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

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 evaluate the interpretive performance and inter-observer agreement on digital mammographs among radiologists and to investigate whether radiologist characteristics affect performance and agreement. MATERIALS AND METHODS: The test sets consisted of full-field digital mammograms and contained 12 cancer cases among 1000 total cases. Twelve radiologists independently interpreted all mammograms. Performance indicators included the recall rate, cancer detection rate (CDR), positive predictive value (PPV), sensitivity, specificity, false positive rate (FPR), and area under the receiver operating characteristic curve (AUC). Inter-radiologist agreement was measured. The reporting radiologist characteristics included number of years of experience interpreting mammography, fellowship training in breast imaging, and annual volume of mammography interpretation. RESULTS: The mean and range of interpretive performance were as follows: recall rate, 7.5% (3.3–10.2%); CDR, 10.6 (8.0–12.0 per 1000 examinations); PPV, 15.9% (8.8–33.3%); sensitivity, 88.2% (66.7–100%); specificity, 93.5% (90.6–97.8%); FPR, 6.5% (2.2–9.4%); and AUC, 0.93 (0.82–0.99). Radiologists who annually interpreted more than 3000 screening mammograms tended to exhibit higher CDRs and sensitivities than those who interpreted fewer than 3000 mammograms (p = 0.064). The inter-radiologist agreement showed a percent agreement of 77.2–88.8% and a kappa value of 0.27–0.34. Radiologist characteristics did not affect agreement. CONCLUSION: The interpretative performance of the radiologists fulfilled the mammography screening goal of the American College of Radiology, although there was inter-observer variability. Radiologists who interpreted more than 3000 screening mammograms annually tended to perform better than radiologists who did not.
Area Under Curve ; Breast ; Fellowships and Scholarships ; Mammography ; Mass Screening ; Medical Audit ; Observer Variation ; ROC Curve ; Sensitivity and Specificity

Breast magnetic resonance imaging (MRI) has been increasingly utilized, especially in screening for high-risk cases, because of its high sensitivity and superior ability to detect cancers as compared with mammography and ultrasound. Several limitations such as higher cost, longer examination time, longer interpretation time, and low availability have hindered the wider application of MRI, especially for screening of average-risk women. To overcome some of these limitations and increase access to MRI screening, an abbreviated breast MRI protocol has been introduced. Abbreviated breast MRI is becoming popular and challenges the status quo. This review aims to present an overview of abbreviated MRI, discuss the current findings, and introduce ongoing prospective trials.
Breast Neoplasms ; Breast ; Female ; Humans ; Magnetic Resonance Imaging ; Mammography ; Mass Screening ; Prospective Studies ; Ultrasonography

mammography is an effective margin assessment method in Asian women. Thus, 182 patients who underwent breast-conserving surgery (BCS) were evaluated. After wide excision, intraoperative specimen mammography was used to assess margin adequacy. The control group comprised 84 patients who underwent BCS and were evaluated for margin of frozen section during surgery. 61.6% patients had dense breasts and 85.7% of dense breasts could margin assess by intraoperative specimen mammography. There were no significant differences in the incidence of extremely close margins (p = 0.421) and second operation (p = 0.252) between both groups. Significant correlations were found between radiological and histological margins (R² = 0.222, p < 0.05). The frozen section analysis group had longer operative time than the specimen mammography group. The study results show that intraoperative specimen mammography of breast lesions in BCS is useful in identifying margin clearance.]]>
Asian Continental Ancestry Group ; Breast ; Breast Neoplasms ; Female ; Frozen Sections ; Humans ; Incidence ; Mammography ; Mastectomy, Segmental ; Methods ; Operative Time

mammography. There have been many efforts to develop a blood-based diagnostic assay for breast cancer diagnosis; however, none have been approved for clinical use at this time. The purpose of this study was to determine the accuracy of a novel blood-based proteomic test for aiding breast cancer diagnosis in a relatively large cohort of cancer patients.METHODS: A blood-based test using multiple reaction monitoring (MRM) measured by mass spectrometry to quantify 3 peptides (apolipoprotein C-1, carbonic anhydrase 1, and neural cell adhesion molecule L1-like protein) present in human plasma was investigated. A total of 1,129 blood samples from 575 breast cancer patients, 454 healthy controls, and 100 patients with other malignancies were used to verify and optimize the assay.RESULTS: The diagnostic sensitivity, specificity, and accuracy of the MRM-based proteomic assay were 71.6%, 85.3%, and 77%, respectively; the area under the receiver operating characteristic curve was 0.8323. The proteomic assay did not demonstrate diagnostic accuracy in patients with other types of malignancies including thyroid, pancreatic, lung, and colon cancers. The diagnostic performance of the proteomic assay was not associated with the timing of blood sampling before or after anesthesia.CONCLUSION: The data demonstrated that an MRM-based proteomic assay that measures plasma levels of three specific peptides can be a useful tool for breast cancer screening and its accuracy is cancer-type specific.]]>
Anesthesia ; Biomarkers ; Blood Proteins ; Breast Neoplasms ; Breast ; Carbonic Anhydrases ; Cohort Studies ; Colonic Neoplasms ; Diagnosis ; Humans ; Lung ; Mammography ; Mass Screening ; Mass Spectrometry ; Neural Cell Adhesion Molecules ; Peptides ; Plasma ; Proteomics ; ROC Curve ; Sensitivity and Specificity ; Thyroid Gland

OBJECTIVE: To develop a scoring system stratifying the malignancy risk of mammographic microcalcifications using the 5th edition of the Breast Imaging Reporting and Data System (BI-RADS).MATERIALS AND METHODS: One hundred ninety-four lesions with microcalcifications for which surgical excision was performed were independently reviewed by two radiologists according to the 5th edition of BI-RADS. Each category's positive predictive value (PPV) was calculated and a scoring system was developed using multivariate logistic regression. The scores for benign and malignant lesions or BI-RADS categories were compared using an independent t test or by ANOVA. The area under the receiver operating characteristic curve (AUROC) was assessed to determine the discriminatory ability of the scoring system. Our scoring system was validated using an external dataset.RESULTS: After excision, 69 lesions were malignant (36%). The PPV of BI-RADS descriptors and categories for calcification showed significant differences. Using the developed scoring system, mean scores for benign and malignant lesions or BI-RADS categories were significantly different (p < 0.001). The AUROC of our scoring system was 0.874 (95% confidence interval, 0.840–0.909) and the PPV of each BI-RADS category determined by the scoring system was as follows: category 3 (0%), 4A (6.8%), 4B (19.0%), 4C (68.2%), and 5 (100%). The validation set showed an AUROC of 0.905 and PPVs of 0%, 8.3%, 11.9%, 68.3%, and 94.7% for categories 3, 4A, 4B, 4C, and 5, respectively.CONCLUSION: A scoring system based on BI-RADS morphology and distribution descriptors could be used to stratify the malignancy risk of mammographic microcalcifications.
Breast Neoplasms ; Breast ; Dataset ; Information Systems ; Logistic Models ; Mammography ; ROC Curve ; Subject Headings