Comparison of Breast Density Between Synthesized Versus Standard Digital Mammography

Synthetic Mammography: Benefits, Drawbacks, and Pitfalls

Digital breast tomosynthesis (DBT) allows three-dimensional assessment of breast tissue; however, DBT requires a two-dimensional (2D) image for comparison with prior mammograms and accurate interpretation of calcifications. Traditionally, full-field digital mammography (FFDM) has been performed after the DBT image acquisition. Synthetic mammography (SM), the 2D reconstruction of the tomosynthesis slice dataset, has been designed to replace FFDM. Advantages of SM include decreased image acquisition time and decreased radiation exposure, with maintained or improved screening performance metrics. Because SM algorithms give extra weight to lesion-like characteristics (eg, calcifications and architectural distortions), they may enable increased visibility of these characteristics relative to that at FFDM. Although SM algorithms were designed to improve lesion identification, they have led to varied outcomes in studies reported in the literature. Compared with FFDM, SM has been reported to be associated with a higher false-positive rate for calcifications, decreased conspicuity of asymmetries, lower breast density assessments, and imaging artifacts (eg, metallic artifact, bright-band artifact, blurring of the axilla, and truncation artifact). The authors review the literature on SM, including its implementation, benefits, and artifacts. ©RSNA, 2023 Quiz questions for this article are available through the Online Learning Center.

International Interobserver Variability of Breast Density Assessment

PURPOSE

The aim of this study was to determine variability in visually assessed mammographic breast density categorization among radiologists practicing in Indonesia, the Netherlands, South Africa, and the United States.

METHODS

Two hundred consecutive 2-D full-field digital screening mammograms obtained from September to December 2017 were selected and retrospectively reviewed from four global locations, for a total of 800 mammograms. Three breast radiologists in each location (team) provided consensus density assessments of all 800 mammograms using BI-RADS® density categorization. Interreader agreement was compared using Gwet's AC2 with quadratic weighting across all four density categories and Gwet's AC1 for binary comparison of combined not dense versus dense categories. Variability of distribution among teams was calculated using the Stuart-Maxwell test of marginal homogeneity across all four categories and using the McNemar test for not dense versus dense categories. To compare readers from a particular country on their own 200 mammograms versus the other three teams, density distribution was calculated using conditional logistic regression.

RESULTS

For all 800 mammograms, interreader weighted agreement for distribution among four density categories was 0.86 (Gwet's AC2 with quadratic weighting; 95% confidence interval, 0.85-0.88), and for not dense versus dense categories, it was 0.66 (Gwet's AC1; 95% confidence interval, 0.63-0.70). Density distribution across four density categories was significantly different when teams were compared with one another and one team versus the other three teams combined (P < .001). Overall, all readers placed the largest number of mammograms in the scattered and heterogeneous categories.

CONCLUSIONS

Although reader teams from four different global locations had almost perfect interreader agreement in BI-RADS density categorization, variability in density distribution across four categories remained statistically significant.

Beyond Breast Density: Risk Measures for Breast Cancer in Multiple Imaging Modalities

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.

Mammographic Breast Density: Current Assessment Methods, Clinical Implications, and Future Directions

Mammographic breast density is widely accepted as an independent risk factor for the development of breast cancer. In addition, because dense breast tissue may mask breast malignancies, breast density is inversely related to the sensitivity of screening mammography. Given the risks associated with breast density, as well as ongoing efforts to stratify individual risk and personalize breast cancer screening and prevention, numerous studies have sought to better understand the factors that impact breast density, and to develop and implement reproducible, quantitative methods to assess mammographic density. Breast density assessments have been incorporated into risk assessment models to improve risk stratification. Recently, novel techniques for analyzing mammographic parenchymal complexity, or texture, have been explored as potential means of refining mammographic tissue-based risk assessment beyond breast density.

