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Original Research |

Radiation-Induced Breast Cancer Incidence and Mortality From Digital Mammography Screening: A Modeling StudyRadiation-Induced Breast Cancer From Digital Mammography Screening FREE

Diana L. Miglioretti, PhD; Jane Lange, PhD; Jeroen J. van den Broek, MSc; Christoph I. Lee, MD, MSHS; Nicolien T. van Ravesteyn, PhD; Dominique Ritley, MPH; Karla Kerlikowske, MD; Joshua J. Fenton, MD, MPH; Joy Melnikow, MD, MPH; Harry J. de Koning, PhD; and Rebecca A. Hubbard, PhD
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This article was published at www.annals.org on 12 January 2016.


From University of California, Davis, School of Medicine, Davis, California; University of California, Davis, Sacramento, California; Group Health Research Institute, University of Washington, and Hutchinson Institute for Cancer Outcomes Research, Fred Hutchinson Cancer Research Center, Seattle, Washington; Erasmus MC University Medical Center Rotterdam, Rotterdam, The Netherlands; University of California, San Francisco, San Francisco, California; and University of Pennsylvania, Philadelphia, Pennsylvania.

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-290-2012-00015I), 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 http://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/ConflictOf InterestForms.do?msNum=M15-1241.

Editors' Disclosures: Christine Laine, MD, MPH, Editor in Chief, reports that she has no financial relationships or interests to disclose. Darren B. Taichman, MD, PhD, Executive Deputy Editor, reports that he has no financial relationships or interests to disclose. Cynthia D. Mulrow, MD, MSc, Senior Deputy Editor, reports that she has no relationships or interests to disclose. Deborah Cotton, MD, MPH, Deputy Editor, reports that she has no financial relationships or interest to disclose. Jaya K. Rao, MD, MHS, Deputy Editor, reports that she has stock holdings/options in Eli Lilly and Pfizer. Sankey V. Williams, MD, Deputy Editor, reports that he has no financial relationships or interests to disclose. Catharine B. Stack, PhD, MS, Deputy Editor for Statistics, reports that she has stock holdings in Pfizer.

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 (http://breastscreening.cancer.gov).

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.

Current Author Addresses: Dr. Miglioretti: Department of Public Health Sciences, University of California, Davis School of Medicine, One Shields Avenue, Medical Science Building 1C, Room 144, Davis, CA 95616.

Dr. Lange: Group Health Research Institute, 1730 Minor Avenue, Suite 1600, Seattle, WA 98101.

Mr. van den Broek and Drs. van Ravesteyn and de Koning: Department of Public Health, Erasmus MC University Medical Center Rotterdam, PO Box 2040, 3000 CA Rotterdam, The Netherlands.

Dr. Lee: Department of Radiology, University of Washington School of Medicine, 825 Eastlake Avenue East, G3-200, Seattle, WA 98109.

Ms. Ritley and Drs. Fenton and Melnikow: Center for Healthcare Policy and Research, University of California, Davis, 2103 Stockton Boulevard, Sacramento, CA 95817.

Dr. Kerlikowske: General Internal Medicine Section, San Francisco Veterans Affairs Medical Center, 111A1, 4150 Clement Street, San Francisco, CA 94121.

Dr. Hubbard: Department of Biostatistics & Epidemiology, University of Pennsylvania, 604 Blockley Hall, 423 Guardian Drive, Philadelphia, PA 19104-6021.

Author Contributions: Conception and design: D.L. Miglioretti, H.J. de Koning, R.A. Hubbard.

Analysis and interpretation of the data: D.L. Miglioretti, J.J. van den Broek, C.I. Lee, N.T. van Ravesteyn, K. Kerlikowske, J.J. Fenton, J. Melnikow, H.J. de Koning, R.A. Hubbard.

Drafting of the article: J. Lange, C.I. Lee, D. Ritley, K. Kerlikowske.

