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Scientific Data May 2023Mammography, or breast X-ray imaging, is the most widely used imaging modality to detect cancer and other breast diseases. Recent studies have shown that deep...
Mammography, or breast X-ray imaging, is the most widely used imaging modality to detect cancer and other breast diseases. Recent studies have shown that deep learning-based computer-assisted detection and diagnosis (CADe/x) tools have been developed to support physicians and improve the accuracy of interpreting mammography. A number of large-scale mammography datasets from different populations with various associated annotations and clinical data have been introduced to study the potential of learning-based methods in the field of breast radiology. With the aim to develop more robust and more interpretable support systems in breast imaging, we introduce VinDr-Mammo, a Vietnamese dataset of digital mammography with breast-level assessment and extensive lesion-level annotations, enhancing the diversity of the publicly available mammography data. The dataset consists of 5,000 mammography exams, each of which has four standard views and is double read with disagreement (if any) being resolved by arbitration. The purpose of this dataset is to assess Breast Imaging Reporting and Data System (BI-RADS) and breast density at the individual breast level. In addition, the dataset also provides the category, location, and BI-RADS assessment of non-benign findings. We make VinDr-Mammo publicly available as a new imaging resource to promote advances in developing CADe/x tools for mammography interpretation.
Topics: Humans; Benchmarking; Breast; Breast Diseases; Computers; Mammography
PubMed: 37173336
DOI: 10.1038/s41597-023-02100-7 -
Journal of General Internal Medicine Jul 2017As breast cancer screening guidelines have changed recently, additional investigation is needed to understand changes in women's behavior after using breast cancer... (Meta-Analysis)
Meta-Analysis Review
BACKGROUND
As breast cancer screening guidelines have changed recently, additional investigation is needed to understand changes in women's behavior after using breast cancer screening patient decision aids (BCS-PtDAs) and the potential effect on mammography utilization. This systematic review and meta-analysis sought to evaluate the effect of BCS-PtDAs on changes in women's intentions to undergo screening mammography and whether women deciding to begin or discontinue screening mammography displayed similar changes in screening intentions after using a BCS-PtDA.
METHODS
We searched Medline, Scopus, PsycINFO, CENTRAL, Health and Psychosocial Instruments, Health Technology Assessment Database, PsycARTICLES, and cited references in eligible papers for randomized controlled trials (RCTs) and observational studies, published through August 24, 2016. The proportions of women who did and not intend to undergo screening and who were uncertain about undergoing screening mammography were pooled, using risk ratios (RR) and random effects. According to the protocol, RCTs or observational studies and any language were considered eligible for systematic review if they included data about women for which shared decision making is recommended.
RESULTS
We ultimately included six studies with screening intention data for 2040 women. Compared to usual care, the use of BCS-PtDAs in three RCTs resulted in significantly more women deciding not to undergo screening mammography (RR 1.48 [95% CI 1.04-2.13]; P = 0.03), particularly for younger (38-50 years) women (1.77 [1.34-2.34]; P < 0.001). The use of BCS-PtDAs had a non-significant effect on the intentions of older women (69-89 years) to discontinue screening.
CONCLUSIONS
The use of BCS-PtDAs increased younger women's reluctance to undergo screening for breast cancer. The implementation of such BCS-PtDAs in clinical practice would be expected to result in a 77% increase in the number of younger women (aged 38-50) who do not intend to be screened, and as a consequence, may reduce utilization of screening mammography.
REGISTRATION
The protocol of this review is registered in the PROSPERO database, #CRD42016036695.
Topics: Breast Neoplasms; Decision Making; Decision Support Techniques; Early Detection of Cancer; Female; Humans; Mammography; Patient Participation; Randomized Controlled Trials as Topic
PubMed: 28289963
DOI: 10.1007/s11606-017-4027-9 -
Anticipating artificial intelligence in mammography screening: views of Swedish breast radiologists.BMJ Health & Care Informatics May 2023Artificial intelligence (AI) is increasingly tested and integrated into breast cancer screening. Still, there are unresolved issues regarding its possible ethical,...
OBJECTIVES
Artificial intelligence (AI) is increasingly tested and integrated into breast cancer screening. Still, there are unresolved issues regarding its possible ethical, social and legal impacts. Furthermore, the perspectives of different actors are lacking. This study investigates the views of breast radiologists on AI-supported mammography screening, with a focus on attitudes, perceived benefits and risks, accountability of AI use, and potential impact on the profession.
