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You are here: HomeRecommendations for Primary Care PracticePublished RecommendationsRecommendation SummaryOther Supporting Document : Evidence Summary: Supplemental Screening in Women With Dense Breasts

Evidence Summary: Supplemental Screening in Women With Dense Breasts

Other Supporting Document for Breast Cancer: Screening

Supplemental Screening for Breast Cancer in Women With Dense Breasts: A Systematic Review for the U.S. Preventive Services Task Force

By Joy Melnikow, MD, MPH; Joshua J. Fenton, MD, MPH; Evelyn P. Whitlock, MD, MPH; Diana L. Miglioretti, PhD; Meghan S. Weyrich, MPH; Jamie H. Thompson, MPH; and Kunal Shah


The information in this article is intended to help clinicians, employers, policymakers, and others make informed decisions about the provision of health care services. This article is intended as a reference and not as a substitute for clinical judgment.

This article may be used, in whole or in part, as the basis for the development of clinical practice guidelines and other quality enhancement tools, or as a basis for reimbursement and coverage policies. AHRQ or U.S. Department of Health and Human Services endorsement of such derivative products may not be stated or implied.

This article was published online first at www.annals.org on January 12, 2016.

Abstract

Background: Screening mammography has lower sensitivity and specificity in women with dense breasts, who experience higher breast cancer risk.

Purpose: To perform a systematic review of reproducibility of Breast Imaging Reporting and Data System (BI-RADS) density categorization and test performance and clinical outcomes of supplemental screening with breast ultrasonography, magnetic resonance imaging (MRI), and digital breast tomosynthesis (DBT) in women with dense breasts and negative mammography results.

Data Sources: MEDLINE, PubMed, EMBASE, and Cochrane database from January 2000 to July 2015.

Study Selection: Studies reporting BI-RADS density reproducibility or supplemental screening results for women with dense breasts.

Data Extraction: Quality assessment and abstraction of 24 studies from 7 countries; 6 studies were good-quality.

Data Synthesis: Three good-quality studies reported reproducibility of BI-RADS density; 13% to 19% of women were recategorized between “dense” and “nondense” at subsequent screening. Two good-quality studies reported that sensitivity of ultrasonography for women with negative mammography results ranged from 80% to 83%; specificity, from 86% to 94%; and positive predictive value (PPV), from 3% to 8%. The sensitivity of MRI ranged from 75% to 100%; specificity, from 78% to 94%; and PPV, from 3% to 33% (3 studies). Rates of additional cancer detection with ultrasonography were 4.4 per 1000 examinations (89% to 93% invasive); recall rates were 14%. Use of MRI detected 3.5 to 28.6 additional cancer cases per 1000 examinations (34% to 86% invasive); recall rates were 12% to 24%. Rates of cancer detection with DBT increased by 1.4 to 2.5 per 1000 examinations compared with mammography alone (3 studies). Recall rates ranged from 7% to 11%, compared with 7% to 17% with mammography alone. No studies examined breast cancer outcomes.

Limitations: Good-quality evidence was sparse. Studies were small and CIs were wide. Definitions of recall were absent or inconsistent.

Conclusion: Density ratings may be recategorized on serial screening mammography. Supplemental screening of women with dense breasts finds additional breast cancer but increases false-positive results. Use of DBT may reduce recall rates. Effects of supplemental screening on breast cancer outcomes remain unclear.

Primary Funding Source: Agency for Healthcare Research and Quality.

Introduction

Dense breasts are defined by mammographic appearance. The American College of Radiology's (ACR's) Breast Imaging Reporting and Data System (BIRADS) classifies breasts as almost entirely fatty (BIRADS category a), scattered areas of fibroglandular density (category b), heterogeneously dense (category c), or extremely dense (category d).

About 27.6 million (43%) women aged 40 to 74 years in the United States have dense breasts; most of these are classified as category c.1 Higher breast density is associated with decreased mammographic sensitivity and specificity and also with increased breast cancer risk. The relative hazard of breast cancer for women with dense breasts ranged from 1.50 (women aged 65 to 74 years) to 1.83 (women aged 40 to 49 years) in an analysis of 1,169,248 women enrolled in the Breast Cancer Surveillance Consortium (unpublished data). Increased breast density has been associated with hormone replacement therapy use, younger age, and lower body mass index.2 Data on breast density and race or ethnicity are limited. In the United States, Asian women have higher breast density3 but lower than average incidence of breast cancer.4 Increased breast density is not associated with higher breast cancer mortality among women with dense breasts diagnosed with breast cancer, after adjustment for stage and mode of detection.5

Supplemental breast cancer screening with additional screening modalities has been proposed to improve the early detection of breast cancers. No clinical guidelines explicitly recommend use of supplemental breast cancer screening on women with dense breasts,6 –9 but as of September 2015, 24 states had enacted legislation requiring that women be notified of breast density with their mammography results; 9 more states are considering mandatory notification10 (Appendix Table 1). Most states require specific language distinguishing dense (BI-RADS c and d) from nondense breasts, and 4 states require that insurers cover subsequent examinations and tests for women with dense breasts.11–14 Federal legislation requiring breast density notification is pending.15

This report summarizes a systematic review of current evidence on the reproducibility of BI-RADS breast density determinations and on test performance characteristics and outcomes of supplemental screening of women with dense breasts by using hand-held ultrasonography (HHUS), automated whole-breast ultrasonography (ABUS), breast magnetic resonance imaging (MRI), and digital breast tomosynthesis (DBT). Mandatory reporting laws frame notification of women as dense/nondense, so this review focused on this categorization.

Methods

The review protocol included an analytic framework with 4 key questions (KQs) (Appendix Figure 1). Detailed methods, including search strategies, detailed inclusion criteria, and excluded studies, are available in the full evidence report.16

Data Sources and Searches

MEDLINE, PubMed, EMBASE, and the Cochrane Library were searched for relevant English-language studies published between January 2000 and July 2015. We reviewed reference lists from retrieved articles and references suggested by experts.