Breast Density: Current Knowledge, Assessment Methods, and Clinical Implications

Breast density is an accepted independent risk factor for the future development of breast cancer, and greater breast density has the potential to mask malignancies on mammography, thus lowering the sensitivity of screening mammography. The risk associated with dense breast tissue has been shown to be modifiable with changes in breast density. Numerous studies have sought to identify factors that influence breast density, including age, genetic, racial/ethnic, prepubertal, adolescent, lifestyle, environmental, hormonal, and reproductive history factors. Qualitative, semiquantitative, and quantitative methods of breast density assessment have been developed, but to date there is no consensus assessment method or reference standard for breast density. Breast density has been incorporated into breast cancer risk models, and there is growing consciousness of the clinical implications of dense breast tissue in both the medical community and public arena. Efforts to improve breast cancer screening sensitivity for women with dense breasts have led to increased attention to supplemental screening methods in recent years, prompting the American College of Radiology to publish Appropriateness Criteria for supplemental screening based on breast density.

Synthetic Mammography: Review of Benefits and Drawbacks in Clinical Use

Digital breast tomosynthesis (DBT) has been widely adopted as a breast cancer screening tool, demonstrating decreased recall rates and other improved screening performance metrics when compared to digital mammography (DM) alone. Drawbacks of DBT when added to 2D DM include the increased radiation dose and longer examination time. Synthetic mammography (SM), a 2D reconstruction from the tomosynthesis slices, has been introduced to eliminate the need for a separate acquisition of 2D DM. Data show that the replacement of 2D DM by SM, when used with DBT, maintains the benefits of DBT, such as decreased recall rates, improved cancer detection rates, and similar positive predictive values. Key differences between SM and 2D DM include how the image is acquired, assessment of breast density, and visualization of mammographic findings, such as calcifications. Although SM is approved by the Food and Drug Administration and has been shown to be non-inferior when used with DBT, concerns surrounding SM include image quality and artifacts. The purpose of this review article is to review the benefits, drawbacks, and screening performance metrics of SM versus DBT.

Persistent inter-observer variability of breast density assessment using BI-RADS® 5th edition guidelines

OBJECTIVES

Due to most states' legislation, mammographic density categorization has potentially far-reaching implications, but remains subjective based on BIRADS® guidelines. We aimed to determine 1) effect of BI-RADS® 5th edition (5th-ed) vs 4th-edition (4th-ed) guidelines on reader agreement regarding density assessment; 2) 5th-ed vs 4th-ed density distribution, and visual vs quantitative assessment agreement; 3) agreement between experienced vs less experienced readers.

METHODS

In a retrospective review, six breast imaging radiologists (BIR) (23-30 years' experience) visually assessed density of 200 screening mammograms performed September 2012-January 2013 using 5th-ed guidelines. Results were compared to 2016 data of the same readers evaluating the same mammograms using 4th-ed guidelines after a training module. 5th-ed density categorization by seven junior BIR (1-5 years' experience) was compared to eight experienced BIR. Nelson et al.'s kappas (κ_m, κ_w), Fleiss' κ_F, and Cohen's κ were calculated. Quantitative density using Volpara was compared with reader assessments.

RESULTS

Inter-reader weighted agreement using 5th-ed is moderately strong, 0.73 (κ_w, s.e. = 0.01), similar to 4th-ed, 0.71 (κ_w, s.e. = 0.03). Intra-reader Cohen's κ is 0.23-0.34, similar to 4th-ed. Binary not-dense vs dense categorization, using 5th-ed results in higher dense categorization vs 4th-ed (p < 0.001). 5th-ed density distribution results in higher numbers in categories B/C vs 4th-ed (p < 0.001). Distribution for 5th-ed does not differ based on reader experience (p = 0.09). Reader vs quantitative weighted agreement is similar (5th-ed, Cohen's κ = 0.76-0.85; 4th-ed, Cohen's κ = 0.68-0.83).

CONCLUSION

There is persistent subjectivity of visually assessed mammographic density using 5th-ed guidelines; experience does not correlate with better inter-reader agreement.