Critical revision of the article for important intellectual content: D.L. Miglioretti, J. Lange, J.J. van den Broek, C.I. Lee, N.T. van Ravesteyn, D. Ritley, K. Kerlikowske, J.J. Fenton, J. Melnikow, H.J. de Koning, R.A. Hubbard.

Final approval of the article: D.L. Miglioretti, J. Lange, J.J. van den Broek, C.I. Lee, N.T. van Ravesteyn, D. Ritley, K. Kerlikowske, J.J. Fenton, J. Melnikow, H.J. de Koning, R.A. Hubbard.

Statistical expertise: D.L. Miglioretti, J. Lange, J.J. van den Broek, H.J. de Koning, R.A. Hubbard.

Obtaining of funding: D.L. Miglioretti, K. Kerlikowske, H.J. de Koning, R.A. Hubbard.

Administrative, technical, or logistic support: D. Ritley.

Collection and assembly of data: J. Lange, J.J. van den Broek, C.I. Lee.


Ann Intern Med. 2016;164(4):205-214. doi:10.7326/M15-1241
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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.

12 12016.

Context

  • Repeated digital mammography examinations expose women to ionizing radiation that can increase breast cancer risk.

Contribution

  • This modeling study found that annual mammography screening of 100 000 women aged 40 to 74 years might induce 125 breast cancer cases and 16 deaths but avert 968 breast cancer deaths because of early detection. Factors associated with increased risk for radiation-induced cancer included large breasts requiring extra views, higher-than-average doses per view, beginning screening at younger ages, and annual screening.

Caution

  • The model had several assumptions.

Implication

  • Biennial mammography screening starting at age 50 years and use of the fewest number of views possible would decrease risk for radiation-induced breast cancer.


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 (35). 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 (79), 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), 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 (1417). 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.

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Figure 1.

Schematic of 2 modeling approaches used to simulate mammography events and outcomes associated with 8 screening strategies.

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.

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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 System (12) density at the start of screening, which could potentially decrease by 1 category at ages 50 and 65 years (20) (Appendix Table 2).

Table Jump PlaceholderAppendix Table 1. Distribution of Compressed Breast Thickness on Digital Mammography From ACRIN DMIST* 
Table Jump PlaceholderAppendix Table 2. Prevalence of BI-RADS Breast Density (by Age) and Probability of Changing Density Category at Age 50 and 65 Years, Estimated From the Breast Cancer Surveillance Consortium* 
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.

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Figure 2.

Screening mammography process.

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.

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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).

Table Jump PlaceholderAppendix Table 3. Distribution of the Number of Screening Mammography Views From ACRIN DMIST 
Table Jump PlaceholderAppendix Table 4. Number of Plain and Magnification Mammography Views, by Examination or Procedure Type and Breast Size, Estimated From ACRIN DMIST and Expert Opinion, and Percentage of Women With That Number of Views, Where Applicable 
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Appendix Figure.

Distribution of absorbed glandular (breast) dose of a single screening mammography view, by compressed breast thickness from DMIST.

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.

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Table Jump PlaceholderAppendix Table 5. Distribution of Number of Mammography Views and Radiation Dose From Each Screening Examination and All Follow-up Mammographies and Biopsies Within 1 Year of an Examination for Women Receiving Annual Screening From Age 40 to 74 Years 
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).

Table Jump PlaceholderTable 1. Comparison of Lifetime Attributable Risks for Radiation-Induced Breast Cancer and Breast Cancer Death From 2 Modeling Approaches* 
Table Jump PlaceholderTable 2. Lifetime Attributable Risks for Radiation-Induced Breast Cancer and Breast Cancer Death for Different Screening Strategies, by Breast Size* 

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).

Table Jump PlaceholderTable 3. Number of Breast Cancer Deaths Averted by Screening 100 000 Women and Number of Breast Cancer Deaths Averted per Case of and Death From Radiation-Induced Breast Cancer* 

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 (2225). 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.

Hendrick (3) 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 Mainprize (4) 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 (3541).

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.

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.
PubMed
CrossRef
 
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.
 