Study Selection

Two investigators independently reviewed abstracts and full-text articles for inclusion according to predetermined criteria (E.P.W. and J.H.T. for KQ 1, J.M. and J.J.F. for KQs 2 to 4). Included studies examining the reproducibility of BI-RADS breast density categorization focused on asymptomatic women aged 40 years or older undergoing digital or film mammography. Included studies on supplemental screening with HHUS, ABUS, MRI, or DBT reported outcomes for asymptomatic women with dense breasts aged 40 years and older. In studies that focused primarily on women at high risk for breast cancer (including those with preexisting breast cancer or high-risk breast lesions [such as ductal carcinoma in situ, atypical hyperplasia, and lobular carcinoma in situ], BRCA mutations, familial breast cancer syndromes, or previous chest-wall radiation) and studies that included women with nondense breasts, we analyzed the relevant subset when available in the publication or provided by the authors.

A priori inclusion criteria limited studies on BIRADS reproducibility to fair- or good-quality randomized, controlled trials; cohort studies; or test sets involving multiple blind readings by at least 3 readers. Studies on test performance characteristics and outcomes of supplemental screening modalities were limited to fair- or good-quality randomized, controlled trials; cohort studies; or diagnostic accuracy studies with reference standards applied to all participants. We examined sensitivity, specificity, positive predictive values (PPVs), negative predictive values (NPVs), and available clinical outcomes (including cancer detection rates, recall rates, and biopsy rates). We defined recall as the need for any additional diagnostic testing after supplemental screening, including imaging and biopsy.

Data Extraction and Quality Assessment

Two investigators (E.P.W. and J.H.T. for KQ 1, J.M. and J.J.F. for KQs 2 to 4) critically appraised all included studies independently using the U.S. Preventive Services Task Force's (USPSTF's) design-specific criteria,17 supplemented with the National Institute for Health and Clinical Excellence methodology checklists18 and the Quality Appraisal Tool for Studies of Diagnostic Reliability.19 According to USPSTF criteria, a good-quality study generally met all prespecified criteria; fair-quality studies did not meet all criteria but had no important limitations. Poor-quality studies had important limitations that could invalidate results (inadequate or biased application of reference standard; population limited to very high-risk patients).

Data Synthesis and Analysis

When available or provided by the authors, results of supplemental screening for subgroups of women with dense breasts were extracted; we excluded those with other risk factors for breast cancer. We calculated the sensitivity and specificity of the supplemental breast screening tests for women with negative mammography results. Only cancers detected by the supplemental test after negative mammography results and cancers found at interval follow-up were included. Hence, the values reported represent the sensitivity and specificity for detection of additional cancer in women with negative mammography findings. Similarly, we defined cancer detection rates, recall rates, and biopsy rates to include only those cancer cases, recalls, and biopsies related to supplemental screening after negative results on mammography. Meta-analysis was not performed because there were few good-quality studies.

Role of the Funding Source

This research was funded by the Agency for Healthcare Research and Quality (AHRQ) under a contract to support the work of the USPSTF. The investigators worked with USPSTF members to develop and refine the scope, analytic frameworks, and KQs. AHRQ had no role in study selection, quality assessment, synthesis, or development of conclusions. AHRQ provided project oversight; reviewed the draft report; and distributed the draft for peer review, including to representatives of professional societies and federal agencies. AHRQ performed a final review of the manuscript to ensure that the analysis met methodological standards. The investigators are solely responsible for the content and the decision to submit the manuscript for publication.

Results

The literature search yielded 2067 unique citations; 128 full-text articles considered potentially relevant were reviewed to identify 24 unique studies meeting inclusion criteria (Appendix Figure 2). Table 120–43 provides the characteristics of included studies. No studies addressed the effect of supplemental screening (compared with women without supplemental screening) on breast cancer morbidity or mortality.

Accuracy and Reliability of BI-RADS Density Determination

Absent a gold standard for breast density, studies could not evaluate the accuracy of BI-RADS density determinations. Five studies reported repeated assignment of categorical BI-RADS breast density classification by the same or different radiologists, altogether including more than 440,000 women, almost all with data from 2 sequential screening mammograms. To reflect current U.S. practice, we included only studies based on the BI-RADS density categories. The 3 largest studies were set in the United States. Two used data from the Breast Cancer Surveillance Consortium,20, 22 and the third presented findings from community radiologists conducting repeated readings of a large screening test set.24 Two other small studies (not discussed here) were based on mammographic screening programs in Spain21 and Italy.23 All United States–based studies reflected community practice by use of clinical readings from community screening programs or test set readings by practicing community radiologists without additional training.

Overall, group prevalence of BI-RADS density ratings was similar across initial and subsequent examinations among community radiologists (Appendix Table 2), but there was greater disagreement at the individual level. On subsequent screening examinations, approximately 1 in 5 women (23%) was placed in a different BI-RADS density category (a, b, c, d) by the same radiologist, while approximately 1 in 3 was categorized differently when a different radiologist read the subsequent examination result (Table 2). Considering clinical interpretations that combine categories ("dense" representing those with BIRADS c or d and "nondense" representing BI-RADS a or b), 13% to 19% of women were reclassified into a different breast density category on their subsequent screening mammogram (Table 2).

These average estimates do not reflect greater extremes seen among outlier radiologists. Among 34 community radiologists reading sequential examination results in the same women,22 readers assigned the same BI-RADS density assessment on both mammograms 77% of the time, on average; however, individual readers' agreement between repeated ratings ranged from 62% to 87% (data not shown). In a study assessing repeat as well as cross-reader assignment of BI-RADS density categories by 19 radiologists in a test set of 341 examinations, radiologists assigned the same BI-RADS density assignment 82% of the time, on average, although individual readers varied from 66% to 95%.24

In community settings, 19% to 22% of examinations initially classified as dense were subsequently reclassified as nondense, whereas 10% to 16% of initially nondense examinations were reclassified as dense (Table 2). In contrast, initial clinical readings for a test set showed a higher percentage reclassified from nondense to dense than vice versa. Across studies, the most commonly assigned breast density categories (b or c) were also those most likely to be reclassified on subsequent examination (Table 2), representing a clinical reclassification between nondense and dense. Radiologists tended to agree with their own previous assessments of density better than with those made by other readers, although there was substantial variability among pairs of readers due to outliers (more details in full report16). These results apply most to postmenopausal women or those aged 50 years and older because these women made up 71% to 100% of the study samples.

Test Performance Characteristics of Supplemental Screening Technologies in Women With Dense Breasts

Nine studies reported test performance characteristics for supplemental screening with HHUS, ABUS, and MRI among women with negative mammography results (Table 2 and Appendix Figures 3 and 4). No studies reported test performance characteristics of DBT for women with dense breasts.

