Annual screening strategies in BRCA1 and BRCA2 gene mutation carriers

A comparative effectiveness analysis



This article is corrected by:

  1. Errata: Erratum: Annual screening strategies in BRCA1 and BRCA2 gene mutation carriers: A comparative effectiveness analysis Volume 118, Issue 21, 5448, Article first published online: 20 March 2012

  • A portion of this work was presented in abstract form as an oral presentation at the 96th Scientific Assembly and Annual Meeting of the Radiological Society of North America; November 28 to December 3, 2010; Chicago, IL.



Although breast cancer screening with mammography and magnetic resonance imaging (MRI) is recommended for breast cancer-susceptibility gene (BRCA) mutation carriers, there is no current consensus on the optimal screening regimen.


The authors used a computer simulation model to compare 6 annual screening strategies (film mammography [FM], digital mammography [DM], FM and magnetic resonance imaging [MRI] or DM and MRI contemporaneously, and alternating FM/MRI or DM/MRI at 6-month intervals) beginning at ages 25 years, 30 years, 35 years, and 40 years, and 2 strategies of annual MRI with delayed alternating DM/FM versus clinical surveillance alone. Strategies were evaluated without and with mammography-induced breast cancer risk using 2 models of excess relative risk. Input parameters were obtained from the medical literature, publicly available databases, and calibration.


Without radiation risk effects, alternating DM/MRI starting at age 25 years provided the highest life expectancy (BRCA1, 72.52 years, BRCA2, 77.63 years). When radiation risk was included, a small proportion of diagnosed cancers was attributable to radiation exposure (BRCA1, <2%; BRCA2, <4%). With radiation risk, alternating DM/MRI at age 25 years or annual MRI at age 25 years/delayed alternating DM at age 30 years was the most effective, depending on the radiation risk model used. Alternating DM/MRI starting at age 25 years also produced the highest number of false-positive screens per woman (BRCA1, 4.5 BRCA2, 8.1).


Annual MRI at age 25 years/delayed alternating DM at age 30 years is probably the most effective screening strategy in BRCA mutation carriers. Screening benefits, associated risks, and personal acceptance of false-positive results should be considered in choosing the optimal screening strategy for individual women. Cancer 2012. © 2011 American Cancer Society.


Women with breast cancer-susceptibility 1 (BRCA1) and BRCA2 gene mutations have a significantly increased lifetime risk of developing breast cancer, and an estimated 40% to 65% of carriers develop breast cancer by age 70 years.1 Because of their elevated risk, carriers are advised to begin routine annual breast cancer screening at younger ages with both mammography and breast magnetic resonance imaging (MRI).2 However, there is no current consensus regarding the optimal age to begin annual screening or whether multimodality screening should occur contemporaneously or with alternating modalities every 6 months. In addition, concerns have been raised about the risks of earlier and repeated radiation exposure in women who already are at increased breast cancer risk.3-5

It has been demonstrated that screening mammography decreases breast cancer mortality in the general population6, 7 and that the use of screening MRI identifies cancers at smaller sizes and earlier stages in women with an increased breast cancer risk.8 Potential disadvantages of more intensive breast cancer screening include an increased number of false-positive (FP) screens, which may lead to additional imaging, biopsies, and patient anxiety in women without breast cancer. Additional potential harms of screening include the possibility of mammography-induced breast cancer and overdiagnosis/over treatment of breast cancers that ultimately may not cause death.9

A randomized controlled trial of breast cancer screening in women with BRCA gene mutations would be difficult to perform, in part because of the large numbers of participants and long length of follow-up required to demonstrate significant effects on overall life expectancy (LE) and breast cancer mortality. In the absence of definitive randomized controlled trial data, we have developed a computer simulation model to evaluate multimodality breast cancer screening with mammography and breast MRI. This model provides estimates of both screening benefits in terms of LE gains as well as potential disadvantages of FP screens and radiation exposure effects. The objective of this study was to evaluate the comparative effectiveness of breast cancer screening strategies using mammography (film mammography [FM] or digital mammography [DM]), either alone or in combination with MRI, in women with BRCA1/BRCA2 gene mutations.


