Background: Estimates of risk for radiation-induced breast cancer from mammography screening have not considered variation in dose exposure or diagnostic work-up after abnormal screening results.

Objective: To estimate distributions of radiation-induced breast cancer incidence and mortality from digital mammography screening while considering exposure from screening and diagnostic mammography and dose variation among women.

Design: 2 simulation-modeling approaches.

Setting: U.S. population.

Patients: Women aged 40 to 74 years.

Intervention: Annual or biennial digital mammography screening from age 40, 45, or 50 years until age 74 years.

Measurements: Lifetime breast cancer deaths averted (benefits) and radiation-induced breast cancer incidence and mortality (harms) per 100,000 women screened.

Results: Annual screening of 100,000 women aged 40 to 74 years was projected to induce 125 breast cancer cases (95% CI, 88 to 178) leading to 16 deaths (CI, 11 to 23), relative to 968 breast cancer deaths averted by early detection from screening. Women exposed at the 95th percentile were projected to develop 246 cases of radiation-induced breast cancer leading to 32 deaths per 100 000 women. Women with large breasts requiring extra views for complete examination (8% of population) were projected to have greater radiation-induced breast cancer risk (266 cancer cases and 35 deaths per 100,000 women) than other women (113 cancer cases and 15 deaths per 100,000 women). Biennial screening starting at age 50 years reduced risk for radiation-induced cancer 5-fold.

Limitation: Life-years lost from radiation-induced breast cancer could not be estimated.

Conclusion: Radiation-induced breast cancer incidence and mortality from digital mammography screening are affected by dose variability from screening, resultant diagnostic work-up, initiation age, and screening frequency. Women with large breasts may have a greater risk for radiation-induced breast cancer.

Primary Funding Source: Agency for Healthcare Research and Quality, U.S. Preventive Services Task Force, National Cancer Institute.

Exposure to ionizing radiation from repeated mammography examinations may increase breast cancer risk.^{1, 2} Radiation-induced breast cancer incidence and mortality associated with recommended screening strategies are suggested to be low relative to breast cancer deaths prevented.^3–5 However, prior projected population risks were based on exposure from screening only and assumed only 4 standard views per screening examination at the mean radiation dose. Evaluations of screening programs should consider full episodes of care, including diagnostic work-up prompted by an abnormal screening result.⁶ False-positive recalls, breast biopsies, and short-interval follow-up examinations are relatively common in the United States and add radiation exposure from diagnostic mammography.⁷ Some subgroups of women, such as obese women and those with dense breasts, are more likely to have additional evaluations,^7–9 which may increase their risk for radiation-induced cancer.

When risk for radiation-induced breast cancer is being evaluated, it may also be important to consider variation in radiation dose from a single examination. Examinations vary in the number of views performed and dose per view; therefore, some women receive more than the mean dose. The American College of Radiology Imaging Network DMIST (Digital Mammographic Imaging Screening Trial) found an average radiation dose of 1.86 mGy to the breast from a single digital mammography screening view,¹⁰ but dose per view varied from 0.15 to 13.4 mGy (Supplement, available at www.annals.org), and 21% of digital screening examinations used more than 4 views.¹⁰ Radiation dose is strongly correlated with compressed breast thickness; thus, women with large breasts tend to receive greater doses per view and may require more than 4 views for complete examination.^{10, 11}

Women with breast augmentation receive implant-displacement views in addition to standard screening views, which doubles their radiation dose.¹² Women may have repeated views because of movement artifacts or improper breast positioning.

We estimated the distribution of cumulative radiation dose and associated breast cancer risk from full screening episodes to identify subgroups of women who may have a greater risk for radiation-induced cancer because they have factors contributing to greater doses per examination or frequent false-positive screening results that lead to additional radiation exposure from subsequent diagnostic work-up. Using population-based data from the Breast Cancer Surveillance Consortium (BCSC)¹³, we estimated the probability of a false-positive screening result followed by additional imaging evaluation, short-interval follow-up, or biopsy. We used data from the BCSC, DMIST, and other sources in 2 simulation models to estimate radiation exposure and radiation-induced breast cancer incidence and mortality associated with 8 potential screening strategies with different starting ages (40, 45, or 50 years) and screening intervals (annual, biennial, or a hybrid strategy).

Screening Strategies

We used 2 complementary stochastic modeling approaches to evaluate the following 8 strategies for screening with digital mammography: annual screening from age 40 to 74, 45 to 74, or 50 to 74 years; biennial screening from age 40 to 74, 45 to 74, or 50 to 74 years; or a hybrid strategy of annual screening from age 40 to 49 or 45 to 49 years followed by biennial screening from age 50 to 74 years.

We included the hybrid strategies because more frequent screening has been advocated for younger and premenopausal women due to their greater prevalence of dense breasts and more aggressive tumors, resulting in a greater risk for interval cancer, than older women.^14–17 Outcomes were breast cancer deaths averted (benefits) and radiation-induced breast cancer incidence and mortality (harms) associated with a lifetime of mammography screening relative to no screening.