HHUS and ABUS

Two good-quality studies (from the United States25 and Italy26) and 3 fair-quality studies29, 30, 34 reported on HHUS, and 1 fair-quality study from the United States37 reported on ABUS (Table 3). We found no studies reporting variation in performance of these modalities by patient age and other breast cancer risk factors among women with dense breasts. Both good-quality studies applied consistent reference standards to identify interval cancer and included more than 1000 women. The Italian study included women who self-referred to a charity-funded breast clinic and reported findings separately by breast density category. The U.S. study included only women with dense breasts, but many women also had additional major risk factors. The authors provided data for the subset of women without major risk factors. Additional details on all included studies are found in the full report.16

Among women with dense breasts after recent negative results on screening mammography, the sensitivity of HHUS in the 2 good-quality studies for detecting all breast cancer (including ductal carcinoma in situ and invasive cancer) ranged from 0.80 (95% CI, 0.65 to 0.91) to 0.83 (CI, 0.59 to 0.96),25, 26 and specificity ranged from 0.86 (CI, 0.85 to 0.88) to 0.95 (CI, 0.94 to 0.95).25, 26 Sensitivity and specificity for invasive cancers were similar.25, 26 PPV in the good-quality studies ranged from 0.03 to 0.08; NPV was 0.99.25, 26 A single fair-quality study found that ABUS had performance characteristics similar to those of HHUS among women with dense breasts and negative mammography results.37

MRI

Three good-quality studies25, 38, 39 reported test characteristics of supplemental MRI screening (Table 3). These studies included many women with elevated risk for breast cancer. In 2 studies, authors provided us with unpublished data for the subgroup of women with dense breasts, excluding women at very high risk because of BRCA1/2 mutations, chest radiation, or personal histories of breast cancer.25, 39 In both, women had also recently had negative findings on screening with HHUS. The third study included stratified results based on risk factors.38

Among these subgroups of lower-risk women with dense breasts, the sensitivity of MRI screening (after negative mammography results) for all breast cancer ranged across studies from 0.75 (CI, 0.35 to 0.97) to 1.00 (CI, 0.59 to 1.00).25, 38, 39 Specificity also varied, ranging from 0.78 (CI, 0.73 to 0.83)25 to 0.93 (CI, 0.87 to 0.97).39 PPV ranged from 0.03 to 0.33 and NPVs were 0.99 to 1.00.

Cancer Detection and Recall Rates With Supplemental Screening

In general, supplemental screening after negative results on screening mammography consistently detected additional cases of breast cancer, most of which were invasive. Eighteen studies reported rates of additional cancer detected, and most also reported recall and biopsy rates associated with supplemental screening (Table 4 and Appendix Figure 5). With the possible exception of DBT, supplemental testing led to many additional recalls and biopsies.

HHUS and ABUS

Seven studies reported HHUS cancer detection rates,27, 28, 31–33 and 3 studies reported on ABUS.35–37 The two good-quality studies of HHUS consistently estimated an all-cancer detection rate after negative mammography findings of 4.4 per 1000 examinations (CI, 2.5 to 7.2),25, 26 with invasive cancer making up 93%25 and 88%26 of detected cancers. In the same women, mammography cancer detection rates were 4.7 per 1000 examinations in the U.S. study25 and 2.8 per 1000 examinations in the Italian study.26 Only the U.S. study reported the recall rate for supplemental HHUS: 14% (CI, 12.7% to 15.1%).25

Three fair-quality studies reported cancer detection rates for ABUS. Cancer detection rates after negative results on mammography ranged from 1.9 to 15.2 per 1000 examinations.36, 37 In comparison, the cancer detection rate from mammography alone in 1 of these studies was 4.3 per 1000 examinations.37 Recall rates varied between the studies from 2% (CI, 1.1% to 2.0%) to 14% (CI, 12.9% to 14.0%).35, 36

MRI

In 3 good-quality studies of MRI after negative mammography results, breast cancer detection rates varied from 3.5 (CI, 1.3 to 7.6) to 28.6 (CI, 5.9 to 81.2) per 1000 examinations,25, 38, 39 with small numbers of cancer cases detected (range, 2 to 7). In comparison, rates of mammography cancer detection in 2 of these studies for women with dense breasts were 4.1 and 7.0 per 1000 examinations.25, 38 Invasive breast cancer made up 67% and 86% of detected cancer, as reported by 2 studies.25, 39 Notably, women in these studies probably had higher breast cancer risk than the general population of women with dense breasts. A good-quality U.S. study evaluated supplemental HHUS and MRI among 334 women without BRCA mutations or previous breast cancer; after 3 screening rounds with negative mammography and HHUS results over 24 months, screening breast MRI identified 6 additional cases of invasive cancer.25

Recall rates ranged from 9% (CI, 4.0% to 15.7%) to 23% (CI, 18.9% to 28.3%); the rate was highest in the study with 3 rounds of screening.25, 39 Biopsy rates were not reported separately for subgroups of women without increased risk. Because 2 of the studies reported on only 1 round of screening, the cumulative effect of recall for additional imaging and biopsy would likely increase with additional screening rounds.

DBT

Four fair-quality studies of DBT (3 in the United States41–43 and 1 in Italy40) reported on screening populations of women with dense breasts. All U.S. studies were single-site, retrospective studies, and generally focused on outcomes before and after DBT introduction. In 1 study, breast cancer risk among women was described as above average;41 other studies did not report on risk factors.40, 42, 43 Three studies reported cancer detection rates with digital mammography alone ranging from 4.0 to 5.2 per 1000 examinations.40, 42, 43 With DBT, combined detection ranged from 5.4 (CI, 3.5 to 7.9) to 6.9 (CI, 4.8 to 9.6) per 1000 examinations.42, 43 A single study reported that 67% of cancer cases detected with combined DBT and mammography were invasive, the same proportion as with mammography alone.42 Recall rates with DBT in 3 retrospective U.S. studies ranged from 7% (CI, 6.2% to 7.7%) to 11% (CI, 10.0% to 11.7%), compared with 9% (CI, 8.4% to 11.0%) to 17% (CI, 15.0% to 18.2%) with digital mammography alone.41–43

Harms of Breast Density Notification

Only 1 study, a good-quality Canadian randomized, controlled trial, examined the effects of notifying women with normal screening results that their mammograms showed dense breasts.44 Women randomly assigned to the intervention group (n = 285) received a report of their breast density with letters summarizing their mammography results and a pamphlet on breast cancer risk factors, including density. No supplemental screening was recommended. Women randomly assigned to the control group (n = 333) were notified of mammography results without information on breast density. At 4 weeks, more women in the intervention group had statistically significantly increased knowledge of breast density (25% in the intervention group vs. 8% in the control group) and were more likely to perceive themselves as having elevated breast cancer risk. These differences did not persist at 6 months. Psychological distress, breast cancer worry, and preoccupation with breast cancer did not differ between groups.