No human subject data were collected from individual patients for this study; therefore, institutional review board approval was not required.

Model Overview and Inputs

The Markov Monte Carlo simulation model includes breast cancer development, detection, and treatment in asymptomatic BRCA1/BRCA2 carriers aged 25 years. For each scenario, 2 million individual women are tracked until death, and their outcomes are aggregated to provide cohort estimates of average LE, cumulative breast cancer incidence, and breast cancer mortality. For the base-case analysis, it was assumed that no women underwent prophylactic bilateral salpingo-oophorectomy, mastectomy, or chemoprevention. Competing mortality risks obtained from the United States 2005 life expectancy tables10 were adjusted to reflect the increased mortality rate from ovarian cancer.11

Model input parameters were obtained through a review of published literature and model calibration. BRCA1-specific and most other model parameter values have been published previously12, 14; BRCA2-specific parameters are provided in Table 1. Values for key model input parameters relating to diagnostic test performance and radiation dose are presented in Table 2. Sensitivity and specificity values for FM and DM were obtained from the Digital Mammographic Imaging Screening Trial (DMIST).22, 23 To approximate the test performance of mammography in BRCA mutation carriers, sensitivity and specificity from the subset of premenopausal women with dense breasts was used in the base-case analysis. Sensitivity values were stratified by tumor invasiveness (ductal carcinoma in situ vs invasive carcinoma) and size. Diagnostic test performance for MRI was obtained from the Magnetic Resonance Imaging Breast Screening (MARIBS) screening trial.16 Sensitivity and specificity of combined mammography/MRI strategies were obtained by combining individual modality test performance under an assumption of conditional independence.13 Estimates of radiation dose to the breast from screening mammography were obtained from the DMIST study.21, 22

Table 1. BRCA2-Related Model Parameters
ParameterBase-Case ValueRange Used In Sensitivity AnalysisaSource
  • Abbreviations: BRCA2, breast cancer-susceptibility gene 2; DCIS, ductal carcinoma in situ.

  • a

    Model calibration identified 44 good-fitting parameter sets, and the parameter set that best reproduced the expected stage distribution of clinically presenting cancers was selected for use in the base-case analysis. The remaining parameter sets were used in multiparameter sensitivity analysis (see Fig. 4).

Preclinical phase6.03.0-17.0Calibration
Incidence multiplier1.591.25-3.18Calibration
Probability of invasive cancer at onset0.0590.002-0.089Calibration
Annual rate of DCIS becoming invasive cancer0.2970.25-0.39Calibration
Annual rate of DCIS stabilization0.0610.03-0.27Calibration
Probability of DCIS being nonpalpable0.1590.14-0.22Calibration
Mean tumor growth parameter (log-normal distribution)-2.21−2.10 to −2.26Calibration
Probability of estrogen receptor-positive cancer0.66Not variedLakhani 200215
Table 2. Values and Sources for Test Performance Characteristics and Radiation Doses
Input ParameterBase CaseValues Used for Sensitivity AnalysisSource
  • Abbreviations: DCIS, ductal carcinoma in situ; DM, digital mammography; DMIST, Digital Mammographic Imaging Screening Trial; FM, film mammography; MARIBS, Magnetic Resonance Imaging Breast Screening; mGy, milligrays; RT, radiation therapy.

  • a

    DMIST data were obtained directly from authors E.D.P. and C.G.