METHODS
We conducted an online survey of Swedish breast radiologists. As early adopter of breast cancer screening, and digital technologies, Sweden is a particularly interesting case to study. The survey had different themes, including: attitudes and responsibilities pertaining to AI, and AI's impact on the profession. Responses were analysed using descriptive statistics and correlation analyses. Free texts and comments were analysed using an inductive approach.
RESULTS
Overall, respondents (47/105, response rate 44.8%) were highly experienced in breast imaging and had a mixed knowledge of AI. A majority (n=38, 80.8%) were positive/somewhat positive towards integrating AI in mammography screening. Still, many considered there to be potential risks to a high/somewhat high degree (n=16, 34.1%) or were uncertain (n=16, 34.0%). Several important uncertainties were identified, such as defining liable actor(s) when AI is integrated into medical decision-making.
CONCLUSIONS
Swedish breast radiologists are largely positive towards integrating AI in mammography screening, but there are significant uncertainties that need to be addressed, especially regarding risks and responsibilities. The results stress the importance of understanding actor-specific and context-specific challenges to responsible implementation of AI in healthcare.
Topics: Humans; Female; Artificial Intelligence; Sweden; Mammography; Breast Neoplasms; Radiologists
PubMed: 37217249
DOI: 10.1136/bmjhci-2022-100712 -
BMC Cancer Jul 2022U.S. professional organizations have provided conflicting recommendations on annual vs. biennial mammography screening. Potential harms of more frequent screening...
BACKGROUND
U.S. professional organizations have provided conflicting recommendations on annual vs. biennial mammography screening. Potential harms of more frequent screening include increased anxiety and costs of false positive results, including unnecessary breast biopsies and overdiagnosis.
OBJECTIVE
To characterize current practices and beliefs surrounding mammography screening frequency and perspectives on using risk-based screening to inform screening intervals.
DESIGN
Semi-structured interviews informed by the Consolidated Framework for Implementation Research (CFIR).
PARTICIPANTS
Patients, primary care providers (PCPs), third-party stakeholders (breast radiologists, radiology administrators, patient advocates).
MAIN MEASURES
Qualitative data, with a codebook developed based upon prespecified implementation science constructs.
KEY RESULTS
We interviewed 25 patients, 11 PCPs, and eight key stakeholders, including three radiologists, two radiology administrators, and three patient advocates. Most patients reported having annual mammograms, however, half believed having mammograms every two years was acceptable. Some women were worried early breast cancer would be missed if undergoing biennial screening. PCPs were equally split between recommending annual and biennial mammograms. Although PCPs were interested in using breast cancer risk models to inform screening decisions, concerns raised include time burden and lack of familiarity with breast cancer risk assessment tools. All breast radiologists believed patients should receive annual mammograms, while patient advocates and radiology administrators were split between annual vs. biennial. Radiologists were worried about missing breast cancer diagnoses when mammograms are not performed yearly. Patient advocates and radiology administrators were more open to biennial mammograms and utilizing risk-based screening.
CONCLUSIONS
Uncertainty remains across stakeholder groups regarding appropriate mammogram screening intervals. Radiologists recommend annual mammography, whereas patients and PCPs were evenly split between annual vs. biennial screening, although both favored annual screening among higher-risk women. Breast cancer risk assessment tools may help facilitate decisions about screening intervals, but face barriers to widespread implementation in the primary care setting. These results will inform future implementation strategies to adopt risk-stratified breast cancer screening.
Topics: Breast Neoplasms; Early Detection of Cancer; Female; Humans; Mammography; Mass Screening; Primary Health Care; Time Factors
PubMed: 35897000
DOI: 10.1186/s12885-022-09900-x -
Sensors (Basel, Switzerland) Jan 2023Convolutional Neural Networks (CNN) have received a large share of research in mammography image analysis due to their capability of extracting hierarchical features...
Convolutional Neural Networks (CNN) have received a large share of research in mammography image analysis due to their capability of extracting hierarchical features directly from raw data. Recently, Vision Transformers are emerging as viable alternative to CNNs in medical imaging, in some cases performing on par or better than their convolutional counterparts. In this work, we conduct an extensive experimental study to compare the most recent CNN and Vision Transformer architectures for whole mammograms classification. We selected, trained and tested 33 different models, 19 convolutional- and 14 transformer-based, on the largest publicly available mammography image database OMI-DB. We also performed an analysis of the performance at eight different image resolutions and considering all the individual lesion categories in isolation (masses, calcifications, focal asymmetries, architectural distortions). Our findings confirm the potential of visual transformers, which performed on par with traditional CNNs like ResNet, but at the same time show a superiority of modern convolutional networks like EfficientNet.