Other Harms of Supplemental Screening

In studies of supplemental screening with HHUS and ABUS, more than 90% of positive test results were false-positive, and in MRI studies 66% to 97% of all positive test results were false-positives. Although no studies specifically addressed harms of supplemental screening in women with dense breasts, harms stemming from false-positive results are likely to be at least equivalent to those from mammography.45 We found no studies of whether focus on breast density distracts from assessment of other risk factors for breast cancer. Use of gadolinium contrast required for breast MRI has been associated with nephrogenic systemic fibrosis in patients with acute kidney injury or chronic kidney disease, but we found no reports of this adverse effect specifically related to breast MRI. The ACR recommends screening with serum creatinine before administration of gadolinium for those aged 60 years and older with hypertension, diabetes, or history of renal disease.46 Harms from DBT could come from additional breast radiation exposure.40–43, 47

Discussion

We examined the consistency of categorical BIRADS breast density determinations in U.S. community practices because this is the system recommended by the ACR and written into most of the legislative mandates. According to large, community practice–based studies, BI-RADS density assessments at a population level were generally consistent across sequential examinations by the same or different readers, but there was important variability among readings for individual women. Approximately 80% of examinations received a b or c BI-RADS density assessment; these categories were also most likely to be reassessed differently, whether on a separate reading of the same examination or on a subsequent examination, and whether read by the same or a different reader. As a result, across studies a sizeable 13% to 19% of women13–19 were reclassified from "nondense" to "dense" or vice versa. In these instances, mandated communications about elevated breast cancer risk or the need for additional clinical screenings could provide inconsistent information for the same woman in the span of 2 to 3 years.

Breast density findings can change because of multiple factors related to the woman being examined, the qualitative nature of the technique, and radiologist variability in interpretation of the examinations. The studies we examined tried to control for within-woman biological factors, suggesting that most of the variation in breast density assessment reflects within- and between radiologist variability in density interpretation and the limitations of the current BI-RADS approach. Concerns about BI-RADS breast density determinations are a major impetus for research examining other methods for assigning breast density, including automated volumetric estimates, ultrasonographic assessments, and other computer-assisted methods. Although variability is reduced by use of double readings, which is widely practiced in Europe,40 this approach is impractical in the United States because of workforce requirements. The introduction of standards and quality measures related to breast density categorization could help to minimize potential harms associated with variable breast density categorizations.

When combined with mandated direct-to-consumer communications, variability in breast density assignments may lead to unintended consequences. Reclassification from one overall category to another (for example, "dense" to "not-dense" or vice versa) may undermine a woman's confidence in the screening process and leave her uncertain about her risk for breast cancer, whereas the opposite reclassification may alarm women unnecessarily or prompt supplemental screening tests of uncertain value. The ACR has publicly expressed similar cautions about benefits, possible harms, and unintended consequences for the communication of breast density assessments to women.48

Few studies evaluated test performance of supplemental screening tests for women with dense breasts. In the studies identified, the sensitivity of supplemental MRI screening after negative screening mammography results appeared generally higher than that seen with HHUS screening. However, although we examined subsets of women without specific risk factors, we suspect that, in general, these women were at higher risk. Studies of MRI were small and variable in their sensitivity estimates. No study directly compared sensitivity of supplemental screening modalities among women with dense breasts. Specificity of supplemental screening modalities was similar, and PPV was low. We identified only one study of ABUS and no studies of DBT test performance in women with dense breasts. No studies examined the effects of age or other breast cancer risk factors on supplemental test performance characteristics in women with dense breasts. No studies reported on breast cancer morbidity and mortality outcomes.

Evidence on harms of supplemental screening was also sparse. Added to digital mammography, DBT more than doubles the radiation exposure from each screening examination.49–51 New estimates of cancer induced by radiation from breast imaging have recently been reported.47 Technology that allows reconstruction of the 2-dimensional breast images can reduce radiation exposure but is not widely disseminated.49 We found no reports of adverse effects from use of gadolinium contrast for breast MRI, but a tracking mechanism for this potentially severe, albeit rare, adverse effect should be considered. Potential harms resulting from overdiagnosis of breast cancer through supplemental screening can be identified only through rigorous prospective studies with long-term follow-up.

Our review was limited to studies published in English; studies published in other languages may have met inclusion criteria, although applicability to U.S. practice could be limited. For applicability and feasibility concerns, we focused only on BI-RADS breast density assessment. Studies did not examine the underlying reasons for variability in BI-RADS assessment within or between radiologists, nor did they evaluate any interventions to reduce the variability. The number, quality, and rigor of studies of diagnostic test characteristics and clinical outcomes were limited. Most studies lacked a complete reference standard, sufficient follow-up, or a clear description of follow-up, so diagnostic test performance characteristics could not be evaluated. Recall was often not clearly defined. No studies compared interval breast cancer rates, stage at diagnosis, or breast cancer mortality among two groups of women with dense breasts undergoing screening mammography with or without supplemental testing. No studies addressed the important potential risks of overdiagnosis and the associated harms of unnecessary treatment. Many studies included mixtures of women at increased breast cancer risk due to risk factors other than breast density, limiting the generalizability to the general screening population of women with dense breasts. Literature on ABUS and DBT for women with dense breasts was limited, as was literature on the harms of breast density notification. Only 1 comparative study of cohorts with and without supplemental screening adjusted for differences between cohorts.42

In conclusion, good-quality studies with U.S. radiologists show important reclassification between dense and nondense breasts in women undergoing sequential screening examinations. Reclassification of breast density may introduce confusion or reduce confidence among women. Moving from a "dense" to a "nondense" breast categorization may result in different mandated communications in states with breast density notification, as well as fluctuation in clinical recommendations for supplemental screening.

