classifier to learn important category-sensitive information in the tumor, peritumoral, and background regions and seamlessly fuse them to obtain a better feature representation. We conduct detailed ablation experiments of the proposed RA module and comparative experiments with four recent state-of-the-art peer multi-task learning methods, three single-task segmentation methods, and four single-task classification methods on two public BUS datasets. Experimental results show that the proposed RMTL-Net achieves the best overall segmentation and classification accuracy in terms of five segmentation metrics and six classification metrics.

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

The primary aim of our study was to assess the main mammographic and ultrasonographic features of invasive male breast malignancies. The secondary aim was to evaluate whether a specific radiological presentation would be associated with a worse receptor profile. Radiological images (mammography and/or ultrasound) of all patients who underwent surgery for male invasive breast cancer in our institution between 2008 and 2023 were retrospectively analyzed by two breast radiologists in consensus. All significant features of radiological presentation known in the literature were re-evaluated. Fifty-six patients were selected. The mean age at surgery of patients was 69 years (range: 35–81); in 82% of cases (46 patients), the histologic outcome was invasive ductal carcinoma. A total of 28 out of 56 (50%) patients had preoperative mammography; in 9/28 cases (32%), we found a mass with microcalcifications on mammography. The mass presented high density in 25 out of 28 patients (89%); the mass showed irregular margins in 15/28 (54%) cases. A total of 46 out of 56 patients had preoperative ultrasounds. The lesion showed a solid mass in 41/46 (89%) cases. In 5/46 patients (11%), the lesion was a mass with a mixed (partly liquid–partly solid) structure. We did not find any statistically significant correlation between major types of radiological presentation and tumor receptor arrangement. Knowledge of the main radiologic presentation patterns of malignant male breast neoplasm can help better manage this type of disease, which is rare but whose incidence is increasing.

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

Background and Objectives: Estimation of the preoperative size of a breast tumor is of primary importance in deciding the treatment modality. Hence, clinical examination of the lump aided by the imaging is necessary. Our study is instrumental in correlating the size of breast tumor by high-resolution ultrasonography (USG) with the morphological size and also in comparing clinical staging of breast carcinoma against the gold standard pathological staging. Methods: It is a cross-sectional study of correlating the size of tumor on USG with the morphological size of biopsy proven invasive ductal carcinoma when received after excision. The size of the tumor was measured grossly and was compared with the USG measurements and palpatory findings. Results: Seventy percent of cases had good correlation between the USG and morphological size. In 50% of cases, the size on physical examination corroborated with the USG findings and only in 40% of the cases size on physical examination matched with morphological findings. In 65% of cases, the clinical staging of the breast carcinoma matched with the pathological staging. Our study reflected that USG promised to be the most useful radiological tool in predicting the accurate preoperative size of the tumor. Interpretation and Conclusion: We observed that USG has proved to be a very useful conjunct to clinical examination as only palpation was not found to be very accurate in predicting the actual tumor size. It proved to be safer and cheaper and effectively instrumental in meting out an effective management at pre- and postoperative level to the patient. [ABSTRACT FROM AUTHOR]

The article informs about a case of a 48-year-old postmenopausal woman with a complex fibroadenoma in the right breast, characterized by a single, nonprogressive lump. Topic include the cytological examination revealed irregularly bordered, variable-sized sheets of ductal epithelial cells with significant discohesiveness, supporting the diagnosis of a complex fibroadenoma, which requires precise identification due to its increased risk of developing malignancy.

Convolutional neural networks (CNNs) have played a significant role in feature extraction and tasks thereafter for accurate and automated diagnosis from ultrasound (US) breast tumor images. However, using pre-trained architectures and transfer learning for feature extraction could cause negative transfer in medical domain. Also publicly accessible online training/ validation US breast tumor datasets are seldom available. Hence, it becomes prudent to develop alternate CNN architectures as feature extraction backbones which are trained on smaller datasets without any consequences of overfitting. In this paper, a CNN was developed for feature extraction and prediction of breast tumor as benign/ malignant, with hyper parameters (learning rate, regularization factor, momentum, section depth and number of convolution filters) optimized using bayesian optimization. To further prevent any overfitting due to limited training data, a novel neutrosophic augmentation method was also introduced. The obtained simulation results on three different test datasets show that the classification accuracy of the optimized CNN outperforms by at least 3% than many other state-of-the-art deep architectures and significantly greater than 5% for shallow architectures. For segmenting the tumor region, the convolution maps from the higher layers of the optimized CNN are clustered to provide the initial contour for segmentation using active contours. The segmentation metrics with respect to ground truth is greater for the proposed work when compared with U-Net and fully Convolutional Network based segmentation. The structural similarity and mean segmentation error for the proposed method and the latter cases are 0.98, 0.93 and 0.92, and 0.01, 0.21 and 0.28 respectively. [ABSTRACT FROM AUTHOR]