Simulation-Modeling Approaches

Figure 1 summarizes our approach. We used 2 complementary stochastic modeling approaches to simulate mammography events associated with radiation exposure and outcomes for a population adherent with each of the 8 screening strategies. The first approach used the Microsimulation of Screening Analysis–Fatal Diameter (MISCAN-Fadia) model,¹⁸ which is a detailed natural history model of breast cancer. This approach provided estimates of breast cancer incidence and mortality with and without screening to contextualize estimates of radiation-induced breast cancer cases. Although MISCAN-Fadia models the average effects of screening on a population level, it does not model correlation among repeated mammography results in individual women or the specific types of work-up after an abnormal screening result; thus, it cannot be used to estimate the distribution of cumulative radiation exposure from both screening mammography and subsequent diagnostic work-up among women. Therefore, we developed a new simulation model that provides woman-level exposure histories that were not available from the MISCAN-Fadia model. This new model captures exposure heterogeneity by simulating mammography results and subsequent work-up in each woman and allowing for variability in radiation exposure and breast size.

MISCAN-Fadia Model

The MISCAN-Fadia model simulates individual life histories of women with and without breast cancer in the presence and absence of screening from birth to death from breast cancer or other causes. The model has been described in detail elsewhere,¹⁸ information about the model can be found online (http://cisnet.cancer.gov), and inputs and assumptions are described in our report for the draft U.S. Preventive Services Task Force recommendations.¹⁹ In brief, on the basis of BCSC data on sensitivity of digital mammography screening, cancer detection rates, and cancer stage at detection, we estimated thresholds at which tumors become screen-detectable. Screening sensitivity and specificity depended on age, breast density, and screening interval. Breast cancer risk depended on age and breast density. The effect of screening on breast cancer natural history was assessed by modeling continuous tumor growth, in which tumors detected before they reached their fatal diameter were cured and those detected past their fatal diameter led to breast cancer death. We assumed that all women received the mean dose per screening examination and, if recalled, the mean dose associated with diagnostic work-up after a false-positive screening result, both of which were estimated from the radiation exposure model. We also projected breast cancer incidence and mortality with and without screening.

Radiation Exposure Simulation Model

Full details, including approach, data sources, and assumptions, are available in the Supplement. In brief, for each of the 8 screening strategies, we simulated woman-level factors and screening-related events for 100,000 women.

Woman-Level Factors. Each woman was assigned a compressed breast thickness from the DMIST distribution (Appendix Table 1). Women with a compressed breast thickness of 7.5 cm or greater (8% of DMIST population) were assumed to have large breasts that required extra views for complete examination. On the basis of distributions seen in the BCSC, each woman was assigned a baseline Breast Imaging Reporting and Data System¹² density at the start of screening, which could potentially decrease by 1 category at ages 50 and 65 years²⁰ (Appendix Table 2).

Evaluation of a Positive Screening Result. For each screening strategy, we simulated events after a positive screening result that did not lead to a diagnosis of breast cancer (Figure 2) to focus on risk for first breast cancer induced by radiation. We modeled the probability of each event by using data from digital mammography done at BCSC facilities from 2003 to 2011 on women aged 40 to 74 years without a history of breast cancer or cancer diagnosed within 1 year after the examination. At each screening, a woman's probability of recall for additional imaging was based on age, breast density, screening interval, prior screening results, and a woman-specific random effect. If recalled, the probability of referral to biopsy, short-interval follow-up, or return to routine screening was based on age, breast density, and screening interval.

Radiation Dose. For each screening and diagnostic event, we sampled the number of screening mammography views from the DMIST distribution (Appendix Table 3) and number of views for diagnostic work-up on the basis of expert opinion, conditional on compressed breast thickness (Appendix Table 4). We assumed different distributions of views for women with and without large breasts. We randomly sampled the radiation dose per view on the basis of the DMIST distribution conditional on the woman's compressed breast thickness (Appendix Figure). For each age, we calculated total breast-level dose by multiplying half the number of views of both breasts by the dose per view. We report the mean and the 5th, 25th, 75th, and 95th percentiles (to quantify exposure leading to increased risk for radiation-induced breast cancer) for the number of mammography views and associated dose from each screening examination and all follow-up mammograms within 1 year of a screening examination (Appendix Table 5).

Radiation-Induced Breast Cancer Incidence and Mortality

We estimated radiation-induced breast cancer incidence by using the excess absolute risk model from pooled analysis of 4 cohorts by Preston and colleagues,¹ the preferred model for estimating radiation-induced breast cancer incidence.^{2, 21} Details are provided in the Supplement. Women in these cohorts received cumulative radiation doses of 20 mGy or greater. This level of cumulative radiation exposure is reached after 2 to 4 years of mammography screening and diagnostic work-up (Appendix Table 5). This model assumes that excess risk for radiation-induced breast cancer increases linearly with increasing radiation dose within the exposure ranges from mammography.

