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Breast Cancer Research : BCR May 2015Mammography screening for breast cancer is widely available in many countries. Initially praised as a universal achievement to improve women's health and to reduce the... (Review)
Review
Mammography screening for breast cancer is widely available in many countries. Initially praised as a universal achievement to improve women's health and to reduce the burden of breast cancer, the benefits and harms of mammography screening have been debated heatedly in the past years. This review discusses the benefits and harms of mammography screening in light of findings from randomized trials and from more recent observational studies performed in the era of modern diagnostics and treatment. The main benefit of mammography screening is reduction of breast-cancer related death. Relative reductions vary from about 15 to 25% in randomized trials to more recent estimates of 13 to 17% in meta-analyses of observational studies. Using UK population data of 2007, for 1,000 women invited to biennial mammography screening for 20 years from age 50, 2 to 3 women are prevented from dying of breast cancer. All-cause mortality is unchanged. Overdiagnosis of breast cancer is the main harm of mammography screening. Based on recent estimates from the United States, the relative amount of overdiagnosis (including ductal carcinoma in situ and invasive cancer) is 31%. This results in 15 women overdiagnosed for every 1,000 women invited to biennial mammography screening for 20 years from age 50. Women should be unpassionately informed about the benefits and harms of mammography screening using absolute effect sizes in a comprehensible fashion. In an era of limited health care resources, screening services need to be scrutinized and compared with each other with regard to effectiveness, cost-effectiveness and harms.
Topics: Breast Neoplasms; Early Detection of Cancer; Female; Humans; Mammography; Mass Screening; Mortality; Sensitivity and Specificity
PubMed: 25928287
DOI: 10.1186/s13058-015-0525-z -
European Journal of Radiology Nov 2022Contrast-enhanced mammography (CEM) is a promising vascular-based breast imaging technique with high diagnostic performance in detecting breast cancer. Dual-energy... (Review)
Review
Contrast-enhanced mammography (CEM) is a promising vascular-based breast imaging technique with high diagnostic performance in detecting breast cancer. Dual-energy acquisition using low and high energy x-ray spectra following intravenous iodinated contrast injection provides both anatomic and functional information in the same examination. The low-energy images are equivalent to standard digital mammography and the post-processed recombined images depict enhancement analogous to contrast-enhanced breast magnetic resonance imaging (MRI). Thus, CEM has the potential to detect abnormal morphologic features as well as neovascularity associated with breast cancer. Since its emergence in 2011, CEM has consistently demonstrated superior performance compared with standard mammography and mammography plus ultrasound, particularly in women with dense breasts, with high sensitivity approaching that of MRI, supporting its use as a cost-effective diagnostic and screening tool. CEM has been primarily used in the diagnostic setting to evaluate patients with screening abnormalities or with symptomatic breasts, to perform preoperative staging of newly diagnosed breast cancer, and to evaluate response to neoadjuvant chemotherapy. More recently, CEM has been performed to screen women who have an intermediate to high lifetime risk of developing breast cancer. In addition to its high diagnostic performance, CEM is less expensive and more accessible than MRI and potentially better tolerated by patients. Minor drawbacks to CEM include a slightly increased radiation dose compared with standard mammography and a low risk for contrast allergy reaction. The aim of this study is to review the background, current literature, and future applications of CEM in breast cancer screening.
Topics: Female; Humans; Breast Neoplasms; Early Detection of Cancer; Mammography; Breast; Contrast Media; Magnetic Resonance Imaging
PubMed: 36108478
DOI: 10.1016/j.ejrad.2022.110513 -
Seminars in Cancer Biology Jul 2021Screening for breast cancer with mammography has been introduced in various countries over the last 30 years, initially using analog screen-film-based systems and, over... (Review)
Review
Screening for breast cancer with mammography has been introduced in various countries over the last 30 years, initially using analog screen-film-based systems and, over the last 20 years, transitioning to the use of fully digital systems. With the introduction of digitization, the computer interpretation of images has been a subject of intense interest, resulting in the introduction of computer-aided detection (CADe) and diagnosis (CADx) algorithms in the early 2000's. Although they were introduced with high expectations, the potential improvement in the clinical realm failed to materialize, mostly due to the high number of false positive marks per analyzed image. In the last five years, the artificial intelligence (AI) revolution in computing, driven mostly by deep learning and convolutional neural networks, has also pervaded the field of automated breast cancer detection in digital mammography and digital breast tomosynthesis. Research in this area first involved comparison of its capabilities to that of conventional CADe/CADx methods, which quickly demonstrated the potential of this new technology. In the last couple of years, more mature and some commercial products have been developed, and studies of their performance compared to that of experienced breast radiologists are showing that these algorithms are on par with human-performance levels in retrospective data sets. Although additional studies, especially prospective evaluations performed in the real screening environment, are needed, it is becoming clear that AI will have an important role in the future breast cancer screening realm. Exactly how this new player will shape this field remains to be determined, but recent studies are already evaluating different options for implementation of this technology. The aim of this review is to provide an overview of the basic concepts and developments in the field AI for breast cancer detection in digital mammography and digital breast tomosynthesis. The pitfalls of conventional methods, and how these are, for the most part, avoided by this new technology, will be discussed. Importantly, studies that have evaluated the current capabilities of AI and proposals for how these capabilities should be leveraged in the clinical realm will be reviewed, while the questions that need to be answered before this vision becomes a reality are posed.
