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Canadian Association of Radiologists... Feb 2022Digital breast tomosynthesis (DBT) is gradually being implemented in routine clinical breast imaging practice. The technique of image acquisition reduces the confounding... (Review)
Review
Digital breast tomosynthesis (DBT) is gradually being implemented in routine clinical breast imaging practice. The technique of image acquisition reduces the confounding effect of overlapping breast tissue, which substantially affects cancer detection, abnormal recall, and interval cancer rates in a screening/ surveillance setting. In a diagnostic setting, tomosynthesis also allows for improved lesion localization and characterization over conventional imaging, which potentially improves the accuracy and improved workflow efficiency. To optimize the utility of tomosynthesis, imagers should be aware of the pertinent aspects of image acquisition as it relates to interpretation, the appearance of benign and malignant pathologies, and sources of possible misinterpretation. This article aims to provide a practical knowledge base of DBT and demonstrate its potential benefits when incorporated into routine clinical practice.
Topics: Breast; Breast Neoplasms; Early Detection of Cancer; Female; Humans; Mammography
PubMed: 34229477
DOI: 10.1177/08465371211025229 -
AJR. American Journal of Roentgenology Nov 2020Contrast-enhanced digital mammography (CEDM) combines the high spatial resolution of mammography with the improved enhancement provided by contrast medium. In this... (Review)
Review
Contrast-enhanced digital mammography (CEDM) combines the high spatial resolution of mammography with the improved enhancement provided by contrast medium. In this article, CEDM technique-the current and potential clinical applications and current challenges-will be reviewed. CEDM is a promising technique in the supplemental evaluation of patients with mammographically inconclusive findings and potentially in the screening of women with mammographically dense breasts. CEDM is emerging as a cost-effective alternative to dynamic contrast-enhanced MRI to stage newly diagnosed breast cancer and evaluate response to neoadjuvant chemotherapy.
Topics: Breast Neoplasms; Contrast Media; Female; Humans; Mammography
PubMed: 32877247
DOI: 10.2214/AJR.19.22412 -
Medical Physics Jun 2023Beginning around 1972 with the introduction of CT, a steady transition from analog to digital imaging in radiology took place. Here, I offer a personal perspective of... (Review)
Review
Beginning around 1972 with the introduction of CT, a steady transition from analog to digital imaging in radiology took place. Here, I offer a personal perspective of the exciting multi-institutional and multidisciplinary team effort of developing digital mammography. That effort required the collaboration of visionary individuals in academic research labs, industry, and the clinical arena, catalyzed by a focused commitment from government (NCI and The Office of Women's Health). This collaboration greatly accelerated the timeline from laboratory prototypes to clinical systems and evaluation, resulting in a new imaging modality and, later, several spinoff applications (CAD, contrast-enhanced mammography, tomosynthesis) that provide improved earlier detection of breast cancer.
Topics: Female; Humans; Mammography; Breast Neoplasms
PubMed: 36709417
DOI: 10.1002/mp.16241 -
Physical and Engineering Sciences in... Sep 2020Mammography dose data has been collected from Western Australian units to establish Diagnostic Reference Levels for the state. Reference levels have been determined for...
Mammography dose data has been collected from Western Australian units to establish Diagnostic Reference Levels for the state. Reference levels have been determined for a variety of phantom thicknesses for both full field digital mammography units and digital breast tomosynthesis units. Levels for the American College of Radiology (ACR) Phantom have been established as 1.3 mGy and 1.5 mGy mean glandular dose for full field digital mammography and digital breast tomosynthesis respectively. 2 cm PMMA was 0.9 mGy and 1.0 mGy and 6 cm PMMA had values of 2.0 mGy and 2.3 mGy. This data can be utilised to help establish national reference levels in the future.
Topics: Aged; Diagnostic Reference Levels; Dose-Response Relationship, Radiation; Female; Humans; Mammography; Middle Aged; Phantoms, Imaging; Radiation Dosage; Western Australia
PubMed: 32757165
DOI: 10.1007/s13246-020-00914-y -
Journal of Medical Imaging and... Mar 2022In medical imaging a benefit to risk analysis is required when justifying or implementing diagnostic procedures. Screening mammography is no exception and in particular... (Review)
Review
INTRODUCTION/BACKGROUND
In medical imaging a benefit to risk analysis is required when justifying or implementing diagnostic procedures. Screening mammography is no exception and in particular concerns around the use of radiation to help diagnose cancer must be addressed.
METHODS
The Medline database and various established reports on breast screening and radiological protection were utilised to explore this review.