In addition, risk decreases with increasing age at exposure, especially after age 50 years (a surrogate for menopause), and increases with age; the highest incidence of radiation-induced breast cancer occurs late in life. We modeled the latency period for developing radiation-induced breast cancer by using a logistic function that phases in increased breast cancer risk between 4 and 11 years after exposure.²¹ We estimated radiation-induced breast cancer mortality by multiplying radiation-induced breast cancer incidence by the age-specific case–fatality rates of non–radiation-induced breast cancer derived from MISCAN-Fadia and assuming 100% adherence to screening and available treatment. We assumed that breast cancer induced by radiation is screen-detected at the same rate as noninduced cancer. We approximated CIs by reestimating risk for radiation-induced breast cancer by using the upper and lower 95% CIs for the risk coefficient, β, because this uncertainty dominates the uncertainty in estimated risk.^{2, 21}

The MISCAN-Fadia model was programmed in Delphi (Borland). All other analyses were done in R, version 3.1.0 (R Foundation for Statistical Computing), and SAS, version 9.4 (SAS Institute).

Role of the Funding Source

This study was funded by the Agency for Healthcare Research and Quality under a contract to support the work of the U.S. Preventive Services Task Force and by the National Cancer Institute. Investigators worked with Task Force members and Agency staff to develop the scope, analytic framework, and key questions. The funding source had no role in model input selection, data synthesis, or data analysis. Agency staff provided project oversight and reviewed the report to ensure that the analysis met methodological standards. The authors are solely responsible for the content and the decision to submit the manuscript for publication.

Radiation Exposure

Most radiation exposure from screening and subsequent diagnostic work-up was due to the screening examination (Appendix Table 5). Diagnostic work-up accounted for only 10% of the mean annual radiation dose but 24% of the dose for women with exposure at the 95th percentile. On average, women with large breasts were exposed to 2.3 times more radiation than those with small or average-sized breasts.

Radiation-Induced Breast Cancer Incidence and Breast Cancer Death

Risk estimates corresponding to mean exposures were similar for the 2 modeling approaches (Table 1); therefore, we focus on results from the radiation exposure model. We projected that annual screening and diagnostic work-up of 100,000 women aged 40 to 74 years (35 screening examinations per woman) would induce an average of 125 breast cancer cases (95% CI, 88 to 178), resulting in 16 deaths (CI, 11 to 23) (Table 1). Risk projections varied widely, with 100,000 women exposed at the 5th percentile projected to develop 64 radiation-induced cancer cases (CI, 44 to 90), resulting in 8 deaths (CI, 6 to 12), and 100,000 women exposed at the 95th percentile projected to develop 246 radiation-induced cases of cancer (CI, 171 to 349), resulting in 32 deaths (CI, 22 to 45). Women with large breasts requiring extra views for complete examination had more than twice as many cases of radiation-induced breast cancer (mean, 266 cases [CI, 186 to 380]) and breast cancer deaths (mean, 35 deaths [CI, 24 to 50]) than women with small or average-sized breasts (113 breast cancer cases [CI, 79 to 161] and 15 breast cancer deaths [CI, 10 to 21]) (Table 2).

Starting screening at age 50 years and following a biennial strategy (13 screening examinations) greatly reduced risk for radiation-induced breast cancer and breast cancer death (Table 1). Compared with annual screening from age 40 to 74 years, biennial screening from age 50 to 74 years was projected to cause approximately one fifth of the radiation-induced breast cancer cases (mean, 125 cases [CI, 88 to 178] vs. 27 cases [CI, 19 to 38] per 100,000 women, respectively, and 266 cases [CI, 186 to 380] vs. 57 cases [CI, 40 to 82] per 100,000 women with large breasts) (Table 2).

Breast Cancer Deaths Averted per Radiation-Induced Case of Breast Cancer

From the MISCAN-Fadia model, we projected that 16 947 breast cancer cases would be diagnosed from age 40 years through death per 100,000 women screened annually from age 40 to 74 years (data not shown). The number of breast cancer deaths averted ranged from 627 per 100,000 women screened biennially from age 50 to 74 years to 968 per 100,000 women screened annually from age 40 to 74 years (Table 3). For biennial screening from age 50 to 74 years, we projected a mean of 23 breast cancer deaths averted for each radiation-induced case of breast cancer (CI, 16 to 33) (5th percentile, 48; 95th percentile, 11) and 140 breast cancer deaths averted for each radiation-induced breast cancer death (CI, 98 to 199) (5th percentile, 289; 95th percentile, 68). For annual screening from age 40 to 74 years, these ratios were lower, at 8 breast cancer deaths averted per radiation-induced case of breast cancer (CI, 5 to 11) (5th percentile, 15; 95th percentile, 4) and 59 breast cancer deaths averted per radiation-induced breast cancer death among all women (CI, 42 to 85) (5th percentile, 117; 95th percentile, 30). For annual screening from age 40 to 74 years of women with large breasts, ratios were even lower, at 4 breast cancer deaths averted per radiation-induced case of breast cancer (CI, 3 to 5) and 28 per radiation-induced breast cancer death (CI, 20 to 40).

