Abstract
Background
It is unclear whether breast US screening outcomes for women with dense breasts vary with levels of breast cancer risk.
Purpose
To evaluate US screening outcomes for female patients with dense breasts and different estimated breast cancer risk levels.
Materials and Methods
This retrospective observational study used data from US screening examinations in female patients with heterogeneously or extremely dense breasts conducted from January 2014 to October 2020 at 24 radiology facilities within three Breast Cancer Surveillance Consortium (BCSC) registries. The primary outcomes were the cancer detection rate, false-positive biopsy recommendation rate, and positive predictive value of biopsies performed (PPV3). Risk classification of participants was performed using established BCSC risk prediction models of estimated 6-year advanced breast cancer risk and 5-year invasive breast cancer risk. Differences in high- versus low- or average-risk categories were assessed using a generalized linear model.
Results
In total, 34 791 US screening examinations from 26 489 female patients (mean age at screening, 53.9 years ± 9.0 [SD]) were included. The overall cancer detection rate per 1000 examinations was 2.0 (95% CI: 1.6, 2.4) and was higher in patients with high versus low or average risk of 6-year advanced breast cancer (5.5 [95% CI: 3.5, 8.6] vs 1.3 [95% CI: 1.0, 1.8], respectively; P = .003). The overall false-positive biopsy recommendation rate per 1000 examinations was 29.6 (95% CI: 22.6, 38.6) and was higher in patients with high versus low or average 6-year advanced breast cancer risk (37.0 [95% CI: 28.2, 48.4] vs 28.1 [95% CI: 20.9, 37.8], respectively; P = .04). The overall PPV3 was 6.9% (67 of 975; 95% CI: 5.3, 8.9) and was higher in patients with high versus low or average 6-year advanced cancer risk (15.0% [15 of 100; 95% CI: 9.9, 22.2] vs 4.9% [30 of 615; 95% CI: 3.3, 7.2]; P = .01). Similar patterns in outcomes were observed by 5-year invasive breast cancer risk.
Conclusion
The cancer detection rate and PPV3 of supplemental US screening increased with the estimated risk of advanced and invasive breast cancer.
© RSNA, 2024
Supplemental material is available for this article.
See also the editorial by Helbich and Kapetas in this issue.
Summary
In women with dense breasts, US screening yielded a higher cancer detection rate and positive predictive value of biopsy in women with high versus low or average risk of invasive or advanced cancer.
Key Results
■ In a retrospective study including 26 489 female patients and 34 791 US screening examinations, the cancer detection rate was higher in female patients with high 6-year advanced breast cancer risk than in those with low or average risk (5.5 vs 1.3 per 1000 examinations, respectively; P = .003).
■ The positive predictive value of biopsy was higher in patients with high versus low or average risk of advanced cancer (15.0% vs 4.9%, respectively; P = .01).
Introduction
High breast density limits the accuracy of screening mammography (1) and is an independent risk factor for the future development of breast cancer (2). Because approximately half of women have dense breast tissue (3), there is widespread interest in supplemental breast cancer screening modalities (4,5).
Whole-breast US has emerged as a breast cancer screening modality in numerous settings around the globe (reviewed by Mizzi et al [6]). Prospective trials have demonstrated that it increases cancer detection rates and reduces interval cancer rates compared with mammography alone (7,8). However, the role of whole-breast US for supplemental screening remains less well-defined than that of other screening modalities, such as mammography and MRI, with screening guidelines acknowledging the limitations of the available data. Guidelines from the American College of Radiology and the National Comprehensive Cancer Network state that women with dense breasts as their primary risk factor for cancer may consider undergoing US screening after weighing the benefits and harms of this additional testing (9,10). Moreover, the American College of Radiology Appropriateness Criteria state that screening US for women with dense breasts may be appropriate but is controversial due to insufficient data (11). The U.S. Preventive Services Task Force 2024 recommendations (12) state that there is insufficient evidence to support supplemental screening in otherwise average-risk women with dense breasts. Thus, large studies about the performance of screening US are needed to provide additional evidence to guide policy and risk-to-benefit discussions related to supplemental screening.