FM sensitivity by size, mm  DMISTa
  >50.440.51 (>3 mm) 
 Invasive cancers   
DM sensitivity by size, mm  DMISTa
  >50.720.65 (>3 mm) 
 Invasive cancers   
MRI sensitivity by size, mm  Base Case: MARIBS-Leach 2005,16 Sensitivity Analysis: Kriege 2004,17 Sardanelli 2007,18 Kuhl 2005,19 Warner 200420
  >100.500.167-1.00 (>5 mm) 
 Invasive cancers   
  10-150.780.813-0.933 (>5 mm) 
Correlation of mammography and MRI test performance1.00−0.38 to 0.18Lee 200913
FM mammography specificity0.890.90, 0.95DMISTa
DM mammography specificity0.900.91, 0.96DMISTa
MRI specificity0.810.898-0.972MARIBS-Leach 200516
RT dose for screening FM (2 views), mGy4.982.00-8.00Hendrick 201021
RT dose for screening DM (2 views), mGy4.151.70-6.80Hendrick 201021

Strategies Evaluated

Twenty-six screening strategies were compared with a reference strategy of clinical surveillance without imaging. Breast cancer screening modalities included FM, DM, and MRI. Annual mammography (FM or DM) was evaluated either alone or in conjunction with annual MRI, which could be performed contemporaneously or at alternating 6-month intervals. Each of these 6 strategies was evaluated beginning at ages 25 years, 30 years, 35 years, and 40 years. Two additional strategies of annual MRI starting at age 25 years with annual FM or DM added at age 30 years also were evaluated.

Radiation Risk

We used 2 models of excess relative risk (ERR), in which radiation-induced breast cancer risk is proportional to a population's underlying cancer risk: the attained-age (AA) model and the age-at-exposure (AE) model. These models were developed by Preston and colleagues24 using pooled data from 8 different cohorts that were exposed to different levels of ionizing radiation. The results indicated that no single ERR model described observed findings across all cohorts. In the AA model, ERR is a function of the cumulative dose (dc) of radiation exposure and the current age (agec) of the patient:

equation image(1)

In the AE model, the effect of each radiation exposure on ERR is a function of the dose of radiation exposure (de) and the age at which the individual was exposed (agee):

equation image(2)

The cumulative ERR was then calculated by summing the ERR for all exposures. In the simulation model, total ERR due to radiation exposure was recalculated each time a woman underwent screening mammography. This risk was then multiplied by the baseline age-specific breast cancer risk.


The primary outcome projected for each strategy was average LE. No adjustments for quality of life or discounting were applied. Secondary health outcomes included the percentage reduction in breast cancer mortality (relative decrease in breast cancer deaths in screened vs unscreened cohorts) and the average number of FP screens per woman for each strategy without and then with radiation exposure risk. Overdiagnosis was defined as the proportion of detected cancers in the screening scenarios that were not detected in the clinical surveillance scenario.25

Efficiency of Strategies

To examine the benefits of each strategy in the context of potential disadvantages, we defined efficient strategies as those that resulted in maximum health benefit (life-years) while minimizing FP screens. To compare screening strategy efficiency, we first ranked all strategies in order of increasing number of FP screens. For a given number of FP screens per woman, the screening strategy with the highest LE was considered the most efficient. Strategies that provided the greatest additional LE gain as the number of FP screens per woman increased were connected on an “efficiency frontier.” If a strategy with a higher number of FPs resulted in a smaller benefit than strategies with lower numbers of FPs, then that strategy was considered dominated and was not included on the efficiency frontier.

Sensitivity Analyses

We evaluated the effect of uncertainty in model parameters on both the ranking of most efficient strategies and the LE gain from multimodality screening (alternating mammography/MRI at age 25 years) compared with mammography alone. BRCA1/BRCA2 mutation penetrance (which determines lifetime breast cancer risk) was varied over the 95% confidence interval reported in the literature.1 The effects of prophylactic oophorectomy (PO) at ages 35 years, 40 years, or 45 years were examined, assuming that PO would reduce breast cancer risk by 50% and ovarian cancer risk by 100%.26 Diagnostic test performance for mammography was evaluated in 2-way sensitivity analyses using test performance for all women with dense breasts in the DMIST trial.22 MRI test performance was varied similarly using values reported from other prospective trials of MRI in high-risk women.17-20 We also examined the effect of increasing DM and MRI specificity by 5% after the first screen and of positive and negative test correlation for combined-modality strategies. Radiation exposure effects from mammography were assessed by various radiation dose exposures from the 10th percentile to the 90th percentile reported for DMIST participants.21 We examined the effect of variation in natural history parameters by performing multiparameter sensitivity analysis using well-fitting parameter sets identified during model calibration.