We improved previous estimates of the potential harms from radiation exposure of screening strategies for breast cancer by using methods that more fully represent the experience of women who have routine digital screening mammography. Our models included radiation exposure from diagnostic evaluations prompted by abnormal screening results and incorporated variation in dose at each screening and diagnostic examination. In addition to the mean, we reported the 5th and 95th percentiles of the population distribution to highlight that some women have risk that is substantially lower or higher than average because of variation in radiation exposure. Most of the increased risk was due to screening examinations with more than 4 views and higher-than-average doses per view. We used DMIST data to model the number of views per screening examination and to incorporate the increased radiation dose per view for thicker compressed breasts. However, even for a given compressed breast thickness, some women received greater doses than others, which was probably due to greater breast density that required more radiation for penetration. Because women with large breasts may require more views per examination and tend to receive a greater dose per view, breast size was an important factor in determining radiation exposure and associated risk. Another reason for greater radiation exposure is false-positive results; additional imaging performed to work up false-positive results accounted for one fourth of the radiation dose received by women at the 95th percentile compared with only one tenth of the radiation dose received by women at the mean.

Relative to a projected 16,947 breast cancer cases diagnosed per 100,000 women aged 40 years or older with annual screening, we estimate that the number of breast cancer cases induced by screening is probably very small, even for women with the greatest radiation exposures. However, relative to the number of breast cancer deaths averted with screening, radiation-induced breast cancer incidence is not trivial. Most concerning are numbers projected for annual screening and screening before age 50 years of women with large breasts requiring extra views for complete examination, who have more than twice the risk for radiation-induced breast cancer as women with small or average-sized breasts. Although we did not model this explicitly, women with breast augmentation should also have twice the risk for radiation-induced breast cancer because they receive implant-displacement views in addition to standard screening views, resulting in a minimum of 8 views per examination compared with the standard 4 views.¹²

The benefit–harm ratio in terms of breast cancer deaths averted per radiation-induced case of breast cancer could be improved by initiating screening at age 50 years instead of 40 years, thereby reducing risk for radiation-induced breast cancer by 60%, or by using biennial screening, which would cut the risk in half compared with annual screening. Doing both (screening biennially from age 50 to 74 years) would reduce the risk almost 5-fold compared with annual screening from age 40 to 74 years. Several steps should be taken to further improve the benefit–harm ratio. Current efforts to reduce the radiation dose per view should continue. Radiology staff should strive to minimize the number of additional views performed and to reduce false-positive rates, which are much higher in the United States than many other countries, suggesting room for improvement.^22–25 Radiation doses from diagnostic mammography could be avoided for certain screen-detected masses amenable to ultrasonography work-up alone. In addition, facilities should ensure that large breasts are imaged using larger detector sizes to minimize the need for extra views for complete examination.

Hendrick³ also estimated incidence and mortality of radiation-induced breast cancer using DMIST data but used the mean dose for 4 views without accounting for additional radiation exposure from additional screening views received by 21% of women or from diagnostic follow-up imaging. He projected that annual screening of 100,000 women from age 40 to 80 years with an examination-level dose of 3.7 mGy would induce 72 breast cancer cases leading to 20 deaths. For women screened annually from age 40 to 74 years, we estimated fewer breast cancer deaths (16 deaths per 100,000 women), despite more radiation-induced breast cancer cases (125 cases per 100,000 women), because we optimistically assumed 100% adherence to the screening regimen and use of available treatments. In particular, we assumed that 10% to 19% of women diagnosed with breast cancer between ages 40 and 74 years would die of the disease (depending on the screening scenario) compared with recent estimates of more than 23%.²⁶ Thus, we may have underestimated the number of radiation-induced breast cancer deaths. Yaffe and Mainprize⁴ projected that screening 100,000 women annually from age 40 to 55 years and biennially thereafter to age 74 years with a dose of 3.7 mGy would induce 86 breast cancer cases and 11 deaths. In comparison, we projected that screening 100,000 women annually from age 40 to 49 years and biennially thereafter to age 74 years would induce 89 breast cancer cases and 15 deaths. Our estimates are probably greater because we accounted for some screening examinations having more than 4 views and for radiation exposure from diagnostic work-up.