To our knowledge, no prior studies have examined how US screening outcomes might differ based on breast cancer risk. Interval and advanced breast cancer rates among women with dense breasts undergoing mammography vary widely with different estimated 5-year invasive breast cancer risk levels (1,13). A prediction model for 6-year advanced breast cancer risk among women undergoing mammography was recently developed to guide discussion of supplemental imaging (14). The purpose of this study was to evaluate US screening outcomes for women with varying degrees of invasive and advanced breast cancer risk using observational data from US screening examinations among female patients with dense breasts at radiology facilities within three Breast Cancer Surveillance Consortium (BCSC) registries.
Materials and Methods
This retrospective observational study used data from the following three BCSC registries: the Metro Chicago Breast Cancer Registry, the San Francisco Mammography Registry, and the Vermont Breast Cancer Surveillance System (15). These registries are the only three within the BCSC that included facilities that conducted routine screening US examinations. Each registry collects clinical data from female patients undergoing breast imaging at participating health care facilities within their catchment (https://bcsc-research.org/). The registries and a central statistical coordinating center were given institutional review board approval for the study procedures, including passive permission or a waiver of consent to enroll participants, link data, and perform analytic studies. All procedures were compliant with the Health Insurance Portability and Accountability Act.
Study Sample
The study included US screening examinations from female patients aged 40–74 years with no personal history of breast cancer or prior mastectomy. These examinations were performed at 24 participating health care facilities with at least 50 screening US examinations conducted during January 2014 to October 2020 (Fig 1). A total of 130 interpreting radiologists contributed to the data. Among the facilities, two used automated breast US screening, 16 used handheld US screening, and six used both modalities.
Figure 1:
Inclusion and exclusion flowchart. BCSC = Breast Cancer Surveillance Consortium, BI-RADS = Breast Imaging Reporting and Data System.
US screening examinations were identified based on the facility’s coding of the indication as either screening or diagnostic. Consecutive examinations conducted during the study period were included. To avoid misclassifying diagnostic US examinations, the definition of US screening was further restricted to bilateral examinations. Examinations were required to have no associated positive breast imaging findings within the past 9 months unless it was resolved to a negative assessment before the US examination, and also required to have no screening US within the previous 90 days. The exclusion criteria composed of fewer than 90 days of follow-up for cancer diagnosis, nondense breasts (Breast Imaging Reporting and Data System [BI-RADS] categories A and B), self-reported symptoms other than pain, missing initial BI-RADS assessment, and same-day positive screening mammography examination (digital mammography or digital breast tomosynthesis) or combined mammography/US assessment. Breast density was defined based on mammography within 18 months of the US screening. Therefore, all included US screenings occurred within 18 months of mammography. A previous publication (15) described the characteristics of the female patients included in this study.
Clinical and Demographic Data Collection
BCSC registries receive examination-level data from participating health care facilities. Breast imaging data, including imaging modality, indication, assessments, and breast density, were recorded using standard BI-RADS nomenclature (16). Demographic and risk factor information was self-reported by female patients at the time of breast imaging or extracted from electronic medical records. Registries also collect breast biopsy and breast diagnosis data by linking to BCSC registry pathology databases, regional Surveillance Epidemiology and End Results Program programs, and state tumor registries (17).
Evaluations and Definitions
Breast density (BI-RADS category A, almost entirely fatty; B, scattered fibroglandular densities; C, heterogeneously dense; or D, extremely dense) was assigned based on the density recorded on the woman’s most recent mammogram within 18 months of the US screen. Women with heterogeneously or extremely dense breasts were considered to have dense breasts (3,18,19). Benign diagnoses were grouped based on the risk of developing subsequent breast cancer using published taxonomy (20). The order of risk is as follows: risk with lobular carcinoma in situ is greater than atypical hyperplasia, which is greater than proliferative without atypia, which is greater than nonproliferative. If a woman reported a prior biopsy with no available pathology result, the diagnosis was recorded as unknown. Self-reported race and ethnicity were collected because they represent a social construct that is associated with breast cancer risk. Race and ethnicity were recorded and categorized as Hispanic/Latina or non-Hispanic/Latina. Non-Hispanic/Latina individuals were further categorized as American Indian or Alaskan Native, Asian, Black, Native Hawaiian or Pacific Islander, White, self-reported Other race, or Multiracial.
