UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.

UNASSIGNED: The detection of masses on mammogram represents one of the earliest signs of a malignant breast cancer. However, masses may be hard to detect due to dense breast tissue, leading to false negative results. In this study, we aimed to explore the clinical application of the convolutional neural network (CNN)-based deep learning (DL) system constructed in our previous work as an objective and accurate tool for breast cancer screening and diagnosis in Asian women.
UNASSIGNED: This retrospective analysis included 324 patients with masses detected on mammograms at Shenzhen People\'s Hospital between April and December 2019. (I) Detection: images were independently analyzed by two junior radiologists who were blinded to relative results. Then, a senior radiologist analyzed the images after reviewing all the relevant information as the reference. (II) Classification: masses were classified by the same two junior radiologists and in consensus by two other seniors. Images were also input into the DL system. The sensitivity of detection by junior radiologists and the DL system, effects of different factors [breast density; patient age; morphology, margin, size, breast imaging reporting and data system (BI-RADS) category of the mass] on detection, the accuracy, sensitivity, and specificity of classification, and the area under the receiver operating characteristic (ROC) curve (AUC), were evaluated.
UNASSIGNED: A total of 618 masses were detected. The detection sensitivity of the two junior radiologists [78.0% (482/618) and 84.0% (519/618), respectively] was lower than that of the DL system [86.2% (533/618)]. Breast density significantly affected the detection by two junior radiologists (both P=0.030), but not by the DL system (P=0.385). The AUC for classifying masses as negative (BI-RADS 1, 2, 3) or positive (BI-RADS 4A, 4B, 4C, 5) for the DL system was significantly higher compared to those of the two junior radiologists, but not significantly different compared to seniors [DL system, 0.697; junior, 0.612 and 0.620 (P=0.021, 0.019); senior in consensus, 0.748 (P=0.071)].
UNASSIGNED: The CNN-based DL system could assist junior radiologists in improving mass detection and is not affected by breast density. This DL system may have clinical utility in women with dense breasts, including reducing the impact caused by inexperienced radiologists and the potential for missed diagnoses.

BACKGROUND: Breast cancer is one of the leading causes of mortality among women. In addition, 1 in 8 women and 1 in 833 men will be diagnosed with breast cancer in 2022. The detection of breast cancer can not only lower treatment costs but also increase survival rates. Due to increased cancer awareness, more women are undergoing breast cancer screening, leading to more cases being diagnosed worldwide, but doctors\' ability to analyze these images is limited. As a result, they get overloaded leading to misinterpretations. The advent of computer-aided diagnosis (CAD) minimized man\'s involvement and achieved good results. CAD helps medical doctors automatically detect and analyze abnormalities found in the breast. Such abnormalities may be benign or malignant tumors.
OBJECTIVE: The goal of this study is to evaluate the effectiveness of using seven layers to classify breast cancer as either benign or malignant using mammograms.
METHODS: The open-source MIAS dataset of 322 images was used for our study, of which 207 were normal images and 115 were abnormal images. The proposed CNN model convolves an image into seven layers that extract features from the input images, and these features are used to classify breast cancer as malignant or benign.
RESULTS: The proposed CNN used a limited data set and achieved the best result compared to previous work. The method achieved results with a 0.39% loss, 99.89% accuracy, 99.85% precision, 99.89% recall, 99.87% F1-score, and an area under the curve noted to be 100.0%.
CONCLUSIONS: CNN uses a small amount of data to determine abnormalities; the method will assist a medical doctor in determining whether or not a specific patient has cancer.