Topics: Mammography; Neural Networks, Computer; Image Processing, Computer-Assisted; Databases, Factual
PubMed: 36772268
DOI: 10.3390/s23031229 -
Sensors (Basel, Switzerland) Feb 2022Breast cancer is widespread around the world and can be cured if diagnosed at an early stage. Digital mammograms are used as the most effective imaging modalities for...
Breast cancer is widespread around the world and can be cured if diagnosed at an early stage. Digital mammograms are used as the most effective imaging modalities for the diagnosis of breast cancer. However, mammography images suffer from low contrast, background noise as well as contrast as non-coherency among the regions, and these factors makes breast cancer diagnosis challenging. These problems can be overcome by using a new image enhancement technique. The objective of this research work is to enhance mammography images to improve the overall process of segmentation and classification of breast cancer diagnosis. We proposed the image enhancement for mammogram images, as well as the ablation of the pectoral muscle. The image enhancement technique involves several steps. In the first step, we process the mammography images in three channels (red, green and blue), the second step is based on the uniformity of the background on morphological operations, and the third step is to obtain a well-contrasted image using principal component analysis (PCA). The fourth step is based on the removal of the pectoral muscle using a seed-based region growth technique, and the last step contains the coherence of the different regions of the image using a second order Gaussian Laplacian (LoG) and an oriented diffusion filter to obtain a much-improved contrast image. The proposed image enhancement technique is tested with our data collected from different hospitals in Qassim health cluster Qassim province Saudi Arabia, and it contains the five Breast Imaging and Reporting System (BI-RADS) categories and this database contained 11,194 images (the images contain carnio-caudal (CC) view and mediolateral oblique(MLO) view of mammography images), and we used approximately 700 images to validate our database. We have achieved improved performance in terms of peak signal-to-noise ratio, contrast, and effective measurement of enhancement (EME) as well as our proposed image enhancement technique outperforms existing image enhancement methods. This performance of our proposed method demonstrates the ability to improve the diagnostic performance of the computerized breast cancer detection method.
Topics: Algorithms; Breast Neoplasms; Female; Humans; Image Enhancement; Mammography; Pectoralis Muscles
PubMed: 35271015
DOI: 10.3390/s22051868 -
Journal of General Internal Medicine Nov 2018Decades of persuasive messages have reinforced the importance of traditional screening mammography at regular intervals. A potential new paradigm, risk-based screening,...
BACKGROUND
Decades of persuasive messages have reinforced the importance of traditional screening mammography at regular intervals. A potential new paradigm, risk-based screening, adjusts mammography frequency based on a woman's estimated breast cancer risk in order to maximize mortality reduction while minimizing false positives and overdiagnosis. Women's views of risk-based screening are unknown.
OBJECTIVE
To explore women's views and personal acceptability of a potential risk-based mammography screening paradigm.
DESIGN
Four semi-structured focus group discussions about screening mammography and surveys before provision of information about risk-based screening. We analyzed coded focus group transcripts using a mixed deductive (content analysis) and inductive (grounded theory) approach.
PARTICIPANTS
Convenience sample of 29 women (40-74 years old) with no personal history of breast cancer recruited by print and online media in New Hampshire and Vermont.
RESULTS
Twenty-seven out of 29 women reported having undergone mammography screening. All participants were white and most were highly educated. Some women accepted the idea that early cancer detection with traditional screening was beneficial-although many also reported hearing inconsistent recommendations from clinicians and mixed messages from media reports about mammography. Some women were familiar with a risk-based screening paradigm (primarily related to cervical cancer, n = 8) and thought matching screening mammography frequency to personal risk made sense (n = 8). Personal acceptability of risk-based screening was mixed. Some believed risk-based screening could reduce the harms of false positives and overdiagnosis (n = 7). Others thought screening less often might result in missing a dangerous diagnosis (n = 14). Many (n = 18) expressed concerns about the feasibility of risk-based screening and questioned whether breast cancer risk estimates could be accurate. Some suspected that risk-based mammography was motivated by a desire to save money (n = 6).
CONCLUSION
Some women thought risk-based screening made sense. Willingness to abandon traditional screening for the new paradigm was mixed. Broad acceptability of risk-based screening will require clearer communication about its rationale and feasibility and consistent messages from the health care team.