Introduction: To compare the accuracy of Artificial Intelligent Breast Ultrasound (AIBUS) with hand-held breast ultrasound (HHUS) in asymptomatic women and to offer recommendations for screening in regions with limited medical resources. Methods: 852 participants who underwent both HHUS and AIBUS were enrolled between December 2020 and June 2021. Two radiologists, who were unaware of the HHUS results, reviewed the AIBUS data and scored the image quality on a separate workstation. Breast imaging reporting and data system (BI-RADS) final recall assessment, breast density category, quantified lesion features, and examination time were evaluated for both devices. The statistical analysis included McNemar's test, paired t-test, and Wilcoxon test. The kappa coefficient and consistency rate were calculated in different subgroups. Results: Subjective satisfaction with AIBUS image quality reached 70%. Moderate agreements were found between AIBUS with good quality images and HHUS for the BI-RADS final recall assessment (k = 0.47, consistency rate = 73.9%) and breast density category (k = 0.50, consistency rate = 74.8%). The lesions measured by AIBUS were statistically smaller and deeper than those measured by HHUS (P < 0.001), though they were not significant in clinical diagnosis (all < 3 mm). The total time required for the AIBUS examination and image interpretation was 1.03 (95% CI (0.57, 1.50)) minutes shorter than that of HHUS per case. Conclusion: Moderate agreement was obtained for the description of the BIRADS final recall assessment and breast density category. With image quality comparable to that of HHUS, AIBUS was superior for the efficiency of primary screening. [ABSTRACT FROM AUTHOR]

Attempts to use computers to aid in the detection of breast malignancies date back more than 20 years. Despite significant interest and investment, this has historically led to minimal or no significant improvement in performance and outcomes with traditional computer-aided detection. However, recent advances in artificial intelligence and machine learning are now starting to deliver on the promise of improved performance. There are at present more than 20 FDA-approved AI applications for breast imaging, but adoption and utilization are widely variable and low overall. Breast imaging is unique and has aspects that create both opportunities and challenges for AI development and implementation. Breast cancer screening programs worldwide rely on screening mammography to reduce the morbidity and mortality of breast cancer, and many of the most exciting research projects and available AI applications focus on cancer detection for mammography. There are, however, multiple additional potential applications for AI in breast imaging, including decision support, risk assessment, breast density quantitation, workflow and triage, quality evaluation, response to neoadjuvant chemotherapy assessment, and image enhancement. In this review the current status, availability, and future directions of investigation of these applications are discussed, as well as the opportunities and barriers to more widespread utilization. [ABSTRACT FROM AUTHOR]

The preoperative differentiation of breast phyllodes tumors (PTs) from fibroadenomas (FAs) plays a critical role in identifying an appropriate surgical treatment. Although several imaging modalities are available, reliable differentiation between PT and FA remains a great challenge for radiologists in clinical work. Artificial intelligence (AI)-assisted diagnosis has shown promise in distinguishing PT from FA. However, a very small sample size was adopted in previous studies. In this work, we retrospectively enrolled 656 breast tumors (372 FAs and 284 PTs) with 1945 ultrasound images in total. Two experienced ultrasound physicians independently evaluated the ultrasound images. Meanwhile, three deep-learning models (i.e., ResNet, VGG, and GoogLeNet) were applied to classify FAs and PTs. The robustness of the models was evaluated by fivefold cross validation. The performance of each model was assessed by using the receiver operating characteristic (ROC) curve. The area under the curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were also calculated. Among the three models, the ResNet model yielded the highest AUC value, of 0.91, with an accuracy value of 95.3%, a sensitivity value of 96.2%, and a specificity value of 94.7% in the testing data set. In contrast, the two physicians yielded an average AUC value of 0.69, an accuracy value of 70.7%, a sensitivity value of 54.4%, and a specificity value of 53.2%. Our findings indicate that the diagnostic performance of deep learning is better than that of physicians in the distinction of PTs from FAs. This further suggests that AI is a valuable tool for aiding clinical diagnosis, thereby advancing precision therapy. [ABSTRACT FROM AUTHOR]