Limited evidence suggests that more breast cancer cases will be detected by supplemental HHUS and MRI screening of women with dense breasts, and most detected breast cancer cases will be invasive. Studies have not evaluated whether diagnosis of additional breast cancer by supplemental screening leads to improved clinical outcomes or what proportion of the cancer diagnosed represents overdiagnosis. Supplemental testing of women with dense breasts with HHUS or MRI is associated with increased recall rates for diagnostic investigation among women without breast cancer. Use of DBT may be associated with lower recall rates, but studies are few and retrospective. To define meaningful clinical outcomes of supplemental screening of women with dense breasts, well-designed, long-term, prospective, comparative studies of supplemental screening are needed.

Copyright and Source Information

Source: This article was published at www.annals.org on January 12, 2016.

Acknowledgment: The authors thank the following for their contributions to this project: AHRQ staff; the USPSTF; Joann Elmore, MD, MPH, Elizabeth Rafferty, MD, Jeffrey Tice, MD, Edward Sickles, MD, Barnett Kramer, MD, MH, Gretchen Gierach, PhD, and Gwendolyn Bryant-Smith, MD, who provided expert and federal partner review of the report; Wendie Berg, MD, PhD, and Christiane Kuhl, MD, for providing unpublished subgroup data; and Bruce Abbott, MLS, and Guibo Xing, PhD, at the University of California, Davis.

Grant Support: By AHRQ (contract HHSA-290-2012-00015-I).

Disclosures: Dr. Melnikow reports a contract with the Agency for Healthcare Research and Quality during the conduct of the study. Dr. Miglioretti reports grants from the Agency for Healthcare Research and Quality and the National Cancer Institute during the conduct of the study. Ms. Weyrich reports grants from the Agency for Healthcare Research and Quality during the conduct of the study. Ms. Thompson reports grants from the Agency for Healthcare Research and Quality during the conduct of the study. Authors not named here have disclosed no conflicts of interest. Disclosures can also be viewed at www.acponline.org/authors/icmje/ConflictOfInterestForms.do?msNum=M15-1789.

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24. Gard CC, Aiello Bowles EJ, Miglioretti DL, Taplin SH, Rutter CM. Misclassification of Breast Imaging Reporting and Data System (BIRADS) mammographic density and implications for breast density reporting legislation. Breast J. 2015;21:481-9. [PMID: 26133090]
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26. Corsetti V, Houssami N, Ghirardi M, Ferrari A, Speziani M, Bellarosa S, et al. Evidence of the effect of adjunct ultrasound screening in women with mammography-negative dense breasts: interval breast cancers at 1 year follow-up. Eur J Cancer. 2011;47:1021-6. [PMID: 21211962]
27. Brancato B, Bonardi R, Catarzi S, Iacconi C, Risso G, Taschini R, et al. Negligible advantages and excess costs of routine addition of breast ultrasonography to mammography in dense breasts. Tumori. 2007;93:562-6. [PMID: 18338490]
28. Girardi V, Tonegutti M, Ciatto S, Bonetti F. Breast ultrasound in 22,131 asymptomatic women with negative mammography. Breast. 2013;22:806-9. [PMID: 23558244]
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33. Weigert J, Steenbergen S. The Connecticut experiment: the role of ultrasound in the screening of women with dense breasts. Breast J. 2012;18:517-22. [PMID: 23009208]
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35. Brem RF, Tabá r L, Duffy SW, Inciardi MF, Guingrich JA, Hashimoto BE, et al. Assessing improvement in detection of breast cancer with three-dimensional automated breast US in women with dense breast tissue: the SomoInsight Study. Radiology. 2015;274: 663-73. [PMID: 25329763]
36. Giuliano V, Giuliano C. Improved breast cancer detection in asymptomatic women using 3D-automated breast ultrasound in mammographically dense breasts. Clin Imaging. 2013;37:480-6. [PMID: 23116728]
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39. Kuhl CK, Schrading S, Strobel K, Schild HH, Hilgers RD, Bieling HB. Abbreviated breast magnetic resonance imaging (MRI): first postcontrast subtracted images and maximum-intensity projection-a novel approach to breast cancer screening with MRI. J Clin Oncol. 2014;32:2304-10. [PMID: 24958821]
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49. Gur D, Zuley ML, Anello MI, Rathfon GY, Chough DM, Ganott MA, et al. Dose reduction in digital breast tomosynthesis (DBT) screening using synthetically reconstructed projection images: an observer performance study. Acad Radiol. 2012;19:166-71. [PMID: 22098941]
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Table 1. Characteristics of Included Studies