Hendrick RE. Radiation doses and cancer risks from breast imaging studies. Radiology. 2010; 257:246-53.
PubMed
CrossRef
 
Yaffe MJ, Mainprize JG. Risk of radiation-induced breast cancer from mammographic screening. Radiology. 2011; 258:98-105.
PubMed
CrossRef
 
Feig SA, Hendrick RE. Radiation risk from screening mammography of women aged 40–49 years. J Natl Cancer Inst Monogr. 1997; 119-24.
PubMed
 
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.
PubMed
CrossRef
 
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.
CrossRef
 
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.
CrossRef
 
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.
PubMed
CrossRef
 
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.
PubMed
CrossRef
 
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.
PubMed
CrossRef
 
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.
 
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.
PubMed
CrossRef
 
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.
PubMed
CrossRef
 
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.
PubMed
CrossRef
 
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.
PubMed
CrossRef
 
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.
PubMed
CrossRef
 
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.
PubMed
 
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 www.uspreventiveservicestaskforce.org/Page/Document/modeling-report-collaborative-modeling-of-us-breast-cancer-1/breast-cancer-screening1 on 2 September 2015.
 
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.
PubMed
 
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.
PubMed
CrossRef
 
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.
PubMed
CrossRef
 
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.
PubMed
CrossRef
 
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.
PubMed
CrossRef
 
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.
PubMed
CrossRef
 
American Cancer Society.  Cancer Facts & Figure 2015. Atlanta: American Cancer Society; 2015. Accessed at www.cancer.org/Research/CancerFactsFigures/index on 2 September 2015.
 
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.
PubMed
CrossRef
 
Olgar T, Kahn T, Gosch D. Average glandular dose in digital mammography and breast tomosynthesis. Rofo. 2012; 184:911-8.
PubMed
CrossRef
 
U.S. Food and Drug Administration.  Trends in Mammography Dose and Image Quality 1974–2009. 2015. Accessed at www.fda.gov/Radiation-EmittingProducts/MammographyQualityStandardsActandProgram/FacilityScorecard/ucm326264.htm on 4 May 2015.
 
Robinson M, Kotre CJ. Trends in compressed breast thickness and radiation dose in breast screening mammography. Br J Radiol. 2008; 81:214-8.
PubMed
CrossRef
 
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.
PubMed
CrossRef
 
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.
PubMed
CrossRef
 
Feng SS, Sechopoulos I. Clinical digital breast tomosynthesis system: dosimetric characterization. Radiology. 2012; 263:35-42.
PubMed
CrossRef
 
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.
PubMed
CrossRef
 
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.
PubMed
 
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.
PubMed
CrossRef
 
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.
PubMed
CrossRef
 
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.
PubMed
CrossRef
 
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.
PubMed
CrossRef
 
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.
PubMed
CrossRef
 
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.
PubMed
CrossRef
 
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.
PubMed
CrossRef
 

Figures

Grahic Jump Location
Figure 1.

Schematic of 2 modeling approaches used to simulate mammography events and outcomes associated with 8 screening strategies.

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.

Grahic Jump Location
Grahic Jump Location
Figure 2.

Screening mammography process.

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.

Grahic Jump Location
Grahic Jump Location
Appendix Figure.

Distribution of absorbed glandular (breast) dose of a single screening mammography view, by compressed breast thickness from DMIST.

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.