Topics: Animals; Artificial Intelligence; Breast Neoplasms; Early Detection of Cancer; Female; Humans; Mammography
PubMed: 32531273
DOI: 10.1016/j.semcancer.2020.06.002 -
Radiology Jul 2019Digital breast tomosynthesis (DBT) is emerging as the standard of care for breast imaging based on improvements in both screening and diagnostic imaging outcomes. The... (Review)
Review
Digital breast tomosynthesis (DBT) is emerging as the standard of care for breast imaging based on improvements in both screening and diagnostic imaging outcomes. The additional information obtained from the tomosynthesis acquisition decreases the confounding effect of overlapping tissue, allowing for improved lesion detection, characterization, and localization. In addition, the quasi three-dimensional information obtained from the reconstructed DBT data set allows a more efficient imaging work-up than imaging with two-dimensional full-field digital mammography alone. Herein, the authors review the benefits of DBT imaging in screening and diagnostic breast imaging.
Topics: Breast; Breast Neoplasms; Female; Humans; Mammography
PubMed: 31084476
DOI: 10.1148/radiol.2019180760 -
Radiology Mar 2023Breast density is an independent risk factor for breast cancer. In digital mammography and digital breast tomosynthesis, breast density is assessed visually using the... (Review)
Review
Breast density is an independent risk factor for breast cancer. In digital mammography and digital breast tomosynthesis, breast density is assessed visually using the four-category scale developed by the American College of Radiology Breast Imaging Reporting and Data System (5th edition as of November 2022). Epidemiologically based risk models, such as the Tyrer-Cuzick model (version 8), demonstrate superior modeling performance when mammographic density is incorporated. Beyond just density, a separate mammographic measure of breast cancer risk is parenchymal textural complexity. With advancements in radiomics and deep learning, mammographic textural patterns can be assessed quantitatively and incorporated into risk models. Other supplemental screening modalities, such as breast US and MRI, offer independent risk measures complementary to those derived from mammography. Breast US allows the two components of fibroglandular tissue (stromal and glandular) to be visualized separately in a manner that is not possible with mammography. A higher glandular component at screening breast US is associated with higher risk. With MRI, a higher background parenchymal enhancement of the fibroglandular tissue has also emerged as an imaging marker for risk assessment. Imaging markers observed at mammography, US, and MRI are powerful tools in refining breast cancer risk prediction, beyond mammographic density alone.
Topics: Humans; Female; Breast Neoplasms; Breast Density; Breast; Mammography; Risk Factors
PubMed: 36749212
DOI: 10.1148/radiol.222575 -
Medicine Jan 2024Breast cancer remains a complex and prevalent health concern affecting millions of individuals worldwide. This review paper presents a comprehensive analysis of the... (Review)
Review
Breast cancer remains a complex and prevalent health concern affecting millions of individuals worldwide. This review paper presents a comprehensive analysis of the multifaceted landscape of breast cancer, elucidating the diverse spectrum of risk factors contributing to its occurrence and exploring advancements in diagnostic methodologies. Through an extensive examination of current literature, various risk factors have been identified, encompassing genetic predispositions such as BRCA mutations, hormonal influences, lifestyle factors, and reproductive patterns. Age, family history, and environmental factors further contribute to the intricate tapestry of breast cancer etiology. Moreover, this review delineates the pivotal role of diagnostic tools in the early detection and management of breast cancer. Mammography, the cornerstone of breast cancer screening, is augmented by emerging technologies like magnetic resonance imaging and molecular testing, enabling improved sensitivity and specificity in diagnosing breast malignancies. Despite these advancements, challenges persist in ensuring widespread accessibility to screening programs, particularly in resource-limited settings. In conclusion, this review underscores the importance of understanding diverse risk factors in the development of breast cancer and emphasizes the critical role of evolving diagnostic modalities in enhancing early detection. The synthesis of current knowledge in this review aims to contribute to a deeper comprehension of breast cancer's multifactorial nature and inform future directions in research, screening strategies, and preventive interventions.