Purpose: As breast-conserving surgery (BCS) has become the standard for treatment of early breast cancer, the need for new technologies to improve intraoperative margin assessment has become clear. Close or positive margins during BCS lead to additional surgeries, treatment delay, additional stress for patients and increasing healthcare cost. Automated three-dimensional breast ultrasound (ABUS) systems are meant to overcome the shortcomings of hand-held ultrasound (HHUS). In this study, we investigate the feasibility of ABUS to conduct ultrasound on surgical specimens in breast conserving therapy. Methods: In this monocentric, non-interventional study, specimens of 40 women were examined via ABUS. A construction with isotonic saline solution, gel pads and ABUS membranes was invented by our team to produce images of breast cancer specimens using ABUS. Evaluation of the ABUS images was carried out by two independent physicians trained on ABUS evaluation. Results: ABUS was conducted on 40 specimens. 90% of the generated images were of high quality. Measured tumor sizes with ABUS were bigger than measured tumor size with HHUS (mean tumor size 22.9 vs. 18.1 mm, CI 2.38–7.35, p < 0.05). The mean difference between the ABUS tumor size and the pathological tumor size was 1.8 mm (CI − 0.84–4.53, p = 0.17). The mean difference between the HHUS tumor size and the pathological tumor size was 3.2 mm (CI − 5.35 to − 1.03, p = 0.005). Conclusion: ABUS seems to be a suitable method to conduct specimen ultrasound. Further studies are required to evaluate the accuracy of ABUS for intraoperative margin assessment and possible implementation in clinical work routine. [ABSTRACT FROM AUTHOR]

The breast pain or mastalgia is the commonest presenting symptom to a breast clinic. It is estimated that about 60 to 70% of all patients visiting breast clinic suffer from mastalgia. In majority of women, mastalgia is a manifestation of "aberration in normal development and involution" (ANDI). It is considered to arise from disturbed hormonal interplay between hypothalamus, pituitary, ovaries, and the ductolobular tissue of the breast. The present guide has been prepared by a group of experts in the field who are members of Asian Society of Mastology (ASOMA) and who represent the practice in USA (I.J.), Great Britain (A.G.), and Asia (S.K., A.D. C.M. A.S., R.A.)The proposed management algorithm should serve all women across different continents. [ABSTRACT FROM AUTHOR]

The fibroadenoma is considered as a manifestation of aberration in normal development and involution (ANDI); however, its exact etiopathogenesis is poorly understood. We investigated the role of oestrogen (ER) and progesterone receptors (PR) in Fibroadenoma, to assess the ER and PR expression in epithelium and stroma of fibroadenoma and to co-relate this expression with the response to centchroman therapy. Fifty ladies with 65 fibroadenomas were investigated. Tissue from core biopsy of lesions was stained for estimating the ER and PR expression in the epithelial cells and in stroma. Patients received tablet centchroman 30 mg daily for 3 months, and the response to therapy was evaluated. A regression ≥ 30% in largest dimension of fibroadenoma on sonography was considered a significant response. The association between the response and ER and PR expression in epithelium and stroma was assessed. In the epithelium: ER positivity = 62.5%; PR positivity = 93.75%. In the stroma, the ER positivity = 2% and PR positivity = 8.3%. Response to centchroman was observed in 33/48 = 68.7%. Among the 33 responders, 23 lesions expressed ER in epithelium, while 10 lesions were ER negative. The relative risk of regression with "ER positivity of epithelium" as exposure = 1.38 (95% CI; 1.35–1.40, p = 0.12). Among responders, 30/33 exhibited PR positivity in epithelium. Expression of ER and PR in the epithelium of fibroadenoma is associated with significant regression in size with 3 months of therapy with centchroman. [ABSTRACT FROM AUTHOR]

This study aimed to evaluate contrast-enhanced mammography (CEM) and to compare breast lesions on CEM and breast magnetic resonance imaging (MRI) using 5 features. We propose a flowchart for BI-RADS classification of breast lesions on CEM based on the Kaiser score (KS) flowchart for breast MRI. Sixty-eight subjects (women and men; median age 61.4 ± 11.6 years) who were suspected of having a malignant process in the breast based on digital mammography (MG) findings were included in the study. The patients underwent breast ultrasound (US), CEM, MRI and biopsy of the suspicious lesion. There were 47 patients with malignant lesions confirmed by biopsy and 21 patients with benign lesions, for each of which a KS was calculated. In the patients with malignant lesions, the MRI-derived KS was 9 (IQR 8–9); its CEM equivalent was 9 (IQR 8–9); and BI-RADS was 5 (IQR 4–5). In patients with benign lesions, MRI-derived KS was 3 (IQR 2–3); its CEM equivalent was 3 (IQR 1.7–5); and BI-RADS was 3 (IQR 0–4). There was no significant difference between the ROC-AUC of CEM and MRI (p = 0.749). In conclusion, there were no significant differences in KS results between CEM and breast MRI. The KS flowchart is useful for evaluating breast lesions on CEM. [ABSTRACT FROM AUTHOR]