Study, Year (Reference)
USPSTF Quality Rating
Design Country Examinations/
Women Analyzed
Follow-up Period, mo Population Characteristics
Breast Imaging Reporting and Data System density assessment
Harvey et al, 201320
Good
Cohort United States 871,502 examinations
435,751 women
<36* Age: 58.8 y (mean)
Redondo et al, 201221
Good
Stratified random sample Spain 100 examinations
100 women
6* Age: 50–64 y (range)
Spayne et al, 201222
Good
Cohort United States 11,755 women 3–24* Age: 66 y (median)
Bernardi et al, 201223
Fai
Test set Italy 100 examinations
100 women
NA Age: 43 y (median)
Gard et al, 201524
Fair
Test set United States 341 women 6* NR
Hand-held ultrasonography
Berg et al, 201225
Good
Test accuracy United States 3414 examinations
1216 women
>12 Age: 55.2 y (mean)
Dense breasts: 100%
Personal history: 0%
Family history: NR
Corsetti et al, 201126
Good
Test accuracy Italy 7224 examinations
3356 women
12 Age: 55% <50 y
Dense breasts: 100%
Personal history: NR
Family history: NR
Brancato et al, 200727
Fair
Cohort Italy 5227 women NR Age: 68% 40–49 y
Dense breasts: 100%
Personal history: NR
Family history: NR
Girardi et al, 201328
Fair
Cohort United States 9960 women 12 Age: 51.2 y (mean)
Dense breasts: 100%
Personal history: 9.8%
Family history: NR
Hooley et al, 201229
Fair
Test accuracy United States 648 women >15 Age: 52 y (mean)
Dense breasts: 100%
Personal history: NR
Family history: NR
Leong et al, 201230
Fair
Test accuracy Singapore 106 women 12–24 Age: 45.1 y (mean)
Dense breasts: 100%
Personal history: 5%
Family history: 20.9%
Parris et al, 201331
Fair
Cohort United States 5519 women NR Age: 53.6 y (mean)
Dense breasts: 89%
Personal history: 6%
Family history: 42%
Venturini et al, 201332
Fair
Cohort Italy 826 women NR Age: 100% <50 y
Dense breasts: 100%
Personal history: NR
Family history: 24%
Weigert and Steenbergen, 201233
Fair
Cohort United States 8647 examinations
8647 women
NR Age: 54.4 y (mean)
Dense breasts: 100%
Personal history: NR
Family history: NR
Youk et al, 201134
Fair
Test accuracy South Korea 446 examinations 24 Age: 47.5 y (mean)
Dense breasts: 100%
Personal history: 0%
Family history: NR
Automated whole-breast ultrasonography
Brem et al, 201535
Fair
Cohort United States 15,318 women 12 Age: 53.3 y (mean)
Dense breasts: 100%
Personal history: 3.6%
Family history: 44.8%
Giuliano and Giuliano, 201336
Fair
Cohort United States 3418 women 12 Age: NR
Dense breasts: 100%
Personal history: 0%
Family history: 0%
Kelly et al, 201037
Fair
Test accuracy United States 6425 examinations
4419 women
12 Age: 53 y (mean)
Dense breasts: 68%
Personal history: 10%
Family history: 30%
Magnetic resonance imaging
Berg et al, 201225
Good
Test accuracy United States 334 examinations 12 Age: 56.8 y (mean)
Dense breasts: 100%
Personal history: NR
Family history: NR
Kriege et al, 200638
Good
Test accuracy The
Netherlands
1723 examinations 12 Age: 40 y (mean)
Dense breasts: 100%
Personal history: NR
Family history: 100%
Kuhl et al, 201439
Good
Test accuracy Germany 105 women 24 Age: 53.2 y (mean)
Dense breasts: 100%
Personal history: NR
Family history: NR
Digital breast tomosynthesis
Ciatto et al, 201340
Fair
Cohort Italy 1215 women No follow-up except on biopsy results Age: 58 y (median)
Dense breasts: 100%
Personal history: NR
Family history: NR
Haas et al, 201341
Fair
Cohort United States 4794 examinations No follow-up except on biopsy results Age: 33.3% <40 y
Dense breasts: 100%
Personal history: 5.5%
Family history: NR
McCarthy et al, 201442
Fair
Cohort United States 8545 examinations No follow-up except on biopsy results Age: 70% >50 y
Dense breasts: 100%
Personal history: NR
Family history: NR
Rose et al, 201343
Fair
Cohort United States 11,675 examinations No follow-up except on biopsy results Age: 54.2 y (mean)
Dense breasts: 100%
Personal history: NR
Family history: NR

NA = not applicable; NR = not reported; USPSTF = U.S. Preventive Services Task Force.
* Time between mammographic assessments.
Data reflect the entire study population, not necessarily the subgroup with dense breasts.

Table 2. Potential Misclassification of Breast Imaging Reporting and Data System Density Categorization by Density Categories

Study, Year (Reference)
USPSTF Quality Rating
Repeat Exam Readers Women Receiving
a Different
Breast Density
Classification at
Second Exam (4 categories), %
Women Receiving an
Opposite Breast
Density Classification
at Second Exam (2 categories), %
Dense
Exams
Reclassified as Nondense*
(c or d to a or b), %
Dense
Exams
Reclassified as
Nondense
(c to a or b), %
Dense
Exams
Reclassified as
Nondense
(c to b), %
Nondense
Exams
Reclassified
as Dense§
(a or b to c or d), %
Nondense
Exams
Reclassified
as Denseǁ
(b to c or d), %
Nondense
Exams
Reclassified
as Dense
(b to c)
Harvey et al, 201320
Good
Different community radiologists (n = 703) 32 18.7 22 21 20 16 16 15
Spayne et al, 201222
Good
Same community radiologists (n = 34) 23 12.6 19 19 18 10 10 10
Gard et al, 201524
Fair
Same community radiologists (n = 19) 29 16.9 10 10 10 23 23 23

USPSTF = U.S. Preventive Services Task Force.
* Categorized as “heterogeneously dense” or “extremely dense” at first examination and “almost entirely fat” or “scattered fibroglandular densities” at second examination.
Categorized as “heterogeneously dense” at first examination and “almost entirely fat” or “scattered fibroglandular densities” at second examination.
Categorized as “heterogeneously dense” at first examination and “scattered fibroglandular densities” at second examination.
§ Categorized as “almost entirely fat” or “scattered fibroglandular densities” at first examination and “heterogeneously dense” or “extremely dense” at second examination.
ǁ Categorized as “scattered fibroglandular densities” at first examination and “heterogeneously dense” or “extremely dense” at second examination.
Categorized as “scattered fibroglandular densities” at first examination and “heterogeneously dense” at second examination.

Table 3. Test Performance Characteristics for Supplemental Hand-Held Ultrasonography, Automated Whole-Breast Ultrasonography, and Magnetic Resonance Imaging

Study, Year (Reference)
USPSTF Quality Rating
Sensitivity
(95% CI)
Specificity
(95% CI)
PPV
(95% CI)
NPV
(95% CI)
Hand-held ultrasonography
Berg et al, 201225
Good
0.83 (0.59–0.96) 0.86 (0.85–0.88) 0.03 (0.02–0.05) 1.00 (1.00–1.00)
Corsetti et al, 201126
Good
0.80 (0.65–0.91) 0.95 (0.94–0.95) 0.07 (0.05–0.10) 1.00 (1.00–1.00)
Hooley et al, 201229
Fair
1.00 (0.29–1.00) 0.77 (0.73–0.80) 0.02 (0.01–0.06) 1.00 (0.99–1.00)
Leong et al, 201230
Fair
1.00 (0.16–1.00) 0.79 (0.70–0.86) 0.08 (0.02–0.26) 1.00 (0.96–1.00)
Youk et al, 201134
Fair
1.00 (0.72–1.00) 0.72 (0.67–0.76) 0.08 (0.05–0.14) 1.00 (0.99–1.00)
Automated whole-breast ultrasonography
Kelly et al, 201037
Fair
0.68 (0.50–0.83) 0.92 (0.91–0.92) 0.04 (0.03–0.06) 1.00 (1.00–1.00)
Magnetic resonance imaging
Berg et al, 201225
Good
1.00 (0.59–1.00) 0.78 (0.73–0.83) 0.09 (0.04–0.17) 1.00 (0.99–1.00)
Kriege et al, 200638
Good
0.75 (0.35–0.97) 0.89 (0.87–0.90) 0.03 (0.01–0.06) 1.00 (1.00–1.00)
Kuhl et al, 201439
Good
1.00 (0.29–1.00) 0.94 (0.88–0.98) 0.33 (0.12–0.66) 1.00 (0.96–1.00)

NPV = negative predictive value; PPV = positive predictive value; USPSTF = U.S. Preventive Services Task Force.