Trends in Clinical Breast Density Assessment From the Breast Cancer Surveillance Consortium

Changes to mammography practice, including revised Breast Imaging Reporting and Data System (BI-RADS) density classification guidelines and implementation of digital breast tomosynthesis (DBT), may impact clinical breast density assessment. We investigated temporal trends in clinical breast density assessment among 2 990 291 digital mammography (DM) screens and 221 063 DBT screens interpreted by 722 radiologists from 144 facilities in the Breast Cancer Surveillance Consortium. After age-standardization, 46.3% (95% CI = 44.1% to 48.6%) of DM screens were assessed as dense (heterogeneously/extremely dense) during the BI-RADS 4th edition era (2005-2013), compared to 46.5% (95% CI = 43.8% to 49.1%) during the 5th edition era (2014-2016) (P = .93 from two-sided generalized score test). Among DBT screens in the BI-RADS 5th edition era, 45.8% (95% CI = 42.0% to 49.7%) were assessed as dense (P = .77 from two-sided generalized score test) compared to 46.5% (95% CI = 43.8% to 49.1%) dense on DM in BI-RADS 5th edition era. Results were similar when examining all four density categories and age subgroups. Clinicians, researchers, and policymakers may reasonably expect stable density distributions across screened populations despite changes to the BI-RADS guidelines and implementation of DBT.

Effect of Mammographic Screening Modality on Breast Density Assessment: Digital Mammography versus Digital Breast Tomosynthesis

Background Breast Imaging Reporting and Data System (BI-RADS) breast density categories assigned by interpreting radiologists often influence decisions surrounding supplemental breast cancer screening and risk assessment. The landscape of mammographic screening continuously evolves, and different mammographic screening modalities may result in different perception of density, reflected in different assignment of BI-RADS density categories. Purpose To investigate the effect of screening mammography modality on BI-RADS breast density assessments. Materials and Methods Data were retrospectively analyzed from 24 736 individual women (42.3% [10 455 of 24 736] white women, 57.7% [14 281 of 24 736] black women; mean age, 56.3 years; age range, 40.0-74.9 years) who underwent from one to seven mammographic screening examinations from September 2010 through February 2017 (60 766 examinations). Three screening modalities were used: digital mammography alone (8935 examinations); digital mammography with digital breast tomosynthesis (DBT; 30 779 examinations); and synthetic mammography with DBT (21 052 examinations). Random-effects logistic regression analysis was performed to estimate the likelihood of assignment to high versus low BI-RADS density category according to each modality, adjusted for ethnicity, age, body mass index (BMI), and radiologist. The interactions of modality with ethnicity and BMI on density categorization were also tested with the model. Results Women screened with DBT versus digital mammography alone had lower likelihood regarding categorization of high density breasts (digital mammography and DBT vs digital mammography: odds ratio, 0.69 [95% confidence interval: 0.61, 0.80], P < .001; synthetic mammography and DBT vs digital mammography: odds ratio, 0.43 [95% confidence interval: 0.37, 0.50], P < .001). Lower likelihood of high density was also observed at synthetic mammography and DBT compared with digital mammography and DBT (odds ratio, 0.62; 95% confidence interval: 0.56, 0.69; P < .001). There were interactions of modality with ethnicity (P = .007) and BMI (P = .003) on breast density assessment, with greater differences in density categorization according to modality observed for black women than for white women and groups with higher BMI. Conclusion Breast density categorization may vary by screening mammographic modality, and this effect appears to vary by ethnicity and body mass index. © RSNA, 2019 Online supplemental material is available for this article. See also the editorial by Philpotts in this issue.

Breast tomosynthesis: state of the art

Breast tomosynthesis is a continually improving tool for diagnostic radiologists. This update about tomosynthesis reviews the advantages of the technique both in patients with suspected or known disease and in screening, as well as its limitations, of which the dose of radiation is the most important. The more recent advent of synthesized mammography, computer-assisted detection, and tomosynthesis-guided biopsy have helped to reduce the dose of radiation used and have improved the diagnostic performance of tomosynthesis, so they are also discussed in this review.