Doses from current digital mammography systems may be lower than doses from older DMIST units. Nevertheless, DMIST doses may still be conservative because, similar to most prior studies, dose estimates assumed breast compositions of 50% glandular tissue, which probably underestimates doses by 8% to 18%.^{27, 28} Although Mammography Quality Standards Act inspections suggest that doses for a digital mammography view decreased 2.5% between 2007 and 2009,²⁹ these doses were measured with phantoms simulating breasts with a compressed breast thickness at the 30th percentile in DMIST. Radiation dose is highly correlated with compressed breast thickness, which may increase over time with increasing population body mass index.³⁰

The use of digital breast tomosynthesis for screening is increasing in the United States.³¹ Doses from breast tomosynthesis vary by strategy; however, the 3-dimensional acquisition generally uses a radiation dose similar to or slightly greater than standard digital mammography.^{28, 32, 33} Most U.S. practices offering screening tomosynthesis combine it with digital mammography, which at least doubles doses and the risk for radiation-induced breast cancer. Software approved by the U.S. Food and Drug Administration to generate synthetic 2-dimensional views from tomosynthesis acquisitions will probably eliminate the need for standard digital mammography views and their associated radiation exposure;³⁴ however, the rate at which this software will diffuse into clinical practice is unknown. Estimating radiation-induced cancer risks associated with tomosynthesis screening is further complicated by the expectation that this method will decrease recall rates and potentially eliminate the need for diagnostic mammography to work up some imaging findings.^35–41

Our study had several limitations. We had inadequate information on the percentage of women requiring more than 4 views for complete breast examination. In DMIST, 21% of women required more than 4 screening views,¹⁰ although most received only 1 or 2 extra views, probably because of patient movement or poor positioning. On the basis of the observed distribution of compressed breast thickness and number of views, we assumed that 8% of women received extra views because they had large breasts. Of note, the early-generation mammography systems used in DMIST had smaller image detectors.¹⁰ Most modern units have larger detectors; therefore, the percentage of women requiring extra views because of large breast size is probably less than 8%.

We could not calculate life-years lost due to radiation-induced breast cancer, which may occur later in life than deaths prevented from screening. Because of lack of data, we did not model the association between breast size and the probability of a false-positive result; thus, we may have underestimated exposure from additional work-up in women with large breasts because obese women may be 20% more likely than normal-weight women to have false-positive results.⁹ We also assumed that the number of breast cancer deaths averted with screening did not vary by breast size; however, screening may prevent more deaths among postmenopausal obese women (who tend to have large breasts) because they have a greater risk for advanced disease.⁴² In addition, we did not model the association between breast density and radiation dose per view because of lack of representative data. Probabilities for events after screening mammography were based on point estimates from models that used the best available data and did not account for uncertainty due to model misspecification or inherent variability in parameter estimates. We could not estimate 95% CIs for deaths averted with screening because of the computational complexity of the MISCAN-Fadia model and because many input parameters of the model (such as tumor growth rate) are unobservable and therefore have unknown distributions. We also made several simplifying assumptions (Supplement).

In conclusion, population projections of radiation-induced breast cancer incidence and mortality from mammography screening are affected by variability in doses from screening and resultant diagnostic examinations, age at screening initiation, and screening frequency.

Our study suggests that women with large breasts or breast augmentation receive greater radiation doses and may have a greater risk for radiation-induced breast cancer and breast cancer death. Radiology practices should strive to ensure that large breasts are imaged with large detectors with the fewest number of views possible.

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

Disclaimer: The authors are responsible for study design, analysis and interpretation of the data, writing of the manuscript, and the decision to submit the manuscript for publication. The findings and conclusions in this article are those of the authors, who are responsible for its contents, and do not necessarily represent the views of the funding sources.

Acknowledgment: The authors thank Benjamin Herman, PhD, at the American College of Radiology Imaging Network coordinating center for providing the DMIST data and helping with data interpretation; John Boone, PhD, Professor and Vice Chair of Radiology and Professor of Biomedical Engineering at the University of California, Davis, Medical Center, for helpful input and suggestions on our modeling strategy; Chris Tachibana, PhD, from the Group Health Research Institute for scientific editing; and an anonymous reviewer from the American College of Radiology Imaging Network for his/her comments on an earlier draft.

Grant Support: By the Agency for Healthcare Research and Quality (grant HHSA-290-2012-00015-I), U.S. Preventive Services Task Force, and National Cancer Institute (grants P01CA154292, 5U01CA152958, and R03CA182986). Collection of mammography data was supported by the BCSC, which is funded by the National Cancer Institute (grants P01CA154292, HHSN261201100031C, and U54CA163303). The collection of BCSC data was supported in part by several state public health departments and cancer registries throughout the United States. For a full description of these sources, visit breastscreening.cancer.gov/work/acknowledgement.html. Primary research and data collection for the American College of Radiology Imaging Network DMIST were supported by the National Cancer Institute (grants U01 CA80098, U01 CA80098-S1, U01 CA79778, and U01 79778-S1).