Breast cancer is the most common malignancy and the leading cause of death for women. Ultrasound is the main tool for breast cancer screening, but it can be influenced by the subjective factors of sonographers. With the continuous development of medical technology and artificial intelligence (AI), the application of breast ultrasound imaging technology is becoming increasingly widespread. Among them, the application of AI and automated breast volume scanning (ABVS) brings new opportunities and challenges for ultrasound diagnosis of breast diseases, while making breast ultrasound diagnosis more accurate and efficient. This article explores the application and prospects of AI and ABVS in ultrasound diagnosis of breast diseases. [ABSTRACT FROM AUTHOR]

Ultrasound is a commonly used imaging modality for breast cancer diagnosis and prognosis but suffers from false positives, false negatives and interobserver variability. Deep learning (DL), a subset of artificial intelligence, has the potential to improve the efficiency and accuracy of breast ultrasound. This article provides a comprehensive overview of DL applications for breast cancer diagnosis and treatment in ultrasound, including methodological descriptions of various DL models, and clinical applications on noise reduction, lesion localization, risk assessment, diagnosis, response evaluation and outcome prediction. Furthermore, the review highlights the importance of interpretability and small sample size learning of DL-based systems in clinical practice; specific recommendations for further expanding the clinical impact of DL-based systems are also provided. [ABSTRACT FROM AUTHOR]

An elderly woman with preinvasive breast cancer, atrial fibrillation, coronary artery-pulmonary artery fistula, pulmonary hypertension and Stage 4 diabetic nephropathy underwent a mastectomy and sentinel lymph node biopsy. Managing her perioperative fluid balance and anticoagulation treatment were challenging. Having a malignancy, atrial fibrillation plus underhydration will increase her risk of hypercoagulation. However, fluid overload will lead to pulmonary oedema which will decrease her oxygenation further in pulmonary hypertension. Cessation of anticoagulation also increased the risk of hypercoagulation. Anticoagulant therapy increases the risk of a perioperative wound haematoma, which may require another general anaesthesia for identification and arrest of the bleeding source and haematoma evacuation. A haematoma will also increase the risk of surgical site infection; especially as a diabetic. Her anticoagulant therapy (rivoroxaban) was stopped four days preoperatively. Her ejection fraction was 50%, with Grade II diastolic dysfunction and TAPSE 0.6 cm. Her CHA2DS2-VASc Score was 5. Perioperatively, intraarterial cannulation was connected to the FloTrac™ sensor and EV1000™ monitor. Her fluid management was monitored using goal directed (GD) fluid therapy. The patient underwent surgery successfully and her anticoagulant therapy was recommenced 14 days postoperatively. [ABSTRACT FROM AUTHOR]

Medical imaging, like ultrasound, gives a good visual picture of how an organ works. However, a radiologist has a hard time and takes a long time to process these images, which delays the diagnosis. Several automated methods for detecting and segmenting breast lesions have been developed. Nevertheless, due to ultrasonic artifacts and the intricacy of lesion forms and locations, the segmentation of lesions or tumors from breast ultrasonography remains an open issue. Medical image segmentation has seen a breakthrough thanks to deep learning. U-Net is the most noteworthy deep network in this regard. The traditional U-Net design lacks precision when dealing with complex data sets, despite its exceptional performance in segmenting multimedia medical images. To reduce texture detail redundancy and avoid overfitting, we suggest developing the U-Net architecture by including dropout layers after each max pooling layer. Batchnormalization layers and a binary cross-entropy loss function were used to preserve breast tumor texture features and edge attributes while decreasing computational costs. We used the breast ultrasound dataset of 780 images with normal, benign, or malignant tumors. Our model showed superior segmentation results for breast ultrasound pictures compared to previous deep neural networks. Quantitative measures, accuracy, and IoU values were utilized to evaluate the suggested model's effectiveness. The results were 99.34% and 99.60% for accuracy and IoU. The results imply that the augmented U-Net model that has been suggested has high diagnostic potential in the clinic since it can correctly segment breast lesions. [ABSTRACT FROM AUTHOR]