Topics: Humans; Female; Breast Neoplasms; Early Detection of Cancer; Mammography; Breast; Risk Factors
PubMed: 38241592
DOI: 10.1097/MD.0000000000036905 -
The Lancet. Digital Health Oct 2023Artificial intelligence (AI) as an independent reader of screening mammograms has shown promise, but there are few prospective studies. Our aim was to conduct a...
BACKGROUND
Artificial intelligence (AI) as an independent reader of screening mammograms has shown promise, but there are few prospective studies. Our aim was to conduct a prospective clinical trial to examine how AI affects cancer detection and false positive findings in a real-world setting.
METHODS
ScreenTrustCAD was a prospective, population-based, paired-reader, non-inferiority study done at the Capio Sankt Göran Hospital in Stockholm, Sweden. Consecutive women without breast implants aged 40-74 years participating in population-based screening in the geographical uptake area of the study hospital were included. The primary outcome was screen-detected breast cancer within 3 months of mammography, and the primary analysis was to assess non-inferiority (non-inferiority margin of 0·15 relative reduction in breast cancer diagnoses) of double reading by one radiologist plus AI compared with standard-of-care double reading by two radiologists. We also assessed single reading by AI alone and triple reading by two radiologists plus AI compared with standard-of-care double reading by two radiologists. This study is registered with ClinicalTrials.gov, NCT04778670.
FINDINGS
From April 1, 2021, to June 9, 2022, 58 344 women aged 40-74 years underwent regular mammography screening, of whom 55 581 were included in the study. 269 (0·5%) women were diagnosed with screen-detected breast cancer based on an initial positive read: double reading by one radiologist plus AI was non-inferior for cancer detection compared with double reading by two radiologists (261 [0·5%] vs 250 [0·4%] detected cases; relative proportion 1·04 [95% CI 1·00-1·09]). Single reading by AI (246 [0·4%] vs 250 [0·4%] detected cases; relative proportion 0·98 [0·93-1·04]) and triple reading by two radiologists plus AI (269 [0·5%] vs 250 [0·4%] detected cases; relative proportion 1·08 [1·04-1·11]) were also non-inferior to double reading by two radiologists.
INTERPRETATION
Replacing one radiologist with AI for independent reading of screening mammograms resulted in a 4% higher non-inferior cancer detection rate compared with radiologist double reading. Our study suggests that AI in the study setting has potential for controlled implementation, which would include risk management and real-world follow-up of performance.
FUNDING
Swedish Research Council, Swedish Cancer Society, Region Stockholm, and Lunit.
Topics: Female; Humans; Male; Artificial Intelligence; Breast Neoplasms; Early Detection of Cancer; Mammography; Prospective Studies; Sweden
PubMed: 37690911
DOI: 10.1016/S2589-7500(23)00153-X -
BMJ (Clinical Research Ed.) Sep 2021To examine the accuracy of artificial intelligence (AI) for the detection of breast cancer in mammography screening practice.
OBJECTIVE
To examine the accuracy of artificial intelligence (AI) for the detection of breast cancer in mammography screening practice.
DESIGN
Systematic review of test accuracy studies.
DATA SOURCES
Medline, Embase, Web of Science, and Cochrane Database of Systematic Reviews from 1 January 2010 to 17 May 2021.
ELIGIBILITY CRITERIA
Studies reporting test accuracy of AI algorithms, alone or in combination with radiologists, to detect cancer in women's digital mammograms in screening practice, or in test sets. Reference standard was biopsy with histology or follow-up (for screen negative women). Outcomes included test accuracy and cancer type detected.
STUDY SELECTION AND SYNTHESIS
Two reviewers independently assessed articles for inclusion and assessed the methodological quality of included studies using the QUality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool. A single reviewer extracted data, which were checked by a second reviewer. Narrative data synthesis was performed.
