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

PURPOSE: The purpose of this study was to record and evaluate interobserver agreement as quality control for the modified categorization of screening breast ultrasound developed by the Alliance for Breast Cancer Screening in Korea (ABCS-K) for the Mammography and Ultrasonography Study for Breast Cancer Screening Effectiveness (MUST-BE) trial. METHODS: Eight breast radiologists with 4-16 years of experience participated in 2 rounds of quality control testing for the MUST-BE trial. Two investigators randomly selected 125 and 100 cases of breast lesions with different ratios of malignant and benign lesions. Two versions of the modified categorization were tested. The initially modified classification was developed after the first quality control workshop, and the re-modified classification was developed after the second workshop. The re-modified categorization established by ABCS-K added size criteria and the anterior-posterior ratio compared with the initially modified classification. After a brief lecture on the modified categorization system prior to each quality control test, the eight radiologists independently categorized the lesions using the modified categorization. Interobserver agreement was measured using kappa statistics. RESULTS: The overall kappa values for the modified categorizations indicated moderate to substantial degrees of agreement (initially modified categorization and re-modified categorization: κ=0.52 and κ=0.63, respectively). The kappa values for the subcategories of category 4 were 0.37 (95% confidence interval [CI], 0.24 to 0.52) and 0.39 (95% CI, 0.31 to 0.49), respectively. The overall kappa values for both the initially modified categorization and the re-modified categorization indicated a substantial degree of agreement when dichotomizing the interpretation as benign or suspicious. CONCLUSION: The preliminary results demonstrated acceptable interobserver agreement for the modified categorization.
Breast Neoplasms ; Breast ; Classification ; Education ; Humans ; Korea ; Mammography ; Mass Screening ; Observer Variation ; Quality Control ; Research Personnel ; Ultrasonography

PURPOSE: The purpose of this study was to assess the reliability of automated breast ultrasound (ABUS) examinations of suspicious breast masses in comparison to handheld breast ultrasound (HHUS) with regard to Breast Imaging Reporting and Data System (BI-RADS) category assessment, and to investigate the factors affecting discrepancies in categorization. METHODS: A total of 135 masses that were assessed as BI-RADS categories 4 and 5 on ABUS that underwent ultrasound (US)-guided core needle biopsy from May 2017 to December 2017 were included in this study. The BI-RADS categories were re-assessed using HHUS. Agreement of the BI-RADS categories was evaluated using kappa statistics, and the positive predictive value of each examination was calculated. Logistic regression analysis was performed to identify the mammography and US findings associated with discrepancies in the BI-RADS categorization. RESULTS: The overall agreement between ABUS and HHUS in all cases was good (79.3%, kappa=0.61, P<0.001). Logistic regression analysis revealed that accompanying suspicious microcalcifications on mammography (odds ratio [OR], 4.63; 95% confidence interval [CI], 1.83 to 11.71; P=0.001) and an irregular shape on US (OR, 5.59; 95% CI, 1.43 to 21.83; P=0.013) were associated with discrepancies in the BI-RADS categorization. CONCLUSION: The agreement between ABUS and HHUS examinations in the BI-RADS categorization of suspicious breast masses was good. The presence of suspicious microcalcifications on mammography and an irregular shape on US were factors associated with ABUS yielding a lower level of suspicion than HHUS in terms of the BI-RADS category assessment.
Biopsy, Large-Core Needle ; Breast Neoplasms ; Breast ; Information Systems ; Logistic Models ; Mammography ; 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

OBJECTIVE: To evaluate the value of digital breast tomosynthesis (DBT) in diagnosis of dense breast lesions. METHODS: Clinical and pathological data of 163 patients (58 benign lesions, 122 malignant lesions, and 180 lesions in total) with breast lesions undergoing surgical treatment in Shaoxing Central Hospital from January 2017 to December 2018 were retrospectively analyzed. The lesions were classified into non-homogeneous dense gland type and extremely dense gland type according to BI-RADS creterion. Breast MRI and DBT examinations were performed before the surgery. ROC curve was generated and the diagnostic efficacy of two examination methods for dense breast lesions was evaluated with pathological results as the gold standard. The detection rate, diagnostic accuracy of benign and malignant breast lesions were compared between two methods using chi-square test. The accuracy of lesion size preoperatively evaluated by MRI and DBT was analyzed by Pearson correlation. RESULTS: The detection rate and diagnostic accuracy for benign breast lesions by MRI were higher than those by DBT (91.4% vs. 75.9%, =5.098, <0.05 and 89.7% vs. 67.2%, =8.617, <0.01). But there were no significant differences in detection rate and accuracy for malignant lesions by MRI and DBT (98.4% vs. 95.1%, =2.068, >0.05 and 94.3% vs. 91.8%, =0.569, >0.05). The areas under the ROC curves of MRI, DBT based on BI-RADS classification were 0.910 and 0.832, respectively (=1.860, >0.05). The sensitivities of MRI, DBT to breast lesions were 93.3% and 86.7%, and the specificities were 68.3% and 79.1%. DBT and MRI measurements were positively correlated with pathological measurements (=0.887 and 0.949, all <0.01). CONCLUSIONS DBT can effectively diagnose benign and malignant breast lesions under dense gland background, and it has similar diagnostic efficacy with MRI for breast malignant lesions.
Breast Neoplasms ; Female ; Humans ; Magnetic Resonance Imaging ; Mammography ; ROC Curve ; Retrospective Studies

Objective To compare the values of dynamic enhanced magnetic resonance imaging(DCE-MRI),digital breast tomosynthesis(DBT),and digital mammography(DM)in the early detection and diagnosis of breast cancer.Methods We retrospectively analyzed the clinical and imaging data of 65 cases with early breast cancer confirmed by surgical pathology from June 2017 to December 2018.All patients underwent breast DCE-MRI,DM and DBT before surgery.The receiver operating characteristic(ROC)curves were drawn,with the pathological results as the gold standard,to evaluate the diagnostic performance of different examination methods.The areas under ROC curves(AUCs)were compared using test.The differences among DCE-MRI,DBT and DM in detecting early breast cancer were compared using chi-square test in terms of positive rates,accuracy,sensitivity,and specificity.Pearson correlation analysis was performed to assess the accuracy of these imaging methods in detecting the size of early breast cancer.Results The AUCs of DCE-MRI,DBT,and DM based on the BI-RADS classification for early diagnosis of breast cancer were 0.910,0.832,and 0.700,respectively(=2.132,=0.001);the sensitivity of DCE-MRI,DBT,and DM for early breast cancer was 92.3%,70.8%,and 52.5%,the specificity was 65.0%,85.0%,and 79.3%,and the accuracy was 83.1%,70.8%,and 50.8%,indicating that DCE-MRI(=15.330,=0.0001) and DBT(=5.450,=0.020) had significantly higher diagnostic accuracy than DM.The measurement results of DM,DBT,and DCE-MRI were positively correlated with the pathological measurements(=0.781,=0.847,=0.946;all <0.01). Conclusions DCE-MRI and DBT have higher positive rates and accuracies than DM in detecting early breast cancer.Medical institutions where DCE-MRI is still not available can use DBT to improve the early detection of breast cancer.
Breast ; diagnostic imaging ; Breast Neoplasms ; diagnostic imaging ; Female ; Humans ; Magnetic Resonance Imaging ; Mammography ; methods ; Retrospective Studies

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