Breast cancer is one of the most affecting carcinoma for women from long time. Early detection is necessary to increase the lifespan of patients. In this study deep learning and machine learning approaches are applied to histopathological, mammogram and ultrasound breast cancer modalities. In-order to increase the efficacy of diagnosis of these modalities. Study has been carried out in majorly four phases. First phase involved collection of the datasets of all the three modalities mentioned earlier. Second phase consists of extracting relevant features using ResNet-18. Third phase involves feeding the extracted information to enhanced firefly or to the existing optimization techniques. Fourth phase consists of considering selected features as input to the classifiers. Then enhanced firefly based classifier compared with the existing ant colony and genetic algorithm based classifier. Enhanced firefly based classifier displays better results compared to the state of art approaches. [ABSTRACT FROM AUTHOR]

Purpose of Review: Increased breast density, among other calculated risk factors, decreases the sensitivity of screening mammography. Several supplementary imaging modalities including breast ultrasound, breast MRI, contrast-enhanced mammography (CEM), and molecular breast imaging (MBI) can be used in addition to screening mammograms to increase the sensitivity of cancer detection in above-average-risk women and will be discussed here. Recent Findings: Ultrasound is the most widely used and accessible supplementary screening method that improves the cancer detection rate. MRI, the most sensitive supplemental breast imaging modality, is used to detect and visualize additional cancers in those with a lifetime risk of breast cancer at or greater than 20%. Small studies indicate that CEM has cancer detection rates equivalent to MRI; nevertheless, more evidence is needed to demonstrate its use. MBI has shown promise to be an adjunct to mammography for patients with dense breasts and for situations in which breast MRI is contraindicated. Despite this promising research, the American College of Radiology currently does not recommend the use of MBI for screening. Summary: In women who have a higher-than-average risk of developing breast cancer, additional imaging modalities, such as breast ultrasound, breast magnetic resonance imaging, contrast-enhanced mammography, and molecular breast imaging, can be used in conjunction with screening mammograms to increase the sensitivity of cancer detection. [ABSTRACT FROM AUTHOR]

Background: Sonomammography is a valuable imaging technique for characterizing breast masses into benign and malignant categories. This study aimed to assess the effectiveness of sonomammography in breast mass characterization and explore specific sonographic features associated with benign and malignant lesions. Methods: A prospective cross-sectional study was conducted at B.K. L. Walawalkar Rural Medical College & Hospital in Maharashtra State. A total of 456 patients undergoing breast cancer screening were included. Sonomammography was performed using high-frequency transducers, and additional techniques such as color Doppler imaging and elastography were employed when necessary. Histopathological analysis served as the gold standard for determining the benign or malignant nature of the breast masses. Results: The majority of participants belonged to the age groups of 31-50 years (48.5%) and 51-60 years (34.2%). Among the screened patients, 39 lesions were identified as cancerous (8.6%) and 417 as noncancerous (91.4%). Only 33 participants had attained menopause. Most participants were married (98.0%). The majority of breast lesions exhibited dense breast tissue (69.3%), and a posterior acoustic shadow was observed in the majority of lesions (70.6%). Lesions with a parallel orientation were predominant (94.7%), and regular margins were identified in most cases (90.6%). Anechoic lesions were the most common (89.9%). Conclusion: Sonomammography demonstrated its effectiveness in characterizing breast masses into benign and malignant categories. Dense breast tissue, posterior acoustic shadowing, parallel orientation, regular margins, and anechoic appearance were frequently observed in the study population. These findings highlight the importance of sonomammography as a reliable tool for accurate breast mass characterization. [ABSTRACT FROM AUTHOR]

Breast cancer is one of the most common malignancies that threaten women's health. Ultrasound testing is a widespread technique employed for the early detection of tumors. However, after receiving the paper ultrasound report, most patients often have to wait for several days to receive the diagnosis results, which can increase their psychological burden and may cause treatment delay. Based on deep learning, this study designed a computer-aided diagnostic system that directly classifies benign and malignant tumors in breast ultrasound images on paper reports taken by patients, helping them obtain auxiliary diagnostic results as soon as possible. In order to segment and denoise ultrasound report images of patients, this paper proposes a breast ultrasound report generation method, which mainly includes a segmentation model, a rotating classification model and a generative model. With this method, multiple high-quality individual breast ultrasound images can be obtained from a single ultrasound report photo, improving the performance of the breast ultrasound image classification model. In order to utilize high-quality breast ultrasound images and improve classification performance, this paper proposed a breast ultrasound report classification model that includes a feature extraction module, a channel attention module and a classification module. The accuracy of the model reached 89.31%, recall rate reached 88.65%, specificity reached 89.57%, F1 score reached 89.42% and AUC reached 94.53% when input images contained noise. The method proposed in this article is more suitable for practical application scenarios and it can quickly and accurately assist patients in obtaining the benign and malignant classification results of ultrasound reports. [ABSTRACT FROM AUTHOR]