Table 4. Breast Cancer Detection Outcomes for Supplemental HHUS, ABUS, MRI, and DBT

Study, Year (Reference)
USPSTF Quality Rating
Cancer Cases Detected Cancer Detection Rate
(95% CI)
Recall Rate %,
(95% CI)
Biopsy Rate %*,
(95% CI)
HHUS
Berg et al, 201225
Good
15/3414 examinations 4.4 per 1000 examinations (2.5–7.2) 14 (12.7–15.1) NR
Corsetti et al, 201126
Good
32/7224 examinations 4.4 per 1000 examinations (3.0–6.2) NR 6 (5.4–6.5)
Brancato et al, 200727
Fair
2/5227 women 0.4 per 1000 women (0–1.4) 2 (1.7–2.5) 1 (0.9–1.6)
Girardi et al, 201328
Fair
22/9960 women 2.2 per 1000 women (1.4–3.3) NR NR
Hooley et al, 201229
Fair
3/648 women 4.6 per 1000 women (1.0–13.5) 24 (20.3–27.0) 7 (5.2–9.4)
Leong et al, 201230
Fair
2/106 women 18.9 per 1000 women (1.7–50.3) 17 13 (7.4–21.2)
Parris et al, 201331
Fair
10/5519 women 1.8 per 1000 women (0.9–3.3) NR 3 (2.8–3.8)
Venturini et al, 201332
Fair
2/826 women 2.4 per 1000 women (0.3–8.7) 10 (7.5–11.7) 1 (0.6–2.2)
Weigert and Steenbergen, 201233
Fair
25/8647 women 2.9 per 1000 women (1.9–4.3) 14 (13.1–14.5) 5 (4.4–5.3)
Youk et al, 201134
Fair
11/446 examinations 24.7 per 1000 examinations
(12.4–43.7)
14 (10.6–17.2) 11 (8.2–14.3)
ABUS
Brem et al, 201535
Fair
30/15 318 women 1.9 per 1000 examinations (1.3–2.8) 14 (12.9–14.0) 4 (3.4–4.0)
Giuliano and Giuliano, 201336
Fair
DM + ABUS: 52/3418 women
DM: 19/4076 women
DM + ABUS: 15.21 per 1000 women (11.4–19.9)
DM: 4.7 per 1000 women (2.8–7.3)
2 (1.1–2.0) NR
Kelly et al, 201037
Fair
23/6425 examinations 3.6 per 1000 examinations (2.3–5.4) 9 (8.0–9.4) NR
MRI
Berg et al, 201225
Good
7/334 examinations 21 per 1000 examinations (8.5–42.7) 23 (18.9–28.3) NR
Kriege et al, 200638
Good
6/1723 examinations 3.5 per 1000 examinations (1.3–7.6) 12 (10.0–13.1) NR
Kuhl et al, 201439
Good
3/105 women 28.6 per 1000 women (5.9–81.2) 9 (4.0–15.7) NR
DBT
Ciatto et al, 201340
Fair
DBT + DM: 8/1215 examinations
DM: 5/1215 examinations
DBT + DM: 6.6 per 1000 examinations (2.9–12.9)
DM: 4.1 per 1000 examinations (1.3–9.6)
DBT + DM: 7 (5.2–8.1)
DM: 7 (5.8–8.8)
NR
Haas et al, 201341
Fair
NR NR DBT + DM: 10 (8.6–10.9)
DM: 17 (15.0–18.2)
NR
McCarthy et al, 201442
Fair
DBT + DM: 35/5056 examinations
DM: 18/3489 examinations
DBT + DM: 6.9 per 1000 examinations (4.8–9.6)
DM: 5.2 per 1000 examinations (3.1–8.1)
DBT + DM: 11 (10.0–11.7)
DM: 13 (11.7–14.0)
NR
Rose et al, 201343
Fair
DBT + DM: 25/4666 examinations
DM: 28/7009 examinations
DBT + DM: 5.4 per 1000 examinations (3.5–7.9)
DM: 4.0 per 1000 examinations (2.7–5.8)
DBT + DM: 7 (6.2–7.7)
DM: 9 (8.4–11.0)
NR

ABUS = automated whole-breast ultrasonography; DBT = digital breast tomosynthesis; DM = digital mammography; HHUS = hand-held ultrasonography; MRI = magnetic resonance imaging; NR = not reported; USPSTF = U.S. Preventive Services Task Force.
* Biopsy rate includes needle aspiration, core needle, and open biopsies.
Data are based on the 106 women with complete follow-up (out of 141 total).

Appendix Table 1. Breast Density Legislation in the United States

Status of Legislation* Legislative Details States
Pending Legislation Drafting legislation mandating breast density notification Florida, Maine, Illinois, Colorado, Vermont, Mississippi
Introduced legislation mandating breast density notification Washington, Iowa, Indiana, Kentucky, South Carolina, Georgia
Enacted Legislation Mandates patient notification about breast density California, Arizona, Oregon, Nevada, Massachusetts, Minnesota, Texas, Alabama, Missouri, Tennessee, North Carolina, Virginia, Maryland, New Jersey, Pennsylvania, New York, Connecticut, Rhode Island, Hawaii, Michigan, Ohio, Louisiana, Delaware, North Dakota
Requires specific language for patient notification California, Arizona, Texas, Alabama, Missouri, Tennessee, North Carolina, Virginia, Maryland, New Jersey, Pennsylvania, New York, Connecticut, Rhode Island, Hawaii, Ohio, Michigan, Louisiana, Massachusetts
Requires that all mammography reports provide information about breast density and the patient's current breast density level Nevada, North Carolina, Maryland, Pennsylvania, Connecticut, Louisiana
Requires that insurers cover appropriate medical examinations and tests for women with dense breasts Illinois, Connecticut, New Jersey, Indiana

Source:10.
* As of September 2015.
During the 2015 legislative season.