RESULTS
Twelve studies totalling 131 822 screened women were included. No prospective studies measuring test accuracy of AI in screening practice were found. Studies were of poor methodological quality. Three retrospective studies compared AI systems with the clinical decisions of the original radiologist, including 79 910 women, of whom 1878 had screen detected cancer or interval cancer within 12 months of screening. Thirty four (94%) of 36 AI systems evaluated in these studies were less accurate than a single radiologist, and all were less accurate than consensus of two or more radiologists. Five smaller studies (1086 women, 520 cancers) at high risk of bias and low generalisability to the clinical context reported that all five evaluated AI systems (as standalone to replace radiologist or as a reader aid) were more accurate than a single radiologist reading a test set in the laboratory. In three studies, AI used for triage screened out 53%, 45%, and 50% of women at low risk but also 10%, 4%, and 0% of cancers detected by radiologists.
CONCLUSIONS
Current evidence for AI does not yet allow judgement of its accuracy in breast cancer screening programmes, and it is unclear where on the clinical pathway AI might be of most benefit. AI systems are not sufficiently specific to replace radiologist double reading in screening programmes. Promising results in smaller studies are not replicated in larger studies. Prospective studies are required to measure the effect of AI in clinical practice. Such studies will require clear stopping rules to ensure that AI does not reduce programme specificity.
STUDY REGISTRATION
Protocol registered as PROSPERO CRD42020213590.
Topics: Artificial Intelligence; Breast Neoplasms; Early Detection of Cancer; Female; Humans; Mammography; Mass Screening
PubMed: 34470740
DOI: 10.1136/bmj.n1872 -
Seminars in Ultrasound, CT, and MR Feb 2023This topical review is focused on the clinical breast x-ray imaging applications of the rapidly evolving field of artificial intelligence (AI). The range of AI... (Review)
Review
This topical review is focused on the clinical breast x-ray imaging applications of the rapidly evolving field of artificial intelligence (AI). The range of AI applications is broad. AI can be used for breast cancer risk estimation that could allow for tailoring the screening interval and the protocol that are woman-specific and for triaging the screening exams. It also can serve as a tool to aid in the detection and diagnosis for improved sensitivity and specificity and as a tool to reduce radiologists' reading time. AI can also serve as a potential second 'reader' during screening interpretation. During the last decade, numerous studies have shown the potential of AI-assisted interpretation of mammography and to a lesser extent digital breast tomosynthesis; however, most of these studies are retrospective in nature. There is a need for prospective clinical studies to evaluate these technologies to better understand their real-world efficacy. Further, there are ethical, medicolegal, and liability concerns that need to be considered prior to the routine use of AI in the breast imaging clinic.
Topics: Female; Humans; Artificial Intelligence; Retrospective Studies; X-Rays; Early Detection of Cancer; Mammography; Breast Neoplasms
PubMed: 36792270
DOI: 10.1053/j.sult.2022.12.002 -
Journal of the National Cancer Institute Sep 2019Artificial intelligence (AI) systems performing at radiologist-like levels in the evaluation of digital mammography (DM) would improve breast cancer screening accuracy...
BACKGROUND
Artificial intelligence (AI) systems performing at radiologist-like levels in the evaluation of digital mammography (DM) would improve breast cancer screening accuracy and efficiency. We aimed to compare the stand-alone performance of an AI system to that of radiologists in detecting breast cancer in DM.
METHODS
Nine multi-reader, multi-case study datasets previously used for different research purposes in seven countries were collected. Each dataset consisted of DM exams acquired with systems from four different vendors, multiple radiologists' assessments per exam, and ground truth verified by histopathological analysis or follow-up, yielding a total of 2652 exams (653 malignant) and interpretations by 101 radiologists (28 296 independent interpretations). An AI system analyzed these exams yielding a level of suspicion of cancer present between 1 and 10. The detection performance between the radiologists and the AI system was compared using a noninferiority null hypothesis at a margin of 0.05.
RESULTS
The performance of the AI system was statistically noninferior to that of the average of the 101 radiologists. The AI system had a 0.840 (95% confidence interval [CI] = 0.820 to 0.860) area under the ROC curve and the average of the radiologists was 0.814 (95% CI = 0.787 to 0.841) (difference 95% CI = -0.003 to 0.055). The AI system had an AUC higher than 61.4% of the radiologists.
CONCLUSIONS
The evaluated AI system achieved a cancer detection accuracy comparable to an average breast radiologist in this retrospective setting. Although promising, the performance and impact of such a system in a screening setting needs further investigation.
Topics: Algorithms; Area Under Curve; Artificial Intelligence; Breast Neoplasms; Early Detection of Cancer; Female; Humans; Image Processing, Computer-Assisted; Mammography; ROC Curve; Radiologists; Reproducibility of Results
PubMed: 30834436
DOI: 10.1093/jnci/djy222