The 5-year invasive breast cancer risk was calculated using the BCSC version 2 risk model, which is based on age, race and ethnicity, first-degree family history of breast cancer, prior benign breast biopsy history, and breast density (20). Using previously developed definitions (1), female patients were classified as having a low (<1%), average (1.00%–1.66%), intermediate (1.67%–2.49%), or high (≥2.50%) 5-year invasive breast cancer risk. The 6-year advanced breast cancer risk (prognostic pathologic stage II or higher) (21) was calculated using the BCSC 6-year advanced breast cancer risk model, which is based on age, race and ethnicity, first-degree family history of breast cancer, history of benign breast disease, breast density, menopausal status, body mass index, and mammography screening interval (14). For all female patients, advanced cancer risk was estimated assuming an annual mammography screening regimen without supplemental screening. Advanced cancer risk was classified according to previously defined thresholds, as follows: low, less than 0.171%; average, 0.172%–0.3796%; intermediate, 0.3797%–0.6582%; and high, greater than 0.6582% (14). See Appendix S1 for further details regarding risk group classifications.
Primary outcomes included the cancer detection rate, false-positive biopsy recommendation rate, and positive predictive value of biopsies performed (PPV3). Screen-detected cancers (ductal carcinoma in situ or invasive carcinoma) were defined as cancers diagnosed within 90 days of screening US with a final BI-RADS assessment category of 3, 4, or 5 (22). False-positive biopsy recommendations were defined as a biopsy recommendation (final BI-RADS category of 4 or 5) with no cancer diagnosis within 90 days. PPV3 was defined as the proportion of examinations with biopsy recommendations and a documented biopsy performed that ultimately yielded a screen-detected cancer. Secondary outcomes included abnormal interpretation rate, short-interval follow-up rate, biopsy recommendation rate, and invasive cancer detection rate (Appendix S1).
Statistical Analysis
All statistical analyses used the US screening examination as the unit of analysis. Descriptive statistics were used to describe the distribution of demographic and risk characteristics of female patients undergoing US screening. Screening performance measures were calculated using logistic regression, and 95% CIs were estimated with general estimating equations that accounted for nonnested clusters of patients, radiologists, and facilities, assuming a working independence correlation structure (23,24). Separate analyses were conducted to estimate US screening performance measures stratified by categorical age group, breast density, US screening round, BCSC 6-year advanced breast cancer risk, BCSC 5-year invasive breast cancer risk classification, and time since most recent mammographic examination. Linear trends were evaluated by modeling each risk classification as an ordinal variable. Differences in high versus low or average risk categories were assessed using a generalized linear model with a binomial distribution and identity link with general estimating equations to account for nonnested clusters. All analyses were conducted by a researcher (L.I., with 25 years of experience) using statistical software (SAS version 9.4; SAS Institute). P < .05 indicated statistical significance.
Results
Patient Characteristics
From an initial sample of 51 211 US screening examinations, we excluded 9219 with fewer than 90 days of follow-up for a cancer diagnosis, 2630 with nondense breasts, 115 with self-reported symptoms other than pain, 19 missing an initial assessment, and 4437 conducted on the same day as a positive screening mammographic examination or with a combined mammography and US assessment. This yielded a final sample of 34 791 US screening examinations in 26 489 patients (Fig 1). The mean age at examination was 53.9 years ± 9.0 (SD). The distribution of race and ethnicity categories is shown in Table 1. Approximately 20% (7030 of 34 791) of the examinations were performed in patients with a first-degree family history of breast cancer, 84.6% (29 436 of 34 791) in patients with heterogeneously dense breasts, and 15.4% (5355 of 34 791) in patients with extremely dense breasts, and more than half (51.3%; 17 854 of 34 791) in patients with normal body mass index (Table 1). A total of 94.6% (32 905 of 34 791) of the examinations occurred on the same day or within 6 months of previous mammography, and 73.9% (25 719 of 34 791) of the examinations were the patient’s first screening US (Table 2). The median time between US screening examinations was 14 months (IQR, 12–20 months). Tables S1 and S2 show examination-level characteristics according to estimated breast cancer risk measures.