Life Expectancy Gains with Screening

For both BRCA1 and BRCA2 mutation carriers, strategies using DM resulted in higher average LE than strategies using FM, and strategies using alternating mammography/MRI screening were more effective than contemporaneous mammography/MRI (Fig. 1). When radiation risk was not included in the model, alternating DM/MRI produced the highest LE at all ages of screening initiation, with the maximum LE achieved when starting at age 25 years. Using this strategy, the LE gain compared with clinical surveillance was 1.89 years for BRCA1 mutation carriers and 1.76 years for BRCA2 mutation carriers (overall LE, 72.52 years and 77.63 years, respectively) (Table 3).

Figure 1.

Projected life expectancy is illustrated for annual screening strategies in (Top) BRCA1 mutation carriers and (Bottom) BRCA2 mutation carriers in the absence of radiation risk. For both populations, alternating digital mammography (DM) with magnetic resonance imaging (MRI) produced the highest life expectancy at all ages of screening initiation, and the highest life expectancy was achieved by alternating DM with MRI starting at age 25 years. FM indicates film mammography.

Table 3. Outcomes for Efficient Screening Strategies
Strategy (Age at Initiation, y)No. of FP Screens per WomanMean LE, yBC Mortality Reduction, %No. of FP Screens per WomanMean LE, yBC Mortality Reduction, %
  • Abbreviations: AA, attained age; AE, age at exposure; BC, breast cancer; BRCA1 and BRCA2, breast cancer-susceptibility genes 1 and 2, respectively; DM, digital mammography; ERR, excess relative risk; FM, film mammography; FP, false-positive; LE, life expectancy; MRI, magnetic resonance imaging; NA, not applicable.

  • a

    These were the dominated strategies.

No radiation risk      
 No imaging0.070.66NA0.075.87NA
 Annual DM (30)1.571.9612.23.077.3326.6
 Annual DM (25)2.072.03a12.63.577.3726.9
 Alternating DM with MRI (30)3.572.4516.27.077.5830.9
 Annual MRI (25) with delayed DM (30)4.272.49a16.67.877.60a31.1
 Alternating DM with MRI (25)4.672.5216.78.277.6331.2
ERR AA radiation risk model      
 No imaging0.070.66NA0.075.87NA
 Annual DM (30)1.471.9111.42.977.2625.1
 Annual DM (25)1.971.91a11.33.477.24a24.6
 Alternating DM with MRI (30)3.472.3815.87.077.5430.0
 Annual MRI (25) with delayed DM (30)4.272.4416.07.877.5930.2
 Alternating DM with MRI (25)4.572.4616.18.177.57a30.1
ERR AE radiation risk model      
 No imaging0.070.66NA0.075.87NA
 Annual DM (30)1.471.9111.42.977.2825.1
 Annual DM (25)1.971.88a11.23.477.24a24.6
 Alternating DM with MRI (30)3.472.3715.56.977.52a29.4
 Annual MRI (25) with delayed DM (30)4.172.4115.97.777.5829.9
 Alternating DM with MRI (25)4.572.39a15.48.077.52a28.6

Effects on Breast Cancer Mortality

When examining the effects of screening strategies on breast cancer mortality both without and with radiation risk, alternating DM/MRI starting at age 25 years achieved the greatest reduction in breast cancer mortality in both BRCA1 and BRCA2 mutation carriers, with the BRCA2 cohort demonstrating greater benefit (Table 3). In the absence of radiation risk, this alternating DM/MRI strategy reduced breast cancer mortality by 16.7% in the BRCA1 cohort and by 31.1% in the BRCA2 cohort. With radiation risk included, the AE model had a greater effect than the AA model; the breast cancer mortality benefit decreased to 15.4% (BRCA1) and 28.6% (BRCA2).

