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Radiology Apr 2021Contrast-enhanced mammography (CEM) has emerged as a viable alternative to contrast-enhanced breast MRI, and it may increase access to vascular imaging while reducing... (Review)
Review
Contrast-enhanced mammography (CEM) has emerged as a viable alternative to contrast-enhanced breast MRI, and it may increase access to vascular imaging while reducing examination cost. Intravenous iodinated contrast materials are used in CEM to enhance the visualization of tumor neovascularity. After injection, imaging is performed with dual-energy digital mammography, which helps provide a low-energy image and a recombined or iodine image that depict enhancing lesions in the breast. CEM has been demonstrated to help improve accuracy compared with digital mammography and US in women with abnormal screening mammographic findings or symptoms of breast cancer. It has also been demonstrated to approach the accuracy of breast MRI in preoperative staging of patients with breast cancer and in monitoring response after neoadjuvant chemotherapy. There are early encouraging results from trials evaluating CEM in the screening of women who are at an increased risk of breast cancer. Although CEM is a promising tool, it slightly increases radiation dose and carries a small risk of adverse reactions to contrast materials. This review details the CEM technique, diagnostic and screening uses, and future applications, including artificial intelligence and radiomics.
Topics: Artificial Intelligence; Breast Neoplasms; Contrast Media; Early Detection of Cancer; Female; Forecasting; Humans; Magnetic Resonance Imaging; Mammography; Radiation Dosage
PubMed: 33650905
DOI: 10.1148/radiol.2021201948 -
Breast (Edinburgh, Scotland) Dec 2022Mammographic density is a well-defined risk factor for breast cancer and having extremely dense breast tissue is associated with a one-to six-fold increased risk of... (Meta-Analysis)
Meta-Analysis Review
OBJECTIVES
Mammographic density is a well-defined risk factor for breast cancer and having extremely dense breast tissue is associated with a one-to six-fold increased risk of breast cancer. However, it is questioned whether this increased risk estimate is applicable to current breast density classification methods. Therefore, the aim of this study was to further investigate and clarify the association between mammographic density and breast cancer risk based on current literature.
METHODS
Medline, Embase and Web of Science were systematically searched for articles published since 2013, that used BI-RADS lexicon 5th edition and incorporated data on digital mammography. Crude and maximally confounder-adjusted data were pooled in odds ratios (ORs) using random-effects models. Heterogeneity regarding breast cancer risks were investigated using I statistic, stratified and sensitivity analyses.
RESULTS
Nine observational studies were included. Having extremely dense breast tissue (BI-RADS density D) resulted in a 2.11-fold (95% CI 1.84-2.42) increased breast cancer risk compared to having scattered dense breast tissue (BI-RADS density B). Sensitivity analysis showed that when only using data that had adjusted for age and BMI, the breast cancer risk was 1.83-fold (95% CI 1.52-2.21) increased. Both results were statistically significant and homogenous.
CONCLUSIONS
Mammographic breast density BI-RADS D is associated with an approximately two-fold increased risk of breast cancer compared to having BI-RADS density B in general population women. This is a novel and lower risk estimate compared to previously reported and might be explained due to the use of digital mammography and BI-RADS lexicon 5th edition.
Topics: Female; Humans; Breast Density; Breast Neoplasms; Mammography; Breast; Risk Factors
PubMed: 36183671
DOI: 10.1016/j.breast.2022.09.007 -
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 -
Radiology Nov 2019Although computer-aided diagnosis (CAD) is widely used in mammography, conventional CAD programs that use prompts to indicate potential cancers on the mammograms have... (Review)
Review
Although computer-aided diagnosis (CAD) is widely used in mammography, conventional CAD programs that use prompts to indicate potential cancers on the mammograms have not led to an improvement in diagnostic accuracy. Because of the advances in machine learning, especially with use of deep (multilayered) convolutional neural networks, artificial intelligence has undergone a transformation that has improved the quality of the predictions of the models. Recently, such deep learning algorithms have been applied to mammography and digital breast tomosynthesis (DBT). In this review, the authors explain how deep learning works in the context of mammography and DBT and define the important technical challenges. Subsequently, they discuss the current status and future perspectives of artificial intelligence-based clinical applications for mammography, DBT, and radiomics. Available algorithms are advanced and approach the performance of radiologists-especially for cancer detection and risk prediction at mammography. However, clinical validation is largely lacking, and it is not clear how the power of deep learning should be used to optimize practice. Further development of deep learning models is necessary for DBT, and this requires collection of larger databases. It is expected that deep learning will eventually have an important role in DBT, including the generation of synthetic images.