Disclosures: Dr. Miglioretti reports grants from the Agency for Healthcare Research and Quality and National Cancer Institute during the conduct of the study. Dr. Lee reports grants and personal fees from GE Healthcare outside the submitted work. Dr. van Ravesteyn reports grants from the National Cancer Institute, National Institutes of Health, during the conduct of the study. Dr. Melnikow reports other from the Agency for Healthcare Research and Quality during the conduct of the study. Dr. Hubbard reports grants from the National Institutes of Health during the conduct of the study. Authors not named here have disclosed no conflicts of interest. Forms can be viewed at www.acponline.org/authors/icmje/ConflictOfInterestForms.do?msNum=M15-1241.

Reproducible Research Statement: Study protocol: Available from Dr. Miglioretti (e-mail, dmiglioretti@ucdavis.edu). Statistical code: The statistical code for the MISCAN-Fadia model is not available. The other statistical code is available from the BCSC's statistical coordinating center (e-mail, SCC@ghc.org). Data set: The BCSC data set is available with approval of the BCSC Steering Committee.

Requests for Single Reprints: Diana L. Miglioretti, PhD, Department of Public Health Sciences, University of California, Davis School of Medicine, One Shields Avenue, Medical Science Building 1C, Room 144, Davis, CA 95616; e-mail, dmiglioretti@ucdavis.edu.