Table 1:
Examination-level Sociodemographic and Risk Characteristics
Table 2:
Characteristics of Screening US Examinations
Overall Outcomes
The overall cancer detection rate was 2.0 per 1000 examinations (95% CI: 1.6, 2.4), with a false-positive biopsy recommendation rate of 29.6 per 1000 examinations (95% CI: 22.6, 38.6) and a PPV3 of 6.9% (67 of 975; 95% CI: 5.3, 8.9) (Table 3). There was no evidence for differences in the cancer detection rate (P = .98), false-positive biopsy recommendation rate (P = .07), or PPV3 (P = .35) in early (2014 to 2017) versus late (2018 to 2020) years of the study period (Table S3). Secondary outcomes and results according to age, breast density, screening round, and time since prior mammography are shown in Table S4. Figure 2 shows sample images of breast cancers depicted at supplemental US screening after negative mammographic examinations.
Table 3:
Overall US Screening Outcomes and Breast Cancer Risk Measures
Figure 2:
Sample images of cancer detection at supplemental US screening after screening mammography with a negative result. (A) Craniocaudal right breast screening mammogram with negative findings in a 54-year-old female patient with extremely dense breast tissue. (B) Coronal view from supplemental screening with automated whole-breast US image in same patient obtained 4 months later shows an irregular hypoechoic mass (dashed yellow circle) in the right breast, diagnosed as invasive ductal carcinoma. (C) Craniocaudal screening right breast mammogram with negative findings in a 74-year-old patient with heterogeneously dense breast tissue. (D) Antiradial gray-scale image in the right breast from supplemental handheld screening US in same 74-year-old patient 7 months later demonstrates an irregular hypoechoic mass in the right breast (yellow outline), which yielded a diagnosis of invasive ductal carcinoma.
Outcomes by 6-year Advanced Breast Cancer Risk
The cancer detection rate, false-positive biopsy recommendation rate, and PPV3 increased with increasing 6-year advanced breast cancer risk (Table 3, Fig 3A). The cancer detection rate was higher in patients with high 6-year advanced breast cancer risk compared with patients with low or average risk (5.5 per 1000 [95% CI: 3.5, 8.6] vs 1.3 per 1000 [95% CI: 1.0, 1.8], respectively; P = .003). The false-positive biopsy recommendation rate was higher in patients with high 6-year advanced breast cancer risk compared with patients with low or average risk (37.0 per 1000 [95% CI: 28.2, 48.4] vs 28.1 per 1000 [95% CI: 20.9, 37.8], respectively; P = .04). PPV3 was higher in patients with high 6-year advanced breast cancer risk compared with patients with low or average risk (15.0% [15 of 100; 95% CI: 9.9, 22.2] vs 4.9% [30 of 615; 95% CI: 3.3, 7.2], respectively; P = .01).
Figure 3:
Cancer detection rates and false-positive biopsy recommendation rates for (A) the 6-year advanced breast cancer risk groups and (B) the 5-year invasive breast cancer risk groups. Error bars indicate 95% CIs. Exams = examinations.
Outcomes by 5-year Invasive Breast Cancer Risk
The cancer detection rate, false-positive biopsy recommendation rate, and PPV3 increased with 5-year invasive breast cancer risk (Table 3, Fig 3B). The cancer detection rate was 3.7 per 1000 (95% CI: 2.4, 5.7) in patients with high 5-year invasive breast cancer risk and 1.4 per 1000 (95% CI: 1.0, 1.9) in patients with low or average risk (P = .02). The false-positive biopsy recommendation rate was higher in patients with high 6-year advanced breast cancer risk compared with patients with low or average risk (36.1 per 1000 [95% CI: 27.6, 47.2] vs 29.5 per 1000 [95% CI: 22.0, 39.5], respectively; P = .02). PPV3 was higher among patients with a high 5-year invasive breast cancer risk compared with patients with a low or average risk (10.5% [22 of 210; 95% CI: 6.2, 17.2] vs 4.9% [26 of 528; 95% CI: 3.2, 7.5], respectively; P = .09).
Cancer Characteristics
Among the 68 US screen-detected cancers, 13 (19%) were classified as ductal carcinoma in situ and 55 (81%) were classified as invasive carcinoma. Among the invasive cancers, 39 (71%) were node-negative, seven (13%) were node-positive, and nine (16%) were missing nodal status (Table 4). The median tumor size for invasive cancers was 11 mm (range, 3 − 48 mm). Screen-detected cancer characteristics by risk groups are in Tables S5 and S6.
