INTRODUCTION: Breast cancer is a leading cause of cancer death among women, and its age-standardised incidence rate is one of the highest in Asia. We aimed to review studies on barriers to breast cancer screening to inform future policies in Singapore. METHOD: This was a literature review of both quantitative and qualitative studies published between 2012 and 2020 using PubMed, Google Scholar and Cochrane databases, which analysed the perceptions and behaviours of women towards breast cancer screening in Singapore. RESULTS: Through a thematic analysis based on the Health Belief Model, significant themes associated with low breast cancer screening uptake in Singapore were identified. The themes are: (1) high perceived barriers versus benefits, including fear of the breast cancer screening procedure and its possible outcomes, (2) personal challenges that impede screening attendance and paying for screening and treatment, and (3) low perceived susceptibility to breast cancer. CONCLUSION Perceived costs/barriers vs benefits of screening appear to be the most common barriers to breast cancer screening in Singapore. Based on the barriers identified, increasing convenience to get screened, reducing mammogram and treatment costs, and improving engagement with support groups are recommended to improve the screening uptake rate in Singapore.
Breast Neoplasms/epidemiology* ; Early Detection of Cancer ; Female ; Health Knowledge, Attitudes, Practice ; Humans ; Mammography ; Mass Screening ; Singapore/epidemiology*

In order to overcome the shortcomings of high false positive rate and poor generalization in the detection of microcalcification clusters regions, this paper proposes a method combining discriminative deep belief networks (DDBNs) to automatically and quickly locate the regions of microcalcification clusters in mammograms. Firstly, the breast region was extracted and enhanced, and the enhanced breast region was segmented to overlapped sub-blocks. Then the sub-block was subjected to wavelet filtering. After that, DDBNs model for breast sub-block feature extraction and classification was constructed, and the pre-trained DDBNs was converted to deep neural networks (DNN) using a softmax classifier, and the network is fine-tuned by back propagation. Finally, the undetected mammogram was inputted to complete the location of suspicious lesions. By experimentally verifying 105 mammograms with microcalcifications from the Digital Database for Screening Mammography (DDSM), the method obtained a true positive rate of 99.45% and a false positive rate of 1.89%, and it only took about 16 s to detect a 2 888 × 4 680 image. The experimental results showed that the algorithm of this paper effectively reduced the false positive rate while ensuring a high positive rate. The detection of calcification clusters was highly consistent with expert marks, which provides a new research idea for the automatic detection of microcalcification clusters area in mammograms.
Algorithms ; Breast Neoplasms/diagnostic imaging* ; Calcinosis/diagnostic imaging* ; Early Detection of Cancer ; Humans ; Mammography ; Neural Networks, Computer

Diagnosis, Differential ; Female ; Granulomatous Mastitis/drug therapy* ; Humans ; Mammography

Mammography is the primary screening method for breast cancers. However, the sensitivity of mammographic screening is lower for dense breasts, which are an independent risk factor for breast cancers. Automated breast ultrasound (ABUS) is used as an adjunct to mammography for screening breast cancers in asymptomatic women with dense breasts. It is an effective screening modality with diagnostic accuracy comparable to that of handheld ultrasound (HHUS). Radiologists should be familiar with the unique display mode, imaging features, and artifacts in ABUS, which differ from those in HHUS. The purpose of this study was to provide a comprehensive review of the clinical significance of dense breasts and ABUS screening, describe the unique features of ABUS, and introduce the method of use and interpretation of ABUS.]]>
Artifacts ; Breast Neoplasms ; Breast ; Early Detection of Cancer ; Female ; Humans ; Mammography ; Mass Screening ; Methods ; Risk Factors ; Ultrasonography

OBJECTIVE: To understand the distribution of the average glandular dose (AGD) in mammography by investigating 1,828 exposure parameters of 8 mammography machines in three cities, by using random sampling. METHODS: A survey of 8 mammography machines in three different cities, sampled using stratified random sampling methods, was performed, and 1,828 mammography exposure parameters were recorded. Incident air kerma (k) was measured by Quality-Assurance (QA) dosimeters, and AGD was calculated by series conversion coefficients based on a 3D detailed Monte Carlo breast model, published by Wang et al. RESULTS: The distribution of compressed breast thickness (CBT) fitted a normal distribution, while that of AGD fitted a skewed distribution. The mean value of CBT in a medio-lateral oblique (MLO) view was about 5.6% higher than that in the craniocaudal (CC) view, with significant statistical difference; mean value of AGD and CBT in the sample was 1.3 mGy and 4.6 cm, respectively. The AGD trended upward with increasing CBT, similar to the results of other researches. CONCLUSION The mean AGD and CBT levels in our study for mammography practice in China were 1.3 mGy and 4.6 cm, respectively. AGD is influenced by manufacturer-specific variation as machine response to CBT changes and target/filter combination. The present study can provide evidence for establishing a diagnostic reference level in China.
Adult ; China ; Female ; Humans ; Mammography ; statistics & numerical data ; Middle Aged ; Radiation Dosage