1. Preston DL, Mattsson A, Holmberg E, Shore R, Hildreth NG, Boice JD Jr. Radiation effects on breast cancer risk: a pooled analysis of eight cohorts. Radiat Res. 2002;158:220-35. [PMID: 12105993]
2. Committee to Assess Health Risks from Exposure to Low Levels of Ionizing Radiation; National Research Council. Health Risks from Exposure to Low Levels of Ionizing Radiation: BEIR VII Phase 2. Washington, DC: National Academies Pr; 2006.
3. Hendrick RE. Radiation doses and cancer risks from breast imaging studies. Radiology. 2010;257:246-53. [PMID: 20736332]
4. Yaffe MJ, Mainprize JG. Risk of radiation-induced breast cancer from mammographic screening. Radiology. 2011;258:98-105. [PMID: 21081671]
5. Feig SA, Hendrick RE. Radiation risk from screening mammography of women aged 40–49 years. J Natl Cancer Inst Monogr. 1997:119-24. [PMID: 9709287]
6. Harris RP, Sheridan SL, Lewis CL, Barclay C, Vu MB, Kistler CE, et al. The harms of screening: a proposed taxonomy and application to lung cancer screening. JAMA Intern Med. 2014;174:281-5. [PMID: 24322781]
7. Hubbard RA, Kerlikowske K, Flowers CI, Yankaskas BC, Zhu W, Miglioretti DL. Cumulative probability of false-positive recall or biopsy recommendation after 10 years of screening mammography: a cohort study. Ann Intern Med. 2011;155:481-92. [PMID: 22007042]
8. Carney PA, Miglioretti DL, Yankaskas BC, Kerlikowske K, Rosenberg R, Rutter CM, et al. Individual and combined effects of age, breast density, and hormone replacement therapy use on the accuracy of screening mammography. Ann Intern Med. 2003;138:168-75. [PMID: 12558355]
9. Elmore JG, Carney PA, Abraham LA, Barlow WE, Egger JR, Fosse JS, et al. The association between obesity and screening mammography accuracy. Arch Intern Med. 2004;164:1140-7. [PMID: 15159273]
10. Hendrick RE, Pisano ED, Averbukh A, Moran C, Berns EA, Yaffe MJ, et al. Comparison of acquisition parameters and breast dose in digital mammography and screen-film mammography in the American College of Radiology Imaging Network digital mammographic imaging screening trial. AJR Am J Roentgenol. 2010;194:362-9. [PMID: 20093597]
11. Wells CL, Slanetz PJ, Rosen MP. Mismatch in breast and detector size during screening and diagnostic mammography results in increased patient radiation dose. Acad Radiol. 2014;21:99-103. [PMID: 24331271]
12. American College of Radiology. American College of Radiology Breast Imaging Reporting and Data System Atlas (ACR BI-RADS Atlas). Reston, VA: American College of Radiology; 2013.
13. Ballard-Barbash R, Taplin SH, Yankaskas BC, Ernster VL, Rosenberg RD, Carney PA, et al. Breast Cancer Surveillance Consortium: a national mammography screening and outcomes database. AJR Am J Roentgenol. 1997;169:1001-8. [PMID: 9308451]
14. Miglioretti DL, Zhu W, Kerlikowske K, Sprague BL, Onega T, Buist DS, et al; Breast Cancer Surveillance Consortium. Breast Tumor Prognostic Characteristics and Biennial vs Annual Mammography, Age, and Menopausal Status. JAMA Oncol. 2015;1:1069-77. [PMID: 26501844]
15. Buist DS, Porter PL, Lehman C, Taplin SH, White E. Factors contributing to mammography failure in women aged 40–49 years. J Natl Cancer Inst. 2004;96:1432-40. [PMID: 15467032]
16. Tabár L, Faberberg G, Day NE, Holmberg L. What is the optimum interval between mammographic screening examinations? An analysis based on the latest results of the Swedish two-county breast cancer screening trial. Br J Cancer. 1987;55:547-51. [PMID: 3606947]
17. Tabar L, Fagerberg G, Chen HH, Duffy SW, Gad A. Tumour development, histology and grade of breast cancers: prognosis and progression. Int J Cancer. 1996;66:413-9. [PMID: 8635853]
18. Tan SY, van Oortmarssen GJ, de Koning HJ, Boer R, Habbema JD. The MISCAN-Fadia continuous tumor growth model for breast cancer. J Natl Cancer Inst Monogr. 2006:56-65. [PMID: 17032895]
19. Mandelblatt J, Cronin K, de Koning H, Miglioretti DL, Schechter C, Stout N. Collaborative Modeling of U.S. Breast Cancer Screening Strategies. AHRQ Publication No. 14-05201-EF-4. Rockville, MD: Agency for Healthcare Research and Quality: 2015. Accessed at https://www.uspreventiveservicestaskforce.org/Page/Document/modeling-report-collaborative-modeling-of-us-breast-cancer-1/breast-cancer-screening1 on 11 April 2019.
20. Sprague BL, Gangnon RE, Burt V, Trentham-Dietz A, Hampton JM, Wellman RD, et al. Prevalence of mammographically dense breasts in the United States. J Natl Cancer Inst. 2014;106. [PMID: 25217577]
21. Berrington de Gonzalez A, Iulian Apostoaei A, Veiga LH, Rajaraman P, Thomas BA, Owen Hoffman F, et al. RadRAT: a radiation risk assessment tool for lifetime cancer risk projection. J Radiol Prot. 2012;32:205-22. [PMID: 22810503]
22. Smith-Bindman R, Chu PW, Miglioretti DL, Sickles EA, Blanks R, Ballard-Barbash R, et al. Comparison of screening mammography in the United States and the United kingdom. JAMA. 2003;290:2129-37. [PMID: 14570948]
23. Kemp Jacobsen K, Abraham L, Buist DS, Hubbard RA, O'Meara ES, Sprague BL, et al. Comparison of cumulative false-positive risk of screening mammography in the United States and Denmark. Cancer Epidemiol. 2015;39:656-63. [PMID: 26013768]
24. Elmore JG, Nakano CY, Koepsell TD, Desnick LM, D'Orsi CJ, Ransohoff DF. International variation in screening mammography interpretations in community-based programs. J Natl Cancer Inst. 2003;95:1384-93. [PMID: 13130114]
25. Hofvind S, Vacek PM, Skelly J, Weaver DL, Geller BM. Comparing screening mammography for early breast cancer detection in Vermont and Norway. J Natl Cancer Inst. 2008;100:1082-91. [PMID: 18664650]
26. American Cancer Society. Cancer Facts & Figure 2015. Atlanta: American Cancer Society; 2015. Accessed at www.cancer.org/Research/CancerFactsFigures/index on 2 September 2015.
27. Yaffe MJ, Boone JM, Packard N, Alonzo-Proulx O, Huang SY, Peressotti CL, et al. The myth of the 50-50 breast. Med Phys. 2009;36:5437-43. [PMID: 20095256]
28. Olgar T, Kahn T, Gosch D. Average glandular dose in digital mammography and breast tomosynthesis. Rofo. 2012;184:911-8. [PMID: 22711250]
29. U.S. Food and Drug Administration. Trends in Mammography Dose and Image Quality 1974–2009. 2015.
30. Robinson M, Kotre CJ. Trends in compressed breast thickness and radiation dose in breast screening mammography. Br J Radiol. 2008;81:214-8. [PMID: 18270295]
31. Hardesty LA, Kreidler SM, Glueck DH. Digital breast tomosynthesis utilization in the United States: a survey of physician members of the Society of Breast Imaging. J Am Coll Radiol. 2014;11:594-9. [PMID: 24713501]
32. Svahn TM, Houssami N, Sechopoulos I, Mattsson S. Review of radiation dose estimates in digital breast tomosynthesis relative to those in two-view full-field digital mammography. Breast. 2015;24:93-9. [PMID: 25554018]
33. Feng SS, Sechopoulos I. Clinical digital breast tomosynthesis system: dosimetric characterization. Radiology. 2012;263:35-42. [PMID: 22332070]
34. Lee CI, Lehman CD. Digital breast tomosynthesis and the challenges of implementing an emerging breast cancer screening technology into clinical practice. J Am Coll Radiol. 2013;10:913-7. [PMID: 24295940]
35. McCarthy AM, Kontos D, Synnestvedt M, Tan KS, Heitjan DF, Schnall M, et al. Screening outcomes following implementation of digital breast tomosynthesis in a general-population screening program. J Natl Cancer Inst. 2014;106. [PMID: 25313245]
36. Rose SL, Tidwell AL, Bujnoch LJ, Kushwaha AC, Nordmann AS, Sexton R Jr. Implementation of breast tomosynthesis in a routine screening practice: an observational study. AJR Am J Roentgenol. 2013;200:1401-8. [PMID: 23701081]
37. Friedewald SM, Rafferty EA, Rose SL, Durand MA, Plecha DM, Greenberg JS, et al. Breast cancer screening using tomosynthesis in combination with digital mammography. JAMA. 2014;311:2499-507. [PMID: 25058084]
38. Skaane P, Bandos AI, Gullien R, Eben EB, Ekseth U, Haakenaasen U, et al. Comparison of digital mammography alone and digital mammography plus tomosynthesis in a population-based screening program. Radiology. 2013;267:47-56. [PMID: 23297332]
39. Ciatto S, Houssami N, Bernardi D, Caumo F, Pellegrini M, Brunelli S, et al. Integration of 3D digital mammography with tomosynthesis for population breast-cancer screening (STORM): a prospective comparison study. Lancet Oncol. 2013;14:583-9. [PMID: 23623721]
40. Haas BM, Kalra V, Geisel J, Raghu M, Durand M, Philpotts LE. Comparison of tomosynthesis plus digital mammography and digital mammography alone for breast cancer screening. Radiology. 2013;269:694-700. [PMID: 23901124]
41. Greenberg JS, Javitt MC, Katzen J, Michael S, Holland AE. Clinical performance metrics of 3D digital breast tomosynthesis compared with 2D digital mammography for breast cancer screening in community practice. AJR Am J Roentgenol. 2014;203:687-93. [PMID: 24918774]
42. Kerlikowske K, Walker R, Miglioretti DL, Desai A, Ballard- Barbash R, Buist DS. Obesity, mammography use and accuracy, and advanced breast cancer risk. J Natl Cancer Inst. 2008;100:1724-33. [PMID: 19033562]