Grahic Jump Location

Tables

Table Jump PlaceholderAppendix Table 1. Distribution of Compressed Breast Thickness on Digital Mammography From ACRIN DMIST* 
Table Jump PlaceholderAppendix Table 2. Prevalence of BI-RADS Breast Density (by Age) and Probability of Changing Density Category at Age 50 and 65 Years, Estimated From the Breast Cancer Surveillance Consortium* 
Table Jump PlaceholderAppendix Table 3. Distribution of the Number of Screening Mammography Views From ACRIN DMIST 
Table Jump PlaceholderAppendix Table 4. Number of Plain and Magnification Mammography Views, by Examination or Procedure Type and Breast Size, Estimated From ACRIN DMIST and Expert Opinion, and Percentage of Women With That Number of Views, Where Applicable 
Table Jump PlaceholderAppendix Table 5. Distribution of Number of Mammography Views and Radiation Dose From Each Screening Examination and All Follow-up Mammographies and Biopsies Within 1 Year of an Examination for Women Receiving Annual Screening From Age 40 to 74 Years 
Table Jump PlaceholderTable 1. Comparison of Lifetime Attributable Risks for Radiation-Induced Breast Cancer and Breast Cancer Death From 2 Modeling Approaches* 
Table Jump PlaceholderTable 2. Lifetime Attributable Risks for Radiation-Induced Breast Cancer and Breast Cancer Death for Different Screening Strategies, by Breast Size* 
Table Jump PlaceholderTable 3. Number of Breast Cancer Deaths Averted by Screening 100 000 Women and Number of Breast Cancer Deaths Averted per Case of and Death From Radiation-Induced Breast Cancer* 

References

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.
PubMed
CrossRef
 
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.
 
Hendrick RE. Radiation doses and cancer risks from breast imaging studies. Radiology. 2010; 257:246-53.
PubMed
CrossRef
 
Yaffe MJ, Mainprize JG. Risk of radiation-induced breast cancer from mammographic screening. Radiology. 2011; 258:98-105.
PubMed
CrossRef
 
Feig SA, Hendrick RE. Radiation risk from screening mammography of women aged 40–49 years. J Natl Cancer Inst Monogr. 1997; 119-24.
PubMed
 
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.
PubMed
CrossRef
 
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.
CrossRef
 
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.
CrossRef
 
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.
PubMed
CrossRef
 
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.
PubMed
CrossRef
 
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.
PubMed
CrossRef
 
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.
 
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.
PubMed
CrossRef
 
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.
PubMed
CrossRef
 
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.
PubMed
CrossRef
 
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.
PubMed
CrossRef
 
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.
PubMed
CrossRef
 
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.
PubMed
 
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 www.uspreventiveservicestaskforce.org/Page/Document/modeling-report-collaborative-modeling-of-us-breast-cancer-1/breast-cancer-screening1 on 2 September 2015.
 
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.
PubMed
 
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.
PubMed
CrossRef
 
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.
PubMed
CrossRef
 
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.
PubMed
CrossRef
 
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.
PubMed
CrossRef
 
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.
PubMed
CrossRef
 
American Cancer Society.  Cancer Facts & Figure 2015. Atlanta: American Cancer Society; 2015. Accessed at www.cancer.org/Research/CancerFactsFigures/index on 2 September 2015.
 
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.
PubMed
CrossRef
 
Olgar T, Kahn T, Gosch D. Average glandular dose in digital mammography and breast tomosynthesis. Rofo. 2012; 184:911-8.
PubMed
CrossRef
 
U.S. Food and Drug Administration.  Trends in Mammography Dose and Image Quality 1974–2009. 2015. Accessed at www.fda.gov/Radiation-EmittingProducts/MammographyQualityStandardsActandProgram/FacilityScorecard/ucm326264.htm on 4 May 2015.
 
Robinson M, Kotre CJ. Trends in compressed breast thickness and radiation dose in breast screening mammography. Br J Radiol. 2008; 81:214-8.
PubMed
CrossRef
 
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.
PubMed
CrossRef
 
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.
PubMed
CrossRef
 
Feng SS, Sechopoulos I. Clinical digital breast tomosynthesis system: dosimetric characterization. Radiology. 2012; 263:35-42.
PubMed
CrossRef
 
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.
PubMed
CrossRef
 
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.
PubMed
 
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.
PubMed
CrossRef
 
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.
PubMed
CrossRef
 
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.
PubMed
CrossRef
 
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.
PubMed
CrossRef
 
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.
PubMed
CrossRef
 
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.
PubMed
CrossRef
 
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.
PubMed
CrossRef
 

Letters

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Radiation-Induced Breast Cancer?
Posted on March 4, 2016
William T. Phillips, Ralph Blumhardt
University of Texas Health Science Center at San Antonio
Conflict of Interest: None Declared