Table 4:
Characteristics of Cancers Detected at Screening US
Discussion
The objective of our study was to evaluate US screening outcomes for patients with dense breasts according to different estimated breast cancer risk levels. Our results demonstrated that the cancer detection rate and positive predictive value of biopsies performed (PPV3) were higher in patients with high advanced breast cancer risk than in those with low or average risk (cancer detection rate, 5.5 vs 1.3 per 1000 examinations, respectively [P = .003]; PPV3, 15.0% vs 4.9%, respectively [P = .001]). Similar patterns were observed according to 5-year invasive breast cancer risk.
Our finding of an increased cancer detection rate for screening US with increasing breast cancer risk levels is consistent with observations from separate randomized trials performed in study samples with different levels of breast cancer risk. The seminal American College of Radiology Imaging Network (known as ACRIN) 6666 trial examined the performance of supplemental US and MRI screening in 2809 patients with dense breasts and a personal history of breast cancer or other strong risk factors (7,25). The trial revealed that supplemental US screening increased the cancer detection rate compared with mammography alone, adding 5.3 per 1000 patients on the first screening round and 3.7 per 1000 patients on subsequent screening rounds (7). These findings were similar in magnitude to the cancer detection rates we observed among patients classified as high-risk using BCSC risk models. A recent trial (26) including 6179 participants with dense breasts from the general screening population showed an estimated supplemental US screening incremental cancer detection rate of 1.3 per 1000 participants at the first screening round and 1.0 per 1000 participants at subsequent screening rounds, which is consistent with our findings for low- or average-risk patients. Differences in US screening performance by risk group may be due in part to differences in both the cancer prevalence in each group and the visibility of those cancers at US.
Our overall US screening performance results are consistent with data reported in prior observational studies. A series of studies (27–30) with up to 13 500 screening US examinations reported incremental cancer detection rates of two to four cancers per 1000 in women with dense breasts after a normal mammographic examination. The PPV3 increased from 7% to 20% during 4 years of clinical practice. In a previous observational BCSC study of 6081 screening US examinations conducted during 2000–2013, the combination of screening US and mammography was associated with a 14% increase in cancer detection compared with mammography alone but more than doubled the number of false-positive biopsy recommendations (31). Our current study shows that the tradeoff between cancer detection and false-positive biopsy recommendations is improved among women with a high risk of invasive or advanced cancer.
Our results provide evidence from a large, diverse study sample from 24 facilities and represent US screening performance with a broad range of conditions occurring in community clinical practices, including variations in radiologist and technologist experience, facility volumes, and timing in relation to mammography. Mammography screening performance metrics in the BCSC were recently reported, with a cancer detection rate of 5.8 per 1000 for digital breast tomosynthesis and 5.3 per 1000 for digital mammography (32). The cancer detection rate of mammography screening at BCSC facilities has been shown to increase with age and breast density (22), with interval cancer rates exceeding 1 per 1000 for women with dense breasts and high invasive breast cancer risk (1). Thus, the US screening cancer detection rate of 2.0 per 1000 examinations observed in our study represents a substantial increase in cancer detection within a population undergoing regular mammography.
Our study was observational and had limitations. First, the data represented clinical practice, in which US protocols varied across facilities. These variations included factors such as whether a technologist or radiologist performed the imaging, the training and experience of the technologist and radiologist, the use of handheld versus automated whole-breast US technologies, and the specific technologies used in the diagnostic workup of US examinations with positive results. Thus, our observed results reflect population averages. Screening outcomes at any facility may vary depending on the conditions, and further research is needed to examine variation in US screening outcomes in relation to specific protocols, equipment, and provider training. Second, we imposed an arbitrary facility-level minimum volume threshold of 50 screening US examinations for inclusion in the study. This was intended to facilitate a representative sample of facilities performing regular US screening, including low-volume facilities while minimizing the inclusion of facilities performing US screening only sporadically. Screening outcomes may differ based on screening volume. Third, we did not evaluate screening sensitivity and specificity because complete capture of false-negative screenings for 1 year of cancer follow-up from regional or statewide cancer registries was not yet available for the full study sample. Fourth, the BCSC risk models consider a limited set of available risk factors; nonetheless, these models are well-calibrated and validated (14,33). The BCSC invasive risk model is recommended by the U.S. Preventive Services Task Force for determining risk for primary prevention, and the BCSC advanced cancer risk model can be used to identify those at high risk of advanced cancer undergoing annual screening who should consider supplemental US or MRI screening. It is unclear how frequently BCSC risk models are used in clinical practice. Finally, we did not evaluate other existing breast cancer risk prediction models or address lifetime risk.