Appendix Figure 1. Analytic framework.

KQ 1: What are the accuracy and reproducibility of BI-RADS determination of breast density?
KQ 2: What are the test performance characteristics of newer technologies for breast cancer screening when used as supplemental tests after a negative screening mammogram in women found to have dense breasts, and how do these performance characteristics differ by age and risk factors?
KQ 3: When performed after a negative screening mammogram in women found to have dense breasts, what is the effectiveness of supplemental screening with breast ultrasonography, MRI, or breast tomosynthesis on proximate clinical outcomes, including cancer detection rates, DCIS detection rates, stage at diagnosis, recall rates, biopsy rates, and interval cancer rates?
KQ 4: What are the harms associated with being identified as having dense breasts, including psychological and quality-of-life impacts and harms associated with supplemental screening evaluation, including evaluation of false-positive results?

BI-RADS = Breast Imaging Reporting and Data System; DCIS = ductal carcinoma in-situ; KQ = key question; MRI = magnetic resonance imaging.

Text Description.

Appendix Figure 1 is the analytic framework that depicts the four key questions described in the report. In general, the figure illustrates how screening for breast cancer in asymptomatic women age 40 years and older may identify women with dense breasts, and these women may need to undergo additional supplemental screening to identify breast cancer, and how supplemental screening may lead to improved outcomes or potential harms. In addition, it examines the accuracy of breast density determinations by radiologists, as well as the harms associated with being identified as having dense breasts.

Appendix Figure 2. Summary of evidence search and selection.

KQ = key question.

Text Description.

Appendix Figure 2 is a flow chart that summarizes the search and selection of articles. There were 2,011 citations identified through literature databases and an additional 56 citations were identified from outside sources, such as reference lists and suggestions from peer reviewers. After duplicates were removed, 2,067 unique citations were screened at the title/abstract stage. The full-text of 128 citations were examined for inclusion for one or more of the four Key Questions. The following number of articles were included for Key Question 1 (n=5), Key Question 2 (n=8), Key Question 3 (n=19), and Key Question 4 (n=1).

Appendix Table 2. Consistency of Breast Imaging Reporting and Data System Density Categories and Population Categorization

Study Time Between
Assessments
Study Sample Inclusion Criteria Assessments a* (%) b (%) c (%) d§ (%)
Harvey et al, 201320
<36 mo
Women aged 40 years and older, with no history of breast cancer or reported use of hormone therapy at the time of exam or during the previous year, and had two or more screening mammographic examinations less than 36 months apart between January 1, 2000 and December 31, 2009
(n = 435,751)
Exam 1
Exam 2
9.4
10.2
45.2
45.1
37.9
37.2
7.5
7.2
Spayne et al, 201222
3–24 mo
Women who were postmenopausalǁ, with no history of breast cancer or reported use of hormone therapy, and had two or more film-screen screening or bilateral diagnostic mammograms including BI-RADS breast density assessments between January 1, 1996 and December 31, 2006
(n = 11,755)
Exam 1
Exam 2
9.8
9.2
61.0
60.2
26.6
28.1
2.5
2.5
Gard et al, 201524
6 mo
Women contributing examinations to the test set had a screening mammogram interpreted in the health care system between 1996 and 1998 and were enrolled in the system for at least 2 years following screening; the test set was designed to include about twice as many examinations of women with cancer as without, and roughly equal numbers of non-dense and dense examinations based on clinical interpretation
(n = 341)
Reading 1
Reading 2
6.1
4.5
44.3
39.2
38.3
47.0
11.4
9.3

BI-RADS=Breast Imaging Reporting and Data System.
* Breast density category a = almost entirely fat.
Breast density category b = scattered fibroglandular densities.
Breast density category c = heterogeneously dense.
§ Breast density category d = extremely dense.
ǁ Aged 55 years or older or reported having experienced natural menopause, having had both ovaries removed, or having more than 365 days elapse since their last menstrual period.
Clinical interpretation of exams prior to inclusion in test set.

Appendix Figure 3. Sensitivity of supplemental screening with hand-held ultrasonography, automated whole-breast ultrasonography, and magnetic resonance imaging in detecting breast cancer.


These estimates include ductal carcinoma in situ and invasive cancers. ABUS = automated whole-breast ultrasonography; HHUS = hand-held ultrasonography; MRI = magnetic resonance imaging.
* Good-quality study.

Text Description.

Appendix Figure 3 is a forest plot depicting the sensitivity of supplemental hand-held ultrasonography, automated whole-breast ultrasound, and magnetic resonance imaging in detecting breast cancer, including ductal carcinoma in situ and invasive cancers.

Appendix Figure 4. Specificity of supplemental screening with hand-held ultrasonography, automated whole-breast ultrasonography, and magnetic resonance imaging in detecting breast cancer.

These estimates include ductal carcinoma in situ and invasive cancer. ABUS = automated whole-breast ultrasonography; HHUS = hand-held ultrasonography; MRI = magnetic resonance imaging.
* Good-quality study.

Text Description.

Appendix Figure 4 is a forest plot depicting the specificity of supplemental hand-held ultrasonography, automated whole-breast ultrasound, and magnetic resonance imaging in detecting breast cancer, including ductal carcinoma in situ and invasive cancers.

Appendix Figure 5. Breast cancer detection rates of supplemental screening with hand-held ultrasonography, automated whole-breast ultrasonography, magnetic resonance imaging, and DBT

These estimates include ductal carcinoma in situ and invasive cancer. ABUS = automated whole-breast ultrasonography; DBT = digital breast tomosynthesis; HHUS = hand-held ultrasonography; MRI = magnetic resonance imaging.
* Good-quality study.

Text Description.

Appendix Figure 5 is a forest plot depicting the breast cancer detection rates of supplemental hand-held ultrasonography, automated whole-breast ultrasound, and magnetic resonance imaging in detecting breast cancer, including ductal carcinoma in situ and invasive cancers.

Current as of: January 2016

Internet Citation: Evidence Summary: Supplemental Screening in Women With Dense Breasts: Breast Cancer: Screening. U.S. Preventive Services Task Force. August 2016.
https://www.uspreventiveservicestaskforce.org/Page/Document/evidence-summary-supplemental-screening-in-women-with-dense-/breast-cancer-screening1

USPSTF Program Office   5600 Fishers Lane, Mail Stop 06E53A, Rockville, MD 20857