We read with interest your recent issue (16 Feb 2016) devoted to breast cancer. We are concerned that women reading the article by Miglioretti et. al may choose not to have screening mammography due to fears of radiation-induced breast cancer. This issue also contains an editorial article entitled "Time to Douse the Firestorm Around Breast Cancer Screening" (1) that states "women deserve to be aware of what the science says so they can make the best choice for themselves, together with their doctor." So, what is the science behind screening with mammography? On the positive side of the issue, women age 50-69 undergoing screening mammography have a 25% to 31% relative reduction in mortality from breast cancer (2). We take issue with the negative side, particularly, the idea that patients are subject to significant cancer risk from screening mammograms. Although we agree that there is an increased incidence of radiation induced cancer in patients who have undergone therapeutic radiation, there is little evidence to support the view that diagnostic radiology exams cause a significant increased incidence of secondary cancer.

We do not believe it should be assumed that "breast cancer increases linearly with increasing radiation dose within the exposure range from mammography" as stated in the first article of the breast cancer issue (3). This assumption is usually based on the Linear No-Threshold Model for which there is little experimental evidence to support the carcinogenicity of low dose radiation (i.e. doses in the range of <100 mSv). A recent manuscript entitled "The Birth of the Illegitimate Linear No-Threshold Model" addresses the phenomena of "radiophobia" which the authors are convinced causes death (4). We have shown in another article that there is no linear relationship between the quantity of radiation dose and the rare incidence of secondary malignancies in patients treated with radioactive iodine for thyroid cancer (5).
The existence of non-linearity of radiation bio-effects is even supported by recent research conducted by the Department of Energy’s Low Dose Research Program. Quoting directly from their website (http://science.energy.gov/ber/research/bssd/low-dose-radiation).

One example that challenges an old assumption is the finding that exposure to a low vs. high dose of radiation results in both qualitatively as well as quantitatively different cellular and molecular responses, thus demonstrating non-linear response with respect to dose. Another is the finding that in addition to high-dose biological damage that may lead to cancer, very low dose radiation exposure may participate in beneficial biological outcomes by stimulation of our natural tissue surveillance mechanisms.

For women considering their choice of whether to participate in breast cancer screening, we certainly believe the projected reductions of 968 breast cancer deaths per 100,000 women aged 40 to 74 with estimates derived from actual clinical studies (2, 3) greatly outweighs the theoretical projected risks of radiation-induced breast cancer deaths of 16 per 100,000, based, unfortunately, on an unproven linear no threshold model (3,4).

References

1. Laine C, Dickersin K, Mulrow C. Time to Douse the Firestorm Around Breast Cancer Screening. Ann Intern Med. 2016 Feb 16;164(4):303-4.

2. Nelson HD, Fu R, Cantor A, Pappas M, Daeges M, Humphrey L.
Effectiveness of Breast Cancer Screening: Systematic Review and Meta-analysis to Update the 2009 U.S. Preventive Services Task Force Recommendation. Ann Intern Med. 2016 Feb 16;164(4):244-55

3. Miglioretti DL, Lange J, van den Broek JJ, Lee CI, van Ravesteyn NT, Ritley D, et al. Radiation-Induced Breast Cancer Incidence and Mortality From Digital Mammography Screening: A Modeling Study.Ann Intern Med. 2016 Feb 16;164(4):205-14.