In conclusion, our registry-based study indicates that the US screening cancer detection rate and positive predictive value of biopsies performed were greater among women with dense breasts who had high 6-year advanced cancer risk or 5-year invasive breast cancer risk than in those who were classified as having low or average risk. The results of this study may provide useful insights to women, clinicians, and policymakers considering supplemental US screening strategies in the United States, as well as other countries where national screening programs have implemented US as a supplement to mammography for women with dense breasts (6). Screening strategies that target supplemental US based on advanced or invasive breast cancer risk may be expected to yield a high rate of cancers while limiting false-positive biopsy recommendations. Future research is needed to estimate the population-level impact of risk-based US screening strategies on cancer detection, stage shift, breast cancer mortality, and costs, and to directly compare the effectiveness of US with other supplemental screening modalities such as MRI.
Acknowledgments
Acknowledgments
We thank the participating women, mammography facilities, and radiologists for the data they provided for this study. You can learn more about the Breast Cancer Surveillance Consortium at http://www.bcsc-research.org/.
Study supported by the National Cancer Institute (R01CA248068). Data collection for this research was additionally supported by the Breast Cancer Surveillance Consortium with funding from the National Cancer Institute (P01CA154292), a Patient-Centered Outcomes Research Institute (PCORI) Program Award (PCS-1504-30370), the Agency for Healthcare Research and Quality (R01 HS018366-01A1), and the National Institute for General Medical Sciences (U54GM115516). The collection of cancer and vital status data was supported in part by several state public health departments and cancer registries throughout the United States (https://www.bcsc-research.org/work/acknowledgement). The sponsors had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication. The statements presented in this work are solely the responsibility of the authors and do not necessarily represent the official views of PCORI, its Board of Governors or Methodology Committee, the National Cancer Institute, or the National Institutes of Health.
Disclosures of conflicts of interest: B.L.S. No relevant relationships. L.I. No relevant relationships. J.E. No relevant relationships. K.P.L. Research grant from American Cancer Society. G.H.R. No relevant relationships. E.S.O. Grant to institution from the Australia Department of Health. D.L.M. Royalties from Elsevier. J.M.L. No relevant relationships. N.K.S. No relevant relationships. S.D.H. No relevant relationships. H.P. No relevant relationships. D.L.W. Royalties from UpToDate; consulting fees for medical/legal review of diagnostic pathology material and reports. K.K. No relevant relationships.
Abbreviations:
- BCSC
- Breast Cancer Surveillance Consortium
- BI-RADS
- Breast Imaging Reporting and Data System
- PPV3
- positive predictive value of biopsies performed
References
- 1. Kerlikowske K , Zhu W , Tosteson AN , et al. ; Breast Cancer Surveillance Consortium . Identifying women with dense breasts at high risk for interval cancer: a cohort study . Ann Intern Med 2015. ; 162 ( 10 ): 673 – 681 . [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2. McCormack VA , dos Santos Silva I . Breast density and parenchymal patterns as markers of breast cancer risk: a meta-analysis . Cancer Epidemiol Biomarkers Prev 2006. ; 15 ( 6 ): 1159 – 1169 . [DOI] [PubMed] [Google Scholar]