Bariatric surgery is one of the most effective methods for treating obesity. It can effectively reduce body weight and reduce the incidence of obesity-related breast cancer. However, there are different conclusions about how bariatric surgery changes breast density. The purpose of this study was to clarify the changes in breast density from before to after bariatric surgery.
The relevant literature was searched through PubMed and Embase to screen for studies. Meta-analysis was used to clarify the changes in breast density from before to after bariatric surgery.
A total of seven studies were included in this systematic review and meta-analysis, including a total of 535 people. The average body mass index decreased from 45.3 kg/m2 before surgery to 34.4 kg/m2 after surgery. By the Breast Imaging Reporting and Data System score, the proportion of grade A breast density from before to after bariatric surgery decreased by 3.83% (183 vs. 176), grade B (248 vs. 263) increased by 6.05%, grade C (94 vs. 89) decreased by 5.32%, and grade D (1 vs. 4) increased by 300%. There was no significant change in breast density from before to after bariatric surgery (OR=1.27, 95% confidence interval (CI) [0.74, 2.20], P=0.38). By the Volpara density grade score, postoperative volumetric breast density increased (standardized mean difference = -0.68, 95% CI [-1.08, -0.27], P = 0.001).
Breast density increased significantly after bariatric surgery, but this depended on the method of detecting breast density. Further randomized controlled studies are needed to validate our conclusions.

Deep convolutional neural networks (CNNs) have been widely used in various medical imaging tasks. However, due to the intrinsic locality of convolution operations, CNNs generally cannot model long-range dependencies well, which are important for accurately identifying or mapping corresponding breast lesion features computed from unregistered multiple mammograms. This motivated us to leverage the architecture of Multi-view Vision Transformers to capture long-range relationships of multiple mammograms from the same patient in one examination. For this purpose, we employed local transformer blocks to separately learn patch relationships within four mammograms acquired from two-view (CC/MLO) of two-side (right/left) breasts. The outputs from different views and sides were concatenated and fed into global transformer blocks, to jointly learn patch relationships between four images representing two different views of the left and right breasts. To evaluate the proposed model, we retrospectively assembled a dataset involving 949 sets of mammograms, which included 470 malignant cases and 479 normal or benign cases. We trained and evaluated the model using a five-fold cross-validation method. Without any arduous preprocessing steps (e.g., optimal window cropping, chest wall or pectoral muscle removal, two-view image registration, etc.), our four-image (two-view-two-side) transformer-based model achieves case classification performance with an area under ROC curve (AUC = 0.818 ± 0.039), which significantly outperforms AUC = 0.784 ± 0.016 achieved by the state-of-the-art multi-view CNNs (p = 0.009). It also outperforms two one-view-two-side models that achieve AUC of 0.724 ± 0.013 (CC view) and 0.769 ± 0.036 (MLO view), respectively. The study demonstrates the potential of using transformers to develop high-performing computer-aided diagnosis schemes that combine four mammograms.

This study aimed to investigate breast cancer screening practices and associated factors among Chinese-Australian women. A cross-sectional quantitative survey method including convenience and snowball sampling was used to recruit 115 Chinese-Australian women living in Sydney, using a self-administered survey. In all, 69.8% of participants reported recent clinical breast examinations and 73.3% had mammograms. Age, religion, employment status, and length of residence were associated with having a clinical breast examination. Income was related to having a mammogram. Associations between knowledge of breast cancer, cancer-related beliefs, and screening participation were found. Length of residence in Australia was the strongest predictor of having a clinical breast examination and mammogram. The most common barrier to mammography was if women felt that doctors did not recommend it to them. Chinese-Australian women need to be educated about awareness of their usual breast health to be aware of any changes, especially if women are not eligible for mammography or have difficulty in accessing health services. Tailored programs, improving screening experiences, and minimizing perceived barriers are needed to promote early detection of breast cancer among Chinese-Australian women.

