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: Digital mammography (DM) has replaced screen-film mammography (SFM). However, findings of comparisons between the performance indicators of DM and SFM for breast-cancer screening have been inconsistent. Moreover, the summarized results from studies comparing the performance of screening mammography according to device type vary over time. Therefore, this study aimed to compare the performance of DM and SFM using recently published data. METHODS: The MEDLINE, Embase, and Cochrane Library databases were searched for paired studies, cohorts, and randomized controlled trials published through 2018 that compared the performance of DM and SFM. All studies comparing the diagnostic accuracy of DM and SFM in asymptomatic, average-risk women aged 40 years and older were included. Two reviewers independently assessed the study quality and extracted the data. RESULTS: Thirteen studies were included in the meta-analysis. The pooled sensitivity (DM, 0.76 [95% confidence interval {CI}, 0.70–0.81]; SFM, 0.76 [95% CI, 0.70–0.81]), specificity (DM, 0.96 [95% CI, 0.94–0.97]; SFM, 0.97 [95% CI, 0.94–0.98]), and area under the receiver-operating characteristic curve (DM, 0.94 [95% CI, 0.92–0.96]; SFM, 0.92 [95% CI, 0.89–0.94]) were similar for both DM and SFM. The pooled screening performance indicators reinforced superior accuracy of full-field DM, which is a more advanced type of mammography, than SFM. The advantage of DM appeared greater among women aged 50 years or older. There was high heterogeneity among studies in the pooled sensitivity, specificity, and overall diagnostic accuracy estimates. Stratifying by study design (prospective or retrospective) and removing studies with a 2-year or greater follow-up period resulted in homogeneous overall diagnostic accuracy estimates. CONCLUSION: The breast-cancer screening performance of DM is similar to that of SFM. The diagnostic performance of DM depends on the study design, and, in terms of performance, full-field DM is superior to SFM, unlike computed radiography systems.
Breast Neoplasms ; Cohort Studies ; Early Detection of Cancer ; Female ; Follow-Up Studies ; Humans ; Mammography ; Mass Screening ; Population Characteristics ; Radiography ; Sensitivity and Specificity

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 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