Objectives: H‐scan ultrasound (US) imaging is a novel tissue characterization technique to detect apoptosis‐induced changes in cancer cells after the initiation of effective drug treatment. The objective of the proposed research was to assess the sensitivity of 3‐dimensional (3D) H‐scan US technique for monitoring the response of breast cancer‐bearing animals to neoadjuvant chemotherapy and correlate results to diffusion‐weighted magnetic resonance imaging (DW‐MRI) measurements of programmed cancer cell death. Methods: Experimental studies used female mice (N = 18) implanted with human breast cancer cells. Animals underwent H‐scan US and DW‐MRI imaging on days 0, 1, 3, 7, and 10. After imaging at day 0, breast tumor‐bearing nude mice were treated biweekly with an apoptosis‐inducing drug. Texture analysis of H‐scan US images explored spatial relationships between local US scattering. At day 10, H‐scan measurements were compared with DW‐MRI‐derived apparent diffusion coefficient (ADC) values and histological findings. Results: H‐scan US imaging of low and high dose cisplatin‐treated cancer‐bearing animals revealed changes in image intensity suggesting a progressive decrease in aggregate US scatterer size that was not observed in control animals. Longitudinal trends discovered in H‐scan US result matched with texture analysis and DW‐MRI (P <.01). Further, analysis of the H‐scan US image intensity and corresponding DW‐MRI‐derived ADC values revealed a strong linear correlation (R2 =.93, P <.001). These changes were due to cancer cell apoptotic activity and consider as early detectable biomarker during treatment. Conclusions: The 3D H‐scan technique holds promise for assisting clinicians in monitoring the early response of breast cancer tumor to neoadjuvant chemotherapy and adding value to traditional diagnostic ultrasound examinations. [ABSTRACT FROM AUTHOR]

Objectives: To introduce an ultrasound‐based scoring system for radiation‐induced breast toxicity and test its reliability. Methods: Breast ultrasound (BUS) was performed on 32 patients receiving breast radiotherapy (RT) to assess the radiation‐induced acute toxicity. For each patient, both the untreated and irradiated breasts were scanned at five locations: 12:00, 3:00, 6:00, 9:00, and tumor bed to evaluate for heterogenous responses to radiation within the entire breast. In total, 314 images were analyzed. Based on ultrasound findings such as skin thickening, dermis boundary irregularity, and subcutaneous edema, a 4‐level, Likert‐like grading scheme is proposed: none (G0), mild (G1), moderate (G2), and severe (G3) toxicity. Two ultrasound experts graded the severity of breast toxicity independently and reported the inter‐ and intra‐observer reliability of the grading system. Imaging findings were compared with standard clinical toxicity assessments using Common Terminology Criteria for Adverse Events (CTCAE). Results: The inter‐observer Pearson correlation coefficient (PCC) was 0.87 (95% CI: 0.83–0.90, P <.001). For intra‐observer repeatability, the PCC of the repeated scores was 0.83 (95% CI: 0.78–0.87, P <.001). Imaging findings were compared with standard clinical toxicity assessments using CTCAE scales. The PCC between BUS scores and CTCAE results was 0.62 (95% CI: 0.35–0.80, P <.001). Among all locations, 6:00 and tumor bed showed significantly greater toxicity compared with 12:00 (P =.04). Conclusions: BUS can investigate the cutaneous and subcutaneous tissue changes after RT. This BUS‐based grading system can complement subjective clinical assessments of radiation‐induced breast toxicity with cutaneous and subcutaneous sonographic information. [ABSTRACT FROM AUTHOR]

To investigate the multimodal ultrasonography (US) features of breast lymphomas (BLs). Multimodal ultrasonography was performed on three patients who had been diagnosed BLs by pathology in our hospital. Two cases of primary BLs appeared hyper hyperechoic and hypoechoic, and one case of second BL appeared isoechoic and hypoechoic. Elastography of two lesions was soft elasticity and the elasticity score was 2 points. Contrast‐enhanced ultrasound (CEUS) revealed rapid uniform high enhancement with clearly defined after injection. There are some common features in ultrasound. Combined with those features can help to improve the sonographer's understanding and diagnosis of the BLs. [ABSTRACT FROM AUTHOR]