Many existing approaches for mammogram analysis are based on single view. Some recent DNN-based multi-view approaches can perform either bilateral or ipsilateral analysis, while in practice, radiologists use both to achieve the best clinical outcome. MommiNet is the first DNN-based tri-view mass identification approach, which can simultaneously perform bilateral and ipsilateral analysis of mammographic images, and in turn, can fully emulate the radiologists\' reading practice. In this paper, we present MommiNet-v2, with improved network architecture and performance. Novel high-resolution network (HRNet)-based architectures are proposed to learn the symmetry and geometry constraints, to fully aggregate the information from all views for accurate mass detection. A multi-task learning scheme is adopted to incorporate both Breast Imaging-Reporting and Data System (BI-RADS) and biopsy information to train a mass malignancy classification network. Extensive experiments have been conducted on the public DDSM (Digital Database for Screening Mammography) dataset and our in-house dataset, and state-of-the-art results have been achieved in terms of mass detection accuracy. Satisfactory mass malignancy classification result has also been obtained on our in-house dataset.

OBJECTIVE: Ultrasound (US) and mammogram (MMG) are the two most common breast cancer (BC) screening tools. This study aimed to assess how the combination of circulating tumor cells (CTC) with US and MMG would improve the diagnostic performance.
METHODS: CTC detection and imaging examinations, US and MMG, were performed in 238 treatment-naive BC patients, 217 patients with benign breast diseases (BBD), and 20 healthy females. Correlations of CTC, US and MMG with patients\' clinicopathological characteristics were evaluated. Diagnostic performances of CTC, US and MMG were estimated by the receiver operating characteristic curves.
RESULTS: CTC, US and MMG could all distinguish BC patients from the control (p < 0.0001). Area under curve (AUC) of CTC, US and MMG are 0.855, 0.861 and 0.759, respectively. While US has the highest sensitivity of 0.79, CTC and MMG have the same specificity of 0.92. Notably, CTC has the highest accuracy of 0.83. Combination with CTC increases the AUC of US and MMG to 0.922 and 0.899, respectively. Combining MMG with CTC or US increases the sensitivity of MMG to 0.87, however \"CTC + MMG\" has a higher specificity of 0.85. \"CTC + US\" performs the best in BC diagnosis, followed by \"CTC + MMG\" and then \"US + MMG\".
CONCLUSIONS: CTC can be used as a diagnostic aid for BC screening. Combination with CTC increases the diagnostic potency of conventional BC screening imaging examinations, US and MMG, in BC diagnosis, especially for MMG.

In breast mass detection, there are many different sizes of masses in the image. However, when the existing target detection model is directly used to detect the breast mass, it is easy to appear the phenomenon of misdetection and missed detection. Therefore, in order to improve the detection accuracy of breast masses, this paper proposed a target detection model D-Mask R-CNN based on Mask R-CNN, which is suitable for breast masses detection. Firstly, this paper improved the internal structure of FPN, and modified the lateral connection mode in the original FPN structure to dense connection. Secondly, modified the size of the anchor of RPN to improve the location accuracy of breast masses. Finally, Soft-NMS was used to replace the NMS in the original model to reduce the possibility that the correct prediction results may be eliminated during the NMS process. This paper used the CBIS-DDSM dataset for all experiments. The results showed that the mAP value of the improved model for detecting breast masses reached 0.66 in the test set, which was 0.05 higher than that of the original Mask R-CNN.

Detecting breast soft-tissue lesions including masses, structural distortions and asymmetries is of great importance due to the high risk leading to breast cancer. Most existing deep learning based approaches detect lesions with only unilateral images. However, multi-view mammogram images provide highly related and complementary information which helps to make the clinical analysis more comprehensive and reliable. In this paper, we propose a multi-view network for breast soft-tissue lesion detection called C2-Net (Compare and Contrast, C2) that fuses information across different views. The proposed model contains the following three modules. The spatial context enhancing (SCE) module compares ipsilateral views and extracts complementary features to model lesion inherent 3D structure. The multi-scale kernel pooling (MKP) module contrasts contralateral views with added misalignment tolerance. Finally, the logic guided fusion (LGF) module fuses multi-view features by enhancing logic modeling capacity. Experimental results on both the public DDSM dataset and the in-house multi-center dataset demonstrate that the proposed method has achieved state-of-the-art performance.