RESULTS/DISCUSSION
The benefit of screening is well argued; the ability to detect and treat breast cancer has led to a 91% 5-year survival rate and 497 deaths prevented from breast cancer amongst 100,000 screened women. Subsequently, screening guidelines by various countries recommend annual, biennial or triennial screening from ages somewhere between 40-74 years. Whilst the literature presents different perspectives on screening younger and older women, the current evidence of benefit for screening women <40 and ≥75 years is currently not strong. The radiation dose and associated risk delivered to each woman for a single examination is dependent upon age, breast density and breast thickness, however the average mean glandular dose is around 2.5-3 mGy, and this would result in 65 induced cancers and 8 deaths per 100,000 women over a screening lifetime from 40-74 years. This results in a ratio of lives saved to deaths from induced cancer of 62:1.
CONCLUSION
Therefore, compared to the potential mortality reduction achievable with screening mammography, the risk is small.
Topics: Adult; Aged; Breast Density; Breast Neoplasms; Early Detection of Cancer; Female; Humans; Mammography; Mass Screening; Middle Aged
PubMed: 34969620
DOI: 10.1016/j.jmir.2021.12.002 -
Acta Radiologica (Stockholm, Sweden :... Mar 2023Computerized image analysis is a viable technique for evaluating image quality as a complement to human observers. (Review)
Review
BACKGROUND
Computerized image analysis is a viable technique for evaluating image quality as a complement to human observers.
PURPOSE
To systematically review the image analysis software used in the assessment of 2D image quality using mammography phantoms.
MATERIAL AND METHODS
A systematic search of multiple databases was performed from inception to July 2020 for articles that incorporated computerized analysis of 2D images of physical mammography phantoms to determine image quality.
RESULTS
A total of 26 studies were included, 12 were carried out using direct digital imaging and 14 using screen film mammography. The ACR phantom (model-156) was the most frequently evaluated phantom, possibly due to the lack of accepted standard software. In comparison to the inter-observer variations, the computerized image analysis was more consistent in scoring test objects. The template matching method was found to be one of the most reliable algorithms, especially for high-contrast test objects, while several algorithms found low-contrast test objects to be harder to distinguish due to the smaller contrast variations between test objects and their backgrounds. This was particularly true for small object sizes.
CONCLUSION
Image analysis software was in agreement with human observers but demonstrated higher consistency and reproducibility of quality evaluation. Additionally, using computerized analysis, several quantitative metrics such as contrast-to-noise ratio (CNR) and the signal-to-noise ratio (SNR) could be used to complement the conventional scoring method. Implementing a computerized approach for monitoring image quality over time would be crucial to detect any deteriorating mammography system before clinical images are impacted.
Topics: Humans; Reproducibility of Results; Mammography; Algorithms; Software; Signal-To-Noise Ratio; Phantoms, Imaging; Radiographic Image Enhancement
PubMed: 35866198
DOI: 10.1177/02841851221112856 -
Journal of Medical Radiation Sciences Mar 2022To report for the first time the image quality of mammograms performed in Papua New Guinea (PNG) using the Perfect, Good, Moderate, Inadequate (PGMI) image evaluation... (Review)
Review
INTRODUCTION
To report for the first time the image quality of mammograms performed in Papua New Guinea (PNG) using the Perfect, Good, Moderate, Inadequate (PGMI) image evaluation system (IES); and to benchmark the image quality against BreastScreen Australia (BSA) National Accreditation Standards (NAS).
METHODS
A retrospective image quality analysis of the de-identified mammograms of 102 women imaged at the Port Moresby General Hospital (PMGH) was undertaken using the PGMI IES. Each craniocaudal (CC) and mediolateral oblique (MLO) image was assigned a grade and the reasons for the grade recorded. Age was recorded in years. Simple frequency analysis was undertaken and comparison with BSA NAS 2.4 was made.
RESULTS
Women were aged between 25 and 74 years. There were 111 CC views and 109 MLO views. The most frequent individual grade for the CC view was G (83.8%) and for the MLO view M (72.48%); and for a routine series (four images), P and G combined (14.8%). Non-visualisation of the IMA (28%), nipple not in profile (26%) and short length of pectoral muscle (12%) were the most cited reasons for assigning an M grade.
CONCLUSION
The reported image quality is not commensurate with that required by BSA (P and G > 50%) and while common positioning errors can be rectified through education and training, it is also important to recognise the complex challenges faced by PNG radiographers in obtaining mammographic images that extend beyond education and training and reflect the emerging nature of the modality as well as wider health, economic and other issues. This work raises the need for national standards, dedicated equipment, and radiographer education to best serve the women of PNG.