Effect of Radiation Exposure on Lifetime Cancer Incidence

For both BRCA1 and BRCA2 mutation carriers, a small percentage of detected cancers may be attributed to radiation exposure from mammographic screening. In the BRCA1 model without radiation risk, lifetime cancer incidence increased from 66% with clinical surveillance to 71.2% with alternating DM with MRI screening starting at age 25 years (Fig. 2) with an overdiagnosis rate of 7.3%. When radiation risk was added, the additional breast cancers with the same screening strategy comprised <2% of all detected breast cancers (lifetime cancer incidence: AA model, 71.7%; AE model, 72.2%). In the BRCA2 model without radiation risk, lifetime cancer incidence increased from 53.8% with clinical surveillance to 56.9% from alternating DM/MRI starting at age 25 years with an overdiagnosis rate of 5.6%. With radiation risk included in the BRCA2 model, the additional breast cancers were <4% of all detected breast cancers, (lifetime cancer incidence: AA model, 57.9%; AE model, 59.1%).

Figure 2.

The cumulative incidence of breast cancer (BC) is illustrated using the strategy of alternating digital mammography with magnetic resonance imaging starting at age 25 years. Compared with clinical surveillance (no screening), the cumulative incidence of BC in BRCA1 mutation carriers increases from 66.1% (without radiation risk; top dashed line) to 71.2% (solid light blue line). The cumulative incidence of BC in BRCA2 mutation carriers increases from 54% (without radiation risk; bottom dashed line) to 57% (solid green line). When radiation effects are included, <2% of diagnosed cancers can be attributed to radiation exposure using both the attained-age (AA) model (solid pink and brown lines) and the age-at-exposure (AE) model (solid navy and yellow lines).

Efficiency of Screening Strategies

The most efficient screening strategies in BRCA1 and BRCA2 carriers without and with radiation risk are summarized in Table 3. For women with either BRCA mutation, LE was maximized with the alternating DM/MRI strategy starting at age 25 years. However, the incremental LE gain between the 2 most effective BRCA1 screening strategies was modest. The LE was 72.49 years from annual MRI at age 25 years/delayed DM at age 30 years and 72.52 years from alternating DM/MRI starting at age 25 years. Similarly, in BRCA2 carriers, the LE was 77.60 years from annual MRI at age 25 years/delayed DM at age 30 years and 77.63 years from alternating DM/MRI starting at age 25 years.

When radiation risk (AA model) was included in the BRCA1 cohort (Fig. 3), alternating DM/MRI at age 25 years remained the most effective strategy (LE = 72.46 years). However, in the AE model, annual MRI at age 25 years/delayed DM at age 30 years produced the highest LE (72.41 years) with fewer FP screens than the alternating DM/MRI strategy. In the BRCA2 cohort, annual MRI at age 25 years/delayed DM at age 30 years was the most effective strategy in both the AA and AE models (77.59 and 77.58 years, respectively), providing greater LE with fewer FP screens than alternating DM/MRI at age 25 years. In both the BRCA1 cohort and the BRCA2 cohort, the incremental gain in LE with annual MRI at age 25 years/delayed DM at age 30 years, compared with alternating DM/MRI at age 30 years, was modest (gain, 0.04-0.06 years) relative to the increase in FP screens per woman (gain, 0.7-0.8).

Figure 3.

These charts illustrate the efficiency frontiers for (Top Left) the BRCA1 attained-age (AA) model, (Top Right) the BRCA1 age-at-exposure (AE) model, (Bottom Left) the BRCA2 AA model, and (Bottom Right) the BRCA2 AE model. The strategies that maximized life expectancy for a given level of false-positive screens are considered efficient and are connected by the solid line. Open circles indicate dominated strategies. Strategies are notated as modality (age at first screen). Ann indicates annual; DM, digital mammography; Alt, alternating at 6-month intervals; MRI, magnetic resonance imaging.