4. Siegel JA, Pennington CW, Sacks B, Welsh JS.
The Birth of the Illegitimate Linear No-Threshold Model: An Invalid Paradigm for Estimating Risk Following Low-dose Radiation Exposure.Am J Clin Oncol. 2015 Nov 3. [Epub ahead of print]

5. Blumhardt R, Wolin EA, Phillips WT, Salman UA, Walker RC, Stack BC Jr, Metter D Current controversies in the initial post-surgical radioactive iodine therapy for thyroid cancer: a narrative review. Endocr Relat Cancer. 2014;21(6):R473-84
Author's Response
Posted on July 6, 2016
Diana L. Miglioretti, PhD, Christoph I. Lee, MD
University of California, Davis, University of Washington
Conflict of Interest: None Declared
We agree with the authors that the benefit of reduced breast cancer mortality associated with guideline-based screening mammography outweighs the risks of radiation-induced breast cancer, as stated in our article.1 Likewise, we also hope women do not forgo mammography due to fears of radiation-induced breast cancer; however, that is not a valid reason to not estimate and report these risks. Radiation exposure from mammography screening is a potential harm, even if a relatively small one, that should be considered when evaluating the benefit-harm tradeoff of different screening strategies.

To model breast cancer risk from radiation exposure, we used the excess absolute risk model from pooled analysis of four cohorts by Preston et al.2, which is the model preferred by the BEIR-VII committee.3 Women in these cohorts were exposed to cumulative radiation doses to the breast of 20 mGy and higher. This level of exposure is reached after two to four rounds of mammography screening and associated diagnostic work-up; thus, our projections are not extrapolated beyond the range of data used for model development. Recent, updated analyses of atomic bomb survivors provide additional evidence of breast cancer risk from exposures in this dose range.4 They found that cancer risk increased linearly with increasing dose, and a formal dose-threshold analysis found no evidence of a threshold below which there was no increased cancer risk.

Our study explored variation in radiation-induced cancer risk across women due to variation in radiation dose per view and additional imaging performed to evaluate abnormal screening results. We found that some women, e.g., women with large breasts, are receiving much higher radiation doses than previously recognized due to receiving extra views at each exam and higher doses per view. To minimize risks, radiology practices should ensure women with large breasts are imaged with large detectors and should minimize the number of additional views.

There is no evidence that full disclosure of potential risks from cumulative radiation exposure would discourage women from obtaining screening mammography. In fact, the literature to date suggests that patients are not dissuaded from undergoing clinically indicated imaging exams when informed about associated radiation-induced cancer risks.5 In an era of greater transparency and shared-decision making, women want, and should be given, information about all of the potential benefits and risks of mammography screening in order to make a truly informed choice.

Diana L. Miglioretti, PhD
University of California, Davis
Division of Biostatistics, Department of Public Health Sciences, University of California Davis School of Medicine, Davis, CA 95616
Group Health Research Institute, Seattle, WA 98101

Christoph I. Lee, MD, MSHS
Department of Radiology, University of Washington, Seattle, WA
Department of Health Services, University of Washington, Seattle, WA
Hutchinson Institute for Cancer Outcomes Research, Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA


REFERENCES
1. Miglioretti DL, Lange J, van den Broek JJ, Lee CI, van Ravesteyn NT, Ritley D, Kerlikowske K, Fenton JJ, Melnikow J, de Koning HJ, Hubbard RA. Radiation-Induced Breast Cancer Incidence and Mortality From Digital Mammography Screening: A Modeling Study. Ann Intern Med. 2016;164(4):205-14. PubMed PMID: 26756460.
2. 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(2):220-35. Epub 2002/07/11. PubMed PMID: 12105993.
3. Committee to Assess Health Risks from Exposure to Low Levels of Ionizing Radiation and National Research Council. Health Risks from Exposure to Low Levels of Ionizing Radiation: BEIR VII Phase 2. Washington, D.C.: The National Academies Press; 2006.
4. Ozasa K, Shimizu Y, Suyama A, Kasagi F, Soda M, Grant EJ, Sakata R, Sugiyama H, Kodama K. Studies of the mortality of atomic bomb survivors, Report 14, 1950-2003: an overview of cancer and noncancer diseases. Radiat Res. 2012;177(3):229-43. PubMed PMID: 22171960.
5. Lam DL, Larson DB, Eisenberg JD, Forman HP, Lee CI. Communicating Potential Radiation-Induced Cancer Risks From Medical Imaging Directly to Patients. AJR Am J Roentgenol. 2015;205(5):962-70. Epub 2015/08/22. PubMed PMID: 26295534.

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