Topics: Adult; Aged; Australia; Breast Neoplasms; Female; Humans; Mammography; Middle Aged; Papua New Guinea; Pectoralis Muscles; Retrospective Studies
PubMed: 34418330
DOI: 10.1002/jmrs.538 -
AJR. American Journal of Roentgenology Sep 2022Artificial intelligence (AI) applications for screening mammography are being marketed for clinical use in the interpretative domains of lesion detection and diagnosis,... (Review)
Review
Artificial intelligence (AI) applications for screening mammography are being marketed for clinical use in the interpretative domains of lesion detection and diagnosis, triage, and breast density assessment and in the noninterpretive domains of breast cancer risk assessment, image quality control, image acquisition, and dose reduction. Evidence in support of these nascent applications, particularly for lesion detection and diagnosis, is largely based on multireader studies with cancer-enriched datasets rather than rigorous clinical evaluation aligned with the application's specific intended clinical use. This article reviews commercial AI algorithms for screening mammography that are currently available for clinical practice, their use, and evidence supporting their performance. Clinical implementation considerations, such as workflow integration, governance, and ethical issues, are also described. In addition, the future of AI for screening mammography is discussed, including the development of interpretive and noninterpretive AI applications and strategic priorities for research and development.
Topics: Artificial Intelligence; Breast; Breast Neoplasms; Early Detection of Cancer; Female; Humans; Mammography
PubMed: 35018795
DOI: 10.2214/AJR.21.27071 -
IEEE Journal of Biomedical and Health... Jul 2022Self-supervised learning (SSL) can alleviate the issue of small sample size, which has shown its effectiveness for the computer-aided diagnosis (CAD) models. However,...
Self-supervised learning (SSL) can alleviate the issue of small sample size, which has shown its effectiveness for the computer-aided diagnosis (CAD) models. However, since the conventional SSL methods share the identical backbone in both the pretext and downstream tasks, the pretext network generally cannot be well trained in the pre-training stage, if the pretext task is totally different from the downstream one. In this work, we propose a novel task-driven SSL method, namely Self-Supervised Bi-channel Transformer Networks (SSBTN), to improve the diagnostic accuracy of a CAD model by enhancing SSL flexibility. In SSBTN, we innovatively integrate two different networks for the pretext and downstream tasks, respectively, into a unified framework. Consequently, the pretext task can be flexibly designed based on the data characteristics, and the corresponding designed pretext network thus learns more effective feature representation to be transferred to the downstream network. Furthermore, a transformer-based transfer module is developed to efficiently enhance knowledge transfer by conducting feature alignment between two different networks. The proposed SSBTN is evaluated on two publicly available datasets, namely the full-field digital mammography INbreast dataset and the wireless video capsule CrohnIPI dataset. The experimental results indicate that the proposed SSBTN outperforms all the compared algorithms.
Topics: Algorithms; Computers; Diagnosis, Computer-Assisted; Humans; Mammography; Neural Networks, Computer
PubMed: 35201993
DOI: 10.1109/JBHI.2022.3153902 -
BMC Public Health Aug 2023In Germany, all women aged 50-69 have been invited to biennial mammography screening since 2009. We aimed to assess longitudinal adherence over ten years in women aged...
BACKGROUND
In Germany, all women aged 50-69 have been invited to biennial mammography screening since 2009. We aimed to assess longitudinal adherence over ten years in women aged 50 in 2009 and characterize the different adherence groups.
METHODS
Using the German Pharmacoepidemiological Research Database (GePaRD, ~ 20% of the German population), we included women aged 50 in 2009 (baseline) with continuous health insurance coverage and without breast cancer or in-situ-carcinoma. We followed them until age 59 and categorized them according to mammography screening participation into the following groups: never, 1-2, 3-4, 5-6 times. We characterized these groups, inter alia, regarding the use of other preventive measures, non-screening mammography (i.e., mammography outside the organized screening program) and menopausal hormone therapy.
RESULTS
Overall, 82,666 women were included. Of these, 27.6% never participated in the screening program, 15.1% participated 1-2 times, 31.7% participated 3-4 times and 25.6% participated regularly (5-6 times). Among regular participants, 91% utilized other preventive measures (e.g., cervical cancer screening, general health checkup) before baseline as compared to 66% among non-participants. Menopausal hormone therapy was least common among non-participants (11% vs. 18% among regular participants). Among non-participants, the proportions using ≥ 1, ≥ 2, and ≥ 3 non-screening mammographies between age 50-59 were 25%, 18%, and 15%, respectively.
CONCLUSIONS
Using a large cohort based on claims data, this study provides novel insights into longitudinal adherence to the mammography screening program and the use of mammography outside of the program in Germany. Between age 50-59, 57% of eligible women participated at least three times in the German mammography screening program and 28% (~ 3 in 10 women) never participated. Among non-participants, 15% had at least three non-screening mammographies during this period, indicating potential gray screening. Participants more often utilized other preventive measures as compared to non-participants.
Topics: Humans; Female; Early Detection of Cancer; Uterine Cervical Neoplasms; Mammography; Breast Neoplasms; Databases, Factual
PubMed: 37653487
DOI: 10.1186/s12889-023-16589-5