Topics: Adult; Aged; Breast Neoplasms; Early Detection of Cancer; False Positive Reactions; Female; Focus Groups; Humans; Mammography; Medical Overuse; Middle Aged; Qualitative Research
PubMed: 30066118
DOI: 10.1007/s11606-018-4601-9 -
AJR. American Journal of Roentgenology Dec 2022Annual surveillance mammography is recommended for breast cancer survivors on the basis of observational studies and meta-analyses showing reduced breast cancer... (Review)
Review
Annual surveillance mammography is recommended for breast cancer survivors on the basis of observational studies and meta-analyses showing reduced breast cancer mortality and improved quality of life. However, breast cancer survivors are at higher risk of subsequent breast cancer and have a fourfold increased risk of interval breast cancers compared with individuals without a personal history of breast cancer. Supplemental surveillance modalities offer increased cancer detection compared with mammography alone, but utilization is variable, and benefits must be balanced with possible harms of false-positive findings. In this review, we describe the current state of mammographic surveillance, summarize evidence for supplemental surveillance in breast cancer survivors, and explore a risk-based approach to selecting surveillance imaging strategies. Further research identifying predictors associated with increased risk of interval second breast cancers and development of validated risk prediction tools may help physicians and patients weigh the benefits and harms of surveillance breast imaging and decide on a personalized approach to surveillance for improved breast cancer outcomes.
Topics: Humans; Female; Breast Neoplasms; Quality of Life; Mammography; Breast; Survivors; Early Detection of Cancer
PubMed: 35544374
DOI: 10.2214/AJR.22.27635 -
Scientific Reports Feb 2023In this study, we aimed to evaluate the benefits and losses of mammography with and without implant displacement (ID) and propose an appropriate imaging protocol for the...
In this study, we aimed to evaluate the benefits and losses of mammography with and without implant displacement (ID) and propose an appropriate imaging protocol for the screening of breasts with implants. We evaluated mammograms of 162 breasts in 96 patients including 71 breasts with biopsy-proven cancers. Mammography of each breast included standard MLO and ID MLO images. We reviewed the mammograms using clinical image quality criteria, which consist of parameters that evaluate the proper positioning of the breast and the image resolution. Standard MLO images showed significantly higher scores for proper positioning but showed significantly lower scores for image resolution than the ID MLO images. Moreover, standard MLO images showed significantly higher kVp, mAs, and compressed breast thickness than the ID MLO images. The organ dose was also higher in the standard MLO images than in the ID MLO images, but the difference was not statistically significant. In mammography with proven cancer, ID MLO images showed significantly higher degree of cancer visibility than standard MLO images. For screening mammography in patients with breast implants, ID MLO view alone is sufficient for MLO projection with reducing the patient's radiation dose without compromising the breast cancer detection capability, especially in dense breasts with subpectoral implants.
Topics: Female; Humans; Breast; Breast Implants; Breast Neoplasms; Early Detection of Cancer; Mammography
PubMed: 36725965
DOI: 10.1038/s41598-023-28399-1 -
The British Journal of Radiology Feb 2018Detection of breast cancer is reliant on optimal breast positioning and the production of quality images. Two projections, the mediolateral oblique and craniocaudal... (Review)
Review
Detection of breast cancer is reliant on optimal breast positioning and the production of quality images. Two projections, the mediolateral oblique and craniocaudal (CC), are routinely performed. Determination of successful positioning and inclusion of all breast tissue is achieved through meeting stated image quality criteria. For the CC view, current image quality criteria are inconsistent. Absence of reliable anatomical markers, other than the nipple, further contribute to difficulties in assessing the quality of CC views. The aim of this paper was to explore published international quality standards to identify and find the origin of any CC positioning criteria which might provide for quantitative assessment. The pectoralis major (pectoral) muscle was identified as a key posterior anatomical structure to establish optimum breast tissue inclusion on mammographic projections. It forms the first two of the three main CC metrics that are frequently reported (1) visualization of the pectoral muscle, (2) measurement of the posterior nipple line and (3) depiction of retroglandular fat. This literature review explores the origin of the three metrics, and discusses three key publications, spanning 1992 to 1994, on which subsequent image quality standards have been based. The evidence base to support published CC metrics is sometimes not specified and more often, the same set of publications are cited, most often without critical evaluation. To conclude, there remains uncertainty if the metrics explored for the CC view support objective evaluation and reproducibility to confirm optimal breast positioning and quality images.
Topics: Female; Humans; Mammography; Patient Positioning; Pectoralis Muscles
PubMed: 29125335
DOI: 10.1259/bjr.20170611