We present an analysis and evaluation of breast cancer detection and diagnosis using segmentation models. We used an advanced semantic segmentation method and a deep convolutional neural network to identify the Breast Imaging Reporting and Data System (BI-RADS) lexicon for breast ultrasound images. To improve the segmentation results, we used six models to analyse 309 patients, including 151 benign and 158 malignant tumour images. We compared the Unet3+ architecture with several other models, such as FCN, Unet, SegNet, DeeplabV3+ and pspNet. The Unet3+ model is a state-of-the-art, semantic segmentation architecture that showed optimal performance with an average accuracy of 82.53% and an average intersection over union (IU) of 52.57%. The weighted IU was found to be 89.14% with a global accuracy of 90.99%. The application of these types of segmentation models to the detection and diagnosis of breast cancer provides remarkable results. Our proposed method has the potential to provide a more accurate and objective diagnosis of breast cancer, leading to improved patient outcomes. [ABSTRACT FROM AUTHOR]

Automated breast ultrasound (ABUS) imaging system is a practical technique to automatically scan the whole breast. Automatic tumor detection plays a significant role in the clinic. However, training deep convolutional neural networks (CNNs) for tumor detection needs a large quantity of labeled data. It is time‐consuming and expensive to manually annotate tumor positions in ABUS images. In this paper, a novel semi‐supervised learning EfficientDet (SSL‐E) model is proposed for ABUS tumor detection. Our SSL‐E model solves the tumor detection problem from high similarity and serious unbalance between tumors and backgrounds. Considering the image contrast variation and tumor scale variations in ABUS images, color transformation and geometric transformation are employed for data augmentations. Then the consistency between image and its augmented version is developed, thus the robustness of the detector can be improved. Aiming at the problem of serious unbalance between tumors and backgrounds, a novel copy‐paste synthesis strategy is designed, which can generate more tumor samples and enhance tumor diversity. This method is tested on 68 tumor volumes and 68 normal volumes, including 43,248 slices (1683 tumor slices and 41,565 normal slices). It obtains a promising result with sensitivity of 90.2% and false positives per image (FPs/I) at 0.15. [ABSTRACT FROM AUTHOR]

Background: Neurofibroma of the breast is extremely rare, with only a few reported cases. Here, we report a case of solitary neurofibroma of the breast in a 95-year-old woman. Case presentation: A 95-year-old woman presented with a palpable mass in the left breast. Mammography revealed a well-defined mass. A 1.6-cm round mass was found in the lower outer quadrant of the left breast on ultrasonography. The internal echo of the tumor was a mixture of relatively uniform hypoechoic areas with posterior enhancement and heterogeneous hyperechoic areas. She underwent a core needle biopsy. The pathological findings revealed a spindle cell lesion with no malignant findings. At 2 months follow-up, repeat breast ultrasonography showed that the mass had enlarged to be 2.7 cm in size. A repeat core needle biopsy, however, revealed no particularly new information. Because the tumor was growing and a definite diagnosis was not made, lumpectomy was performed. We found bland-spindled cells with shredded-carrot collagen bundles. Immunohistochemical antibody markers (S100, SOX10, and CD34) were positive for the spindle cells. Some of the tumors maintained the bilayer nature of luminal cells and myoepithelial cells, which might be the reason for internal heterogeneity on ultrasound. A histological diagnosis of neurofibroma with adenosis was made. At 6 months follow-up, no recurrent lesions were found. Conclusions: Ultrasound and pathological images revealed an extremely rare case of neurofibroma combined with adenosis. Tumor resection was performed because it was difficult to make a definitive diagnosis using needle biopsy. Even when a benign tumor is suspected, short-term follow-up is necessary, and if an enlargement is observed, early tumor resection is recommended. [ABSTRACT FROM AUTHOR]