- 3. Sprague BL , Kerlikowske K , Bowles EJA , et al . Trends in Clinical Breast Density Assessment From the Breast Cancer Surveillance Consortium . J Natl Cancer Inst 2019. ; 111 ( 6 ): 629 – 632 . [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4. Berg WA , Seitzman RL , Pushkin J . Implementing the National Dense Breast Reporting Standard, Expanding Supplemental Screening Using Current Guidelines, and the Proposed Find It Early Act . J Breast Imaging 2023. ; 5 ( 6 ): 712 – 723 . [DOI] [PubMed] [Google Scholar]
- 5. Huang S , Houssami N , Brennan M , Nickel B . The impact of mandatory mammographic breast density notification on supplemental screening practice in the United States: a systematic review . Breast Cancer Res Treat 2021. ; 187 ( 1 ): 11 – 30 . [DOI] [PubMed] [Google Scholar]
- 6. Mizzi D , Allely C , Zarb F , et al . Examining the effectiveness of supplementary imaging modalities for breast cancer screening in women with dense breasts: A systematic review and meta-analysis . Eur J Radiol 2022. ; 154 : 110416 . [DOI] [PubMed] [Google Scholar]
- 7. Berg WA , Zhang Z , Lehrer D , et al. ; ACRIN 6666 Investigators . Detection of breast cancer with addition of annual screening ultrasound or a single screening MRI to mammography in women with elevated breast cancer risk . JAMA 2012. ; 307 ( 13 ): 1394 – 1404 . [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8. Ohuchi N , Suzuki A , Sobue T , et al. ; J-START investigator groups . Sensitivity and specificity of mammography and adjunctive ultrasonography to screen for breast cancer in the Japan Strategic Anti-cancer Randomized Trial (J-START): a randomised controlled trial . Lancet 2016. ; 387 ( 10016 ): 341 – 348 . [DOI] [PubMed] [Google Scholar]
- 9. Monticciolo DL , Newell MS , Moy L , Lee CS , Destounis SV . Breast Cancer Screening for Women at Higher-Than-Average Risk: Updated Recommendations From the ACR . J Am Coll Radiol 2023. ; 20 ( 9 ): 902 – 914 . [DOI] [PubMed] [Google Scholar]
- 10. National Comprehensive Cancer Network . NCCN Clinical Practice Guidelines in Oncology: Breast Cancer Screening and Diagnosis V.1.2023 . www.nccn.org/professionals/physician_gls/PDF/breast-screening.pdf. Published 2023. Accessed October 2, 2023 . [DOI] [PubMed]
- 11. Weinstein SP , Slanetz PJ , Lewin AA , et al. ; Expert Panel on Breast Imaging . ACR Appropriateness Criteria® Supplemental Breast Cancer Screening Based on Breast Density . J Am Coll Radiol 2021. ; 18 ( 11S ): S456 – S473 . [DOI] [PubMed] [Google Scholar]
- 12. Nicholson WK , Silverstein M , Wong JB , et al. ; US Preventive Services Task Force . Screening for Breast Cancer: US Preventive Services Task Force Recommendation Statement . JAMA 2024. . 10.1001/jama.2024.5534. Published online April 30, 2024 . [DOI] [PubMed] [Google Scholar]
- 13. Kerlikowske K , Sprague BL , Tosteson ANA , et al . Strategies to Identify Women at High Risk of Advanced Breast Cancer During Routine Screening for Discussion of Supplemental Imaging . JAMA Intern Med 2019. ; 179 ( 9 ): 1230 – 1239 . [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14. Kerlikowske K , Chen S , Golmakani MK , et al . Cumulative Advanced Breast Cancer Risk Prediction Model Developed in a Screening Mammography Population . J Natl Cancer Inst 2022. ; 114 ( 5 ): 676 – 685 . [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15. Sprague BL , Ichikawa L , Eavey J , et al . Breast cancer risk characteristics of women undergoing whole-breast ultrasound screening versus mammography alone . Cancer 2023. ; 129 ( 16 ): 2456 – 2468 . [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16. American College of Radiology . ACR BI-RADS - Mammography . 5th Edition. ACR BI-RADS Atlas: Breast Imaging Reporting and Data System . Reston, VA: : American College of Radiology; ; 2013. . [Google Scholar]
- 17. Lehman CD , Arao RF , Sprague BL , et al . National Performance Benchmarks for Modern Screening Digital Mammography: Update from the Breast Cancer Surveillance Consortium . Radiology 2017. ; 283 ( 1 ): 49 – 58 . [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18. Harris E . FDA Updates Breast Density Reporting Standards, Other Mammogram Rules . JAMA 2023. ; 329 ( 14 ): 1142 – 1143 . [DOI] [PubMed] [Google Scholar]