Estimates of the number of screening examinations and false-positive results from the MISCAN-Fadia model were combined with the mean radiation dose from the radiation exposure model to estimate mean incidence of radiation-induced breast cancer. Estimates of the probability distribution of cumulative radiation dose at each age among women from the radiation exposure model were used to estimate the probability distribution of radiation-induced breast cancer incidence. Radiation-induced breast cancer incidence was combined with breast cancer survival estimates from the MISCAN-Fadia model to estimate radiation-induced breast cancer mortality.
BCSC = Breast Cancer Surveillance Consortium; DMIST = Digital Mammographic Imaging Screening Trial; MISCAN-Fadia = Microsimulation of Screening Analysis–Fatal Diameter.

SIFU examinations included unilateral diagnostic views on the recalled breast at 6 mo after the initial SIFU recommendation. The examinations included unilateral diagnostic views on the recalled breast plus bilateral routine screening views at 12 and 24 mo after the initial SIFU recommendation for women who received annual screening and 24 mo after the initial SIFU recommendation for those who received biennial screening. The routine screening views could result in recall for additional imaging to work up a new finding, followed by a recommendation for another SIFU examination or tissue biopsy.
SIFU = short-interval follow-up.

MISCAN-Fadia = Microsimulation of Screening Analysis–Fatal Diameter.
* Values in parentheses are 95% CIs.

* Values in parentheses are 95% CIs.

* Values in parentheses are 95% CIs.

ACRIN DMIST = American College of Radiology Imaging Network Digital Mammographic Imaging Screening Trial.
* Compressed breast thickness ≥7.5 cm is assumed to correspond to large breasts.

BI-RADS = Breast Imaging Reporting and Data Systems.
* Percentages may not sum to 100 due to rounding.

ACRIN DMIST = American College of Radiology Imaging Network Digital Mammographic Imaging Screening Trial.

ACRIN DMIST = American College of Radiology Imaging Network Digital Mammographic Imaging Screening Trial; CC = craniocaudal; ML = mediolateral; MLO = mediolateral oblique.
* Total plain views + magnification.
† DMIST.
‡ Scaled the distribution for screening examinations from DMIST based on the typical number of views for that diagnostic examination or procedure type relative to the typical number of screening views.
§ Expert opinion.

The boxes show the middle 50% of the data, which is the interquartile range. The horizontal lines within the boxes correspond to the median, and the plus symbols correspond to the mean. The whiskers go out 1.5 box widths or to the last point inside that range. Circles represent values outside the whiskers and are potential outliers.
DMIST = Digital Mammographic Imaging Screening Trial.