Sensitivity Analyses

The ranking of the most effective strategies in both BRCA1 and BRCA2 carriers remained stable across the range of parameters examined. The range in LE gain from multimodality screening (alternating DM/MRI) compared with digital mammography alone was most dependent on MRI test performance (Fig. 4), with the LE benefit in BRCA1 carriers ranging from 0.45 years to 0.72 years. Multimodality screening became more beneficial as breast cancer risk increased and became less beneficial as breast cancer risk decreased. When BRCA1 lifetime breast cancer risk increased from the base-case value of 65% to 74%, the LE benefit with multimodality screening, compared with annual DM alone, increased from 0.49 years (base case) to 0.53 years. Conversely, when BRCA1 lifetime risk was reduced to 42%, the LE gain decreased to 0.32 years.

Figure 4.

Influential parameters and their effects on life-expectancy (LE) gain are illustrated for alternating digital mammography (DM) and magnetic resonance imaging (MRI) starting at age 25 years versus annual DM alone starting at age 25 years. The results are presented in decreasing order of LE gain variation and indicate that MRI test performance and lifetime breast cancer risk estimates are the most influential factors for estimating LE gains from multimodality screening in both breast cancer-susceptibility gene 1 (BRCA1) and BRCA2 mutation carriers. Vertical lines represent the LE gain in the base case (BRCA1, 0.49 years; BRCA2, 0.26 years). ERR indicates excess relative risk.

Similarly, when risk-reducing PO was modeled, the benefits of multimodality screening in BRCA1 carriers decreased to 0.34 years (PO at age 35 years) and 0.47 years (PO at age 45 years). By using test performance data from the DMIST patient subpopulation with dense breast tissue compared with premenopausal women with dense breast tissue slightly decreased the additional benefit of multimodality screening to 0.46 years. The addition of radiation risk to the model increased the benefit from 0.49 years without radiation risk to 0.51 years (AE model) and 0.55 years (AA model).

Sensitivity analysis of these parameters in the BRCA2 cohort had similar effects. Varying MRI test performance resulted in the widest range of LE benefit from multimodality screening (0.21-0.42 years; base-case, 0.26 years). When the lifetime breast cancer risk increased from 50% in the base case to 56%, the added LE benefit remained stable at 0.26 years. When the cumulative incidence decreased to 31%, the LE gain decreased to 0.13 years. When PO was modeled at ages 35 years, 40 years, and 45 years, the LE gain ranged from 0.18 years to 0.20 years. Similar to the BRCA1 cohort, using test performance data from the entire dense breast subgroup in the DMIST population slightly decreased the multimodality screening benefit to 0.24 years. The addition of radiation risk also contributed a small additional multimodality screening benefit, from 0.26 years to 0.29 years.

We also examined the effects of using the different natural history parameters identified in model calibration. When we simulated the strategies using all 172 well fitting parameter sets that were identified during the model calibration process for BRCA1, the LE gain for alternating DM/MRI compared with annual DM ranged from 0.42 years to 0.60 years. When we used 44 well fitting parameter sets identified for the BRCA2 cohort, the LE gain ranged from 0.22 years to 0.35 years.


The results from this study suggest that, in women with BRCA mutations, screening with mammography and MRI will provide greater LE and breast cancer mortality reduction. In this analysis, we examined screening strategies both without and with the risk of radiation-induced breast cancer from screening mammography. Although concerns about radiation risk from screening mammography have been raised, a recent meta-analysis demonstrated an increased but nonsignificant relation between low-dose radiation exposure and breast cancer risk in women with a familial or genetic predisposition.4 However, a significant association was identified among women who reported ≥5 exposures, the scenario most relevant for women choosing breast cancer screening.

When screening strategies are modeled without accounting for the risk of radiation-induced cancers, alternating DM/MRI starting at age 25 years is the most effective strategy. The model included an ERR model of radiation exposure risk in which the radiation-induced risk was proportional to the increased underlying cancer risks in women with BRCA mutations. When radiation risk is modeled, the most effective screening strategy differs between BRCA1 carriers and BRCA2 carriers.