Purpose: The detection of human epidermal growth factor receptor 2 (HER2) expression status is essential to determining the chemotherapy regimen for breast cancer patients and to improving their prognosis. We developed a deep learning radiomics (DLR) model combining time-frequency domain features of ultrasound (US) video of breast lesions with clinical parameters for predicting HER2 expression status. Patients and Methods: Data for this research was obtained from 807 breast cancer patients who visited from February 2019 to July 2020. Ultimately, 445 patients were included in the study. Pre-operative breast ultrasound examination videos were collected and split into a training set and a test set. Building a training set of DLR models combining time-frequency domain features and clinical features of ultrasound video of breast lesions based on the training set data to predict HER2 expression status. Test the performance of the model using test set data. The final models integrated with different classifiers are compared, and the best performing model is finally selected. Results: The best diagnostic performance in predicting HER2 expression status is provided by an Extreme Gradient Boosting (XGBoost)-based time-frequency domain feature classifier combined with a logistic regression (LR)-based clinical parameter classifier of clinical parameters combined DLR, particularly with a high specificity of 0.917. The area under the receiver operating characteristic curve (AUC) for the test cohort was 0.810. Conclusion: Our study provides a non-invasive imaging biomarker to predict HER2 expression status in breast cancer patients. [ABSTRACT FROM AUTHOR]

Purpose: In the last 2 decades, the optimal management of the axilla in breast cancer patients receiving neoadjuvant chemotherapy (NACT) has been one of the most frequently discussed topics. Little is known about the attitudes of surgeons/radiologists towards new developments such as targeted axillary dissection. Therefore, the NOGGO conducted a survey to evaluate the current approach to axillary management. Methods: A standardized digital questionnaire was sent out to > 200 departments in Germany between 7/2021 and 5/2022. The survey was supported by EUBREAST. Results: In total, 116 physicians completed the survey. In cN0 patients scheduled to receive NACT, 89% of respondents recommended sentinel lymph node biopsy (SLNB) after NACT. In case of ypN1mi(sn), 44% advised no further therapy, while 31% proposed ALND and 25% axillary irradiation. 64% of respondents recommended a minimally invasive axillary biopsy to cN + patients. TAD was used at the departments of 82% of respondents and was offered to all cN + patients converting to ycN0 by 57% and only to selected patients, usually based on the number of suspicious nodes at time of presentation, by 43%. The most common marking technique was a clip/coil. 67% estimated that the detection rate of their marker was very good or good. Conclusion: This survey shows a heterogenous approach towards axillary management in the neoadjuvant setting in Germany. Most respondents follow current guidelines. Since only two-thirds of respondents experienced the detection rate of the marker used at their department as (very) good, future studies should focus on the comparative evaluation of different marking techniques. [ABSTRACT FROM AUTHOR]

Purpose: BUS‐Set is a reproducible benchmark for breast ultrasound (BUS) lesion segmentation, comprising of publicly available images with the aim of improving future comparisons between machine learning models within the field of BUS. Method: Four publicly available datasets were compiled creating an overall set of 1154 BUS images, from five different scanner types. Full dataset details have been provided, which include clinical labels and detailed annotations. Furthermore, nine state‐of‐the‐art deep learning architectures were selected to form the initial benchmark segmentation result, tested using five‐fold cross‐validation and MANOVA/ANOVA with Tukey statistical significance test with a threshold of 0.01. Additional evaluation of these architectures was conducted, exploring possible training bias, and lesion size and type effects. Results: Of the nine state‐of‐the‐art benchmarked architectures, Mask R‐CNN obtained the highest overall results, with the following mean metric scores: Dice score of 0.851, intersection over union of 0.786 and pixel accuracy of 0.975. MANOVA/ANOVA and Tukey test results showed Mask R‐CNN to be statistically significant better compared to all other benchmarked models with a p‐value >0.01. Moreover, Mask R‐CNN achieved the highest mean Dice score of 0.839 on an additional 16 image dataset, that contained multiple lesions per image. Further analysis on regions of interest was conducted, assessing Hamming distance, depth‐to‐width ratio (DWR), circularity, and elongation, which showed that the Mask R‐CNN's segmentations maintained the most morphological features with correlation coefficients of 0.888, 0.532, 0.876 for DWR, circularity, and elongation, respectively. Based on the correlation coefficients, statistical test indicated that Mask R‐CNN was only significantly different to Sk‐U‐Net. Conclusions: BUS‐Set is a fully reproducible benchmark for BUS lesion segmentation obtained through the use of public datasets and GitHub. Of the state‐of‐the‐art convolution neural network (CNN)‐based architectures, Mask R‐CNN achieved the highest performance overall, further analysis indicated that a training bias may have occurred due to the lesion size variation in the dataset. All dataset and architecture details are available at GitHub: https://github.com/corcor27/BUS‐Set, which allows for a fully reproducible benchmark. [ABSTRACT FROM AUTHOR]