Topics: Algorithms; Artificial Intelligence; Breast Neoplasms; Deep Learning; Diagnosis, Computer-Assisted; Female; Humans; Machine Learning; Mammography; Radiographic Image Enhancement; Risk Assessment
PubMed: 31549948
DOI: 10.1148/radiol.2019182627 -
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 -
Seminars in Nuclear Medicine Sep 2022This review gives an overview of the current state of deep learning research in breast cancer imaging. Breast imaging plays a major role in detecting breast cancer at an... (Review)
Review
This review gives an overview of the current state of deep learning research in breast cancer imaging. Breast imaging plays a major role in detecting breast cancer at an earlier stage, as well as monitoring and evaluating breast cancer during treatment. The most commonly used modalities for breast imaging are digital mammography, digital breast tomosynthesis, ultrasound and magnetic resonance imaging. Nuclear medicine imaging techniques are used for detection and classification of axillary lymph nodes and distant staging in breast cancer imaging. All of these techniques are currently digitized, enabling the possibility to implement deep learning (DL), a subset of Artificial intelligence, in breast imaging. DL is nowadays embedded in a plethora of different tasks, such as lesion classification and segmentation, image reconstruction and generation, cancer risk prediction, and prediction and assessment of therapy response. Studies show similar and even better performances of DL algorithms compared to radiologists, although it is clear that large trials are needed, especially for ultrasound and magnetic resonance imaging, to exactly determine the added value of DL in breast cancer imaging. Studies on DL in nuclear medicine techniques are only sparsely available and further research is mandatory. Legal and ethical issues need to be considered before the role of DL can expand to its full potential in clinical breast care practice.
Topics: Artificial Intelligence; Breast; Breast Neoplasms; Deep Learning; Female; Humans; Mammography
PubMed: 35339259
DOI: 10.1053/j.semnuclmed.2022.02.003 -
Annals of Internal Medicine Jan 2020The European Commission Initiative for Breast Cancer Screening and Diagnosis guidelines (European Breast Guidelines) are coordinated by the European Commission's Joint...
DESCRIPTION
The European Commission Initiative for Breast Cancer Screening and Diagnosis guidelines (European Breast Guidelines) are coordinated by the European Commission's Joint Research Centre. The target audience for the guidelines includes women, health professionals, and policymakers.
METHODS
An international guideline panel of 28 multidisciplinary members, including patients, developed questions and corresponding recommendations that were informed by systematic reviews of the evidence conducted between March 2016 and December 2018. GRADE (Grading of Recommendations Assessment, Development and Evaluation) Evidence to Decision frameworks were used to structure the process and minimize the influence of competing interests by enhancing transparency. Questions and recommendations, expressed as strong or conditional, focused on outcomes that matter to women and provided a rating of the certainty of evidence.
RECOMMENDATIONS
This synopsis of the European Breast Guidelines provides recommendations regarding organized screening programs for women aged 40 to 75 years who are at average risk. The recommendations address digital mammography screening and the addition of hand-held ultrasonography, automated breast ultrasonography, or magnetic resonance imaging compared with mammography alone. The recommendations also discuss the frequency of screening and inform decision making for women at average risk who are recalled for suspicious lesions or who have high breast density.
Topics: Adult; Age Factors; Aged; Breast Neoplasms; Early Detection of Cancer; Europe; Female; Humans; Mammography; Middle Aged; Ultrasonography, Mammary
PubMed: 31766052
DOI: 10.7326/M19-2125 -
Thoracic Cancer Nov 2022Ultrasound is more widely used than mammography for early diagnosis of breast cancer in China as most Chinese women have small and dense mammary glands. This study... (Clinical Trial)
Clinical Trial
BACKGROUND
Ultrasound is more widely used than mammography for early diagnosis of breast cancer in China as most Chinese women have small and dense mammary glands. This study compared the diagnostic performance of ultrasound and mammography for breast cancer among Chinese women with suspected breast lesions.
METHODS
From November 2019 to November 2021, we compared the results of ultrasound and mammography for breast lesion diagnosis in 2737 consecutive participants with suspected breast lesions; all patients underwent biopsies. We measured the sensitivity, specificity, and diagnostic accuracy separately.
RESULTS
Among the 2737 participants, 2844 breast lesions were detected, including 1935 (68.0%) breast cancers and 909 (32.0%) benign lesions. Of the breast cancers, ultrasound detected 1851 (95.7%), whereas mammography detected 1527 (78.9%). The sensitivity of ultrasound for breast cancer diagnosis was significantly higher than that of mammography (95.7% vs. 78.9%, p < 0.001), whereas the specificity was significantly lower than that of mammography (42.9% vs. 62.3%, p < 0.001). The receiver operating characteristic curves revealed that ultrasound was more accurate in detecting breast cancer than mammography (76.8% vs. 71.3%, p < 0.001). Age, body mass index, and breast density did not influence ultrasound sensitivity and accuracy.
CONCLUSIONS
Ultrasound is more sensitive and accurate than mammography and detects more breast cancers with a lower specificity.
Topics: Female; Humans; Breast Neoplasms; Early Detection of Cancer; Mammography; Prospective Studies; Sensitivity and Specificity; Ultrasonography, Mammary
PubMed: 36177910
DOI: 10.1111/1759-7714.14666