For BRCA1 carriers, either annual MRI at age 25 years combined with alternating DM delayed until age 30 years or alternating DM/MRI at age 25 years provides the greatest LE, depending on the radiation risk model used. Although our results indicate that these 2 strategies provide comparable LE, delaying the start of mammographic screening until age 30 years decreases the number of total mammographic examinations and expected FP screens. For BRCA2 carriers, when radiation exposure risk was included in the model, the most effective strategy shifted from alternating DM/MRI starting at age 25 years to annual MRI at age 25 years with alternating DM added at age 30 years. This strategy provided the greatest LE with both radiation risk models and had fewer FP screens. It is noteworthy that, in both the BRCA1 cohort and the BRCA2 cohort, the additional LE gain with adding annual MRI between ages 25 and 30 years, compared with starting alternating multimodality screening at age 30 years, was small relative to the higher number of FP screens. This suggests that, for some of these high-risk women, it may be reasonable to delay all imaging-based screening until age 30 years, for instance, in women whose first-degree relatives were diagnosed with breast cancer at age ≥40 years or women with high anxiety about FP screens.

Our model further indicates that <4% of all diagnosed cancers would be attributable to radiation exposure. These findings were stable even when the 90th percentile of radiation exposure from mammography was modeled. Thus, the benefits of most screening strategies that include mammography appear to substantially exceed the risks associated with radiation exposure.

Other models have been used to quantify the benefits of multimodality screening in the BRCA1/BRCA2 population but have not examined these benefits in the context of radiation risk or FP screens.27, 28 Moreover, neither model specifically examined the benefits of strategies using DM and alternating with MRI at 6-month intervals, which proved most effective in our analysis. Berrington de Gonzalez et al5 used an ERR model to calculate the long-term risk of radiation-induced breast cancer mortality caused by mammography alone in young women with BRCA1/BRCA2 mutations. Their results suggested that the benefits of mammography at ages <30 years did not outweigh the risk of radiation-induced cancers. Our model similarly suggests that, although the use of MRI is beneficial in women as young as age 25 years, the additional gains of using mammography before age 30 years are small and may be negligible.

As with all modeling studies, ours has some limitations. First, our model is an approximation of reality, with intrinsic simplifying assumptions. We assumed that radiation-induced breast cancers behave like other breast cancers in the BRCA1/BRCA2 populations in terms of growth rates, hormone receptor status, and prognosis, because, currently, these characteristics of radiation-induced breast cancers are not well described. Although we used input data specific to BRCA1/BRCA2 carrier populations whenever possible, some data come from small, very select patient samples. In those instances, we chose base-case input parameter estimates from studies of breast cancer in the general population, such as the large databases maintained by the Surveillance, Epidemiology, and End Results Program11 and the Breast Cancer Surveillance Consortium,29 or meta-analyses of randomized trials.30 Also, the model focuses on the development and detection of first primary breast cancer, and we assumed perfect adherence to screening and treatment protocols.

In conclusion, our results suggest that, for women with BRCA mutations, starting MRI screening at age 25 years, combined with MRI with alternating DM (starting at either age 25 years or age 30 years in women with BRCA1 carriers and starting at age 30 years in BRCA2 carriers) provides the greatest LE. The projected benefits of these strategies, along with their associated risks and patient acceptance of FP screening results, should be considered when making individual screening decisions.


This work was supported by in part by the following grants and organizations: Harvard Medical School Office for Enrichment Programming (K.P.L.), National Institutes of Health (NIH) grant K07CA128816 (J.M.L.), NIH grant K25CA133141 (C.Y.K.), NIH grant R00CA126147 (P.M.M.), and American Cancer Society grant MRSG112037 (E.M.O.).


Dr. Pisano has served as an Advisory Board member and/or consultant for NextRay, Inc., MiCo, ACR Image Metrix, Zumatek, GE Healthcare, Konica-Minolta, VuComp, and Sectra; has stock ownership in NextRay; and has received research grants from Imaging Diagnostic Systems, GE Healthcare, Naviscan PET Systems, Konica-Minolta, DOBI Systems, VuComp, Sectra, Zumatek, Xintek, and Mi-Co. Dr. Gazelle has served as a consultant for GE Healthcare.