Three-dimensional automated breast ultrasonography (ABUS) has been approved for screening Epub ahead of print studies as an adjunct to mammography. ABUS provides proper orientation and documentation, resulting in better reproducibility. Optimal image quality is essential for a proper diagnosis, and high-quality images should be ensured when ABUS is used in clinical settings. Image quality in ABUS is highly dependent on the acquisition procedure. Artifacts can interfere with the visibility of abnormalities, reduce the overall image quality, and introduce clinical and technical problems. Nipple shadow and reverberation artifacts are some of the artifacts frequently encountered in ABUS. Radiologists should be familiar with proper image acquisition techniques and possible artifacts in order to acquire high-quality images.
Artifacts* ; Breast* ; Diagnosis ; Early Detection of Cancer ; Mammography ; Mass Screening ; Nipples ; Ultrasonography, Mammary*

OBJECTIVE: To develop a digital breast tomosynthesis (DBT) imaging system with optimizes imaging chain. METHODS: Based on 3D tomography and DBT imaging scanning, we analyzed the methods for projection data correction, geometric correction, projection enhancement, filter modulation, and image reconstruction, and established a hardware testing platform. In the experiment, the standard ACR phantom and high-resolution phantom were used to evaluate the system stability and noise level. The patient projection data of commercial equipment was used to test the effect of the imaging algorithm. RESULTS: In the high-resolution phantom study, the line pairs were clear without confusing artifacts in the images reconstructed with the geometric correction parameters. In ACR phantom study, the calcified foci, cysts, and fibrous structures were more clearly defined in the reconstructed images after filtering and modulation. The patient data study showed a high contrast between tissues, and the lesions were more clearly displayed in the reconstructed image. CONCLUSIONS This DBT imaging system can be used for mammary tomography with an image quality comparable to that of commercial DBT systems to facilitate imaging diagnosis of breast diseases.
Algorithms ; Artifacts ; Breast ; diagnostic imaging ; Female ; Humans ; Mammography ; methods ; Phantoms, Imaging ; Radiographic Image Enhancement ; methods

OBJECTIVE: To develop a deep features-based model to classify benign and malignant breast lesions on full- filed digital mammography. METHODS: The data of full-filed digital mammography in both craniocaudal view and mediolateral oblique view from 106 patients with breast neoplasms were analyzed. Twenty-three handcrafted features (HCF) were extracted from the images of the breast tumors and a suitable feature set of HCF was selected using -test. The deep features (DF) were extracted from the 3 pre-trained deep learning models, namely AlexNet, VGG16 and GoogLeNet. With abundant breast tumor information from the craniocaudal view and mediolateral oblique view, we combined the two extracted features (DF and HCF) as the two-view features. A multi-classifier model was finally constructed based on the combined HCF and DF sets. The classification ability of different deep learning networks was evaluated. RESULTS: Quantitative evaluation results showed that the proposed HCF+DF model outperformed HCF model, and AlexNet produced the best performances among the 3 deep learning models. CONCLUSIONS The proposed model that combines DF and HCF sets of breast tumors can effectively distinguish benign and malignant breast lesions on full-filed digital mammography.
Breast Neoplasms ; classification ; diagnostic imaging ; Deep Learning ; Diagnosis, Computer-Assisted ; methods ; Female ; Humans ; Mammography ; methods

In this paper, the focus of technical review of the registration application data of digital Breast Tomosynthesis Mammography System was sorted out, so as to provide reference for researchers and manufacturers in China when applying for registration and preparation of such products.
Breast ; diagnostic imaging ; Breast Neoplasms ; diagnostic imaging ; China ; Humans ; Mammography ; instrumentation ; standards ; Radiographic Image Enhancement ; standards ; Risk Factors

PURPOSE: Women with dense breast are known to be at high risk for breast cancer, but their prevalence and number of Korean women are unknown. The current study was to investigate the distribution of mammographic breast density by age of women undergoing screening mammography, and to estimate the prevalence of Korean women with dense breasts, quantitatively. MATERIALS AND METHODS: For obtaining a nationwide representative sample, 6,481 mammograms were collected from 86 screening units participated in the National Cancer Screening Program for breast cancer. Based on the American College of Radiology Breast Imaging Reporting and Data System classification, breast density was evaluated by six breast radiologists, qualitatively. We applied these breast density distributions to age-specific counts of the Korean women population derived to mid-year 2017 to estimate the number of Korean women with dense breasts. RESULTS: Overall, 54.4% (95% confidence interval [CI], 52.9% to 55.8%) of women 40 to 69 years of age had heterogeneously or extremely dense breasts, and this proportion was inversely associated with age. Based on the age distribution of Korean women, we estimated that 6,083,000 women (95% CI, 5,919,600 to 6,245,600) age 40-69 years in Korean have dense breasts. Women aged 40-49 years (n=3,450,000) accounted for 56.7% of this group. CONCLUSION: More than half of Korean women aged 40 and over have dense breasts. To prevent breast cancer effectively and efficiently, it is necessary to develop a new personalized prevention strategy considering her status of breast density.
Age Distribution ; Breast Neoplasms ; Breast ; Classification ; Cross-Sectional Studies ; Early Detection of Cancer ; Female ; Humans ; Information Systems ; Korea ; Mammography ; Mass Screening ; Prevalence