- 19. Ray KM , Price ER , Joe BN . Breast density legislation: mandatory disclosure to patients, alternative screening, billing, reimbursement . AJR Am J Roentgenol 2015. ; 204 ( 2 ): 257 – 260 . [DOI] [PubMed] [Google Scholar]
- 20. Tice JA , Miglioretti DL , Li CS , Vachon CM , Gard CC , Kerlikowske K . Breast Density and Benign Breast Disease: Risk Assessment to Identify Women at High Risk of Breast Cancer . J Clin Oncol 2015. ; 33 ( 28 ): 3137 – 3143 . [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21. Kerlikowske K , Bissell MCS , Sprague BL , et al . Advanced Breast Cancer Definitions by Staging System Examined in the Breast Cancer Surveillance Consortium . J Natl Cancer Inst 2021. ; 113 ( 7 ): 909 – 916 . [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22. Lowry KP , Coley RY , Miglioretti DL , et al . Screening Performance of Digital Breast Tomosynthesis vs Digital Mammography in Community Practice by Patient Age, Screening Round, and Breast Density . JAMA Netw Open 2020. ; 3 ( 7 ): e2011792 . [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23. Miglioretti DL , Heagerty PJ . Marginal modeling of multilevel binary data with time-varying covariates . Biostatistics 2004. ; 5 ( 3 ): 381 – 398 . [DOI] [PubMed] [Google Scholar]
- 24. Miglioretti DL , Heagerty PJ . Marginal modeling of nonnested multilevel data using standard software . Am J Epidemiol 2007. ; 165 ( 4 ): 453 – 463 . [DOI] [PubMed] [Google Scholar]
- 25. Berg WA , Blume JD , Cormack JB , et al. ; ACRIN 6666 Investigators . Combined screening with ultrasound and mammography vs mammography alone in women at elevated risk of breast cancer . JAMA 2008. ; 299 ( 18 ): 2151 – 2163 . [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26. Berg WA , Zuley ML , Chang TS , et al . Prospective Multicenter Diagnostic Performance of Technologist-Performed Screening Breast Ultrasound After Tomosynthesis in Women With Dense Breasts (the DBTUST) . J Clin Oncol 2023. ; 41 ( 13 ): 2403 – 2415 . [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27. Parris T , Wakefield D , Frimmer H . Real world performance of screening breast ultrasound following enactment of Connecticut Bill 458 . Breast J 2013. ; 19 ( 1 ): 64 – 70 . [DOI] [PubMed] [Google Scholar]
- 28. Hooley RJ , Greenberg KL , Stackhouse RM , Geisel JL , Butler RS , Philpotts LE . Screening US in patients with mammographically dense breasts: initial experience with Connecticut Public Act 09-41 . Radiology 2012. ; 265 ( 1 ): 59 – 69 . [DOI] [PubMed] [Google Scholar]
- 29. Weigert J , Steenbergen S . The connecticut experiment: the role of ultrasound in the screening of women with dense breasts . Breast J 2012. ; 18 ( 6 ): 517 – 522 . [DOI] [PubMed] [Google Scholar]
- 30. Weigert JM . The Connecticut Experiment; The Third Installment: 4 Years of Screening Women with Dense Breasts with Bilateral Ultrasound . Breast J 2017. ; 23 ( 1 ): 34 – 39 . [DOI] [PubMed] [Google Scholar]
- 31. Lee JM , Arao RF , Sprague BL , et al . Performance of Screening Ultrasonography as an Adjunct to Screening Mammography in Women Across the Spectrum of Breast Cancer Risk . JAMA Intern Med 2019. ; 179 ( 5 ): 658 – 667 . [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32. Lee CI , Abraham L , Miglioretti DL , et al. ; Breast Cancer Surveillance Consortium . National Performance Benchmarks for Screening Digital Breast Tomosynthesis: Update from the Breast Cancer Surveillance Consortium . Radiology 2023. ; 307 ( 4 ): e222499 . [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33. McCarthy AM , Guan Z , Welch M , et al . Performance of Breast Cancer Risk-Assessment Models in a Large Mammography Cohort . J Natl Cancer Inst 2020. ; 112 ( 5 ): 489 – 497 . [DOI] [PMC free article] [PubMed] [Google Scholar]