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Original Research |

Tipping the Balance of Benefits and Harms to Favor Screening Mammography Starting at Age 40 Years: A Comparative Modeling Study of Risk

Nicolien T. van Ravesteyn, MSc; Diana L. Miglioretti, PhD; Natasha K. Stout, PhD; Sandra J. Lee, DSc; Clyde B. Schechter, MD, MA; Diana S.M. Buist, PhD, MPH; Hui Huang, MS; Eveline A.M. Heijnsdijk, PhD; Amy Trentham-Dietz, PhD; Oguzhan Alagoz, PhD; Aimee M. Near, MPH; Karla Kerlikowske, MD, MS; Heidi D. Nelson, MD, MPH; Jeanne S. Mandelblatt, MD, MPH; and Harry J. de Koning, MD, PhD
[+] Article and Author Information

From Erasmus Medical Center, Rotterdam, the Netherlands; University of Washington, Seattle, Washington; Harvard Medical School/Harvard Pilgrim Health Care Institute and Dana-Farber Cancer Institute, Boston, Massachusetts; Albert Einstein College of Medicine, Bronx, New York; University of Wisconsin-Madison, Madison, Wisconsin; Georgetown University Medical Center and Cancer Prevention and Control Program, Lombardi Comprehensive Cancer Center, Washington, DC: San Francisco Veterans Affairs Medical Center and University of California, San Francisco, San Francisco, California; Oregon Health & Science University and Providence Cancer Center, Providence Health & Services, Portland, Oregon.

Note: Both Jeanne S. Mandelblatt, MD, MPH, and Harry J. de Koning, MD, PhD, served as the senior authors for this manuscript.

Disclaimer: The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Cancer Institute or the National Institutes of Health.

Acknowledgment: The authors thank Drs. Kathleen Cronin and Brian Sprague for their valuable advice and consultation on this project. They also thank the BCSC investigators, participating women, mammography facilities, and radiologists for the data they have provided for this study. The BCSC investigators and procedures for requesting BCSC data for research purposes are listed at http://breastscreening.cancer.gov/.

Grant Support: The collection of BCSC cancer data used in this study was supported in part by several state public health departments and cancer registries throughout the United States. For a full description of these sources, please see www.breastscreening.cancer.gov/work/acknowledgement.html. This research was supported by a National Cancer Institute Activities to Promote Research Collaboration supplement (U01CA086076-10S1), and, in part, by National Cancer Institute grants U01CA88283, U01CA152958, and KO5CA96940. Data collection was supported by the BCSC funded by the National Cancer Institute cooperative agreements U01CA63740, U01CA86076, U01CA86082, U01CA63736, U01CA70013, U01CA69976, U01CA63731, and U01CA70040.

Potential Conflicts of Interest: Disclosures can be viewed at www.acponline.org/authors/icmje/ConflictOfInterestForms.do?msNum=M11-2358.

Reproducible Research Statement:Study protocol: Available from Ms. van Ravesteyn (e-mail, mailto:n.vanravesteyn@erasmusmc.nl). Statistical code: Not available; model profiles are available at http://cisnet.cancer.gov/breast/profiles.html. Data set: Procedures for requesting BCSC data for research purposes are provided at http://breastscreening.cancer.gov/work/proposal_data.html.

Requests for Single Reprints: Nicolien van Ravesteyn, MSc, Department of Public Health, Erasmus Medical Center, PO Box 2040, 3000 CA, Rotterdam, the Netherlands; e-mail, mailto:n.vanravesteyn@erasmusmc.nl.

Current Author Addresses: Ms. van Ravesteyn and Drs. Heijnsdijk and de Koning: Department of Public Health, Erasmus Medical Center, PO Box 2040, 3000 CA, Rotterdam, the Netherlands.

Drs. Miglioretti and Buist: Group Health Research Institute, 1730 Minor Avenue, Suite 1600, Seattle, WA 98101.

Dr. Stout: Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, 133 Brookline Avenue, 6th Floor, Boston, MA 02215.

Dr. Lee: Dana-Farber Cancer Institute, 3 Blackfan Circle, Boston, MA 02115.

Dr. Schechter: Department of Family & Social Medicine, Albert Einstein College of Medicine, 1300 Morris Park Avenue, Mazer Building 406, Bronx, NY 10461.

Ms. Huang: Dana-Farber Cancer Institute, 44 Binney Street, Boston, MA 01720.

Dr. Trentham-Dietz: University of Wisconsin-Madison, 610 Walnut Street, WARF Room 307, Madison, WI 53726.

Dr. Alagoz: University of Wisconsin-Madison, 3242 Mechanical Engineering Building, 1513 University Avenue, Madison, WI 53706.

Ms. Near and Dr. Mandelblatt: Lombardi Comprehensive Cancer Center, 3300 Whitehaven Street, NW, Suite 4100, Washington, DC 20007.

Dr. Kerlikowske: University of California, San Francisco, 4150 Clement Street, Veterans Affairs Medical Center (111A1), San Francisco, CA 94121.

Dr. Nelson: Oregon Evidence-based Practice Center, Oregon Health & Science University, Mailcode BICC, 3181 Southwest Sam Jackson Park Road, Portland, OR 97239-3098.

Author Contributions: Conception and design: N.T. van Ravesteyn, D.L. Miglioretti, N.K. Stout, C.B. Schechter, D.S.M. Buist, A. Trentham-Dietz, H.D. Nelson, J.S. Mandelblatt, H.J. de Koning.

Analysis and interpretation of the data: N.T. van Ravesteyn, N.K. Stout, S.J. Lee, C.B. Schechter, D.S.M. Buist, E.A.M. Heijnsdijk, O. Alagoz, H.D. Nelson, J.S. Mandelblatt, H.J. de Koning.

Drafting of the article: N.T. van Ravesteyn, S.J. Lee, D.S.M. Buist, J.S. Mandelblatt.

Critical revision of the article for important intellectual content: N.T. van Ravesteyn, D.L. Miglioretti, N.K. Stout, C.B. Schechter, D.S.M. Buist, E.A.M. Heijnsdijk, A. Trentham-Dietz, H.D. Nelson, J.S. Mandelblatt, H.J. de Koning.

Final approval of the article: N.T. van Ravesteyn, D.L. Miglioretti, N.K. Stout, S.J. Lee, C.B. Schechter, D.S.M. Buist, E.A.M. Heijnsdijk, A. Trentham-Dietz, O. Alagoz, A.M. Near, H.D. Nelson, J.S. Mandelblatt, H.J. de Koning.

Provision of study materials or patients: D.S.M. Buist.

Statistical expertise: D.L. Miglioretti, N.K. Stout, C.B. Schechter, O. Alagoz.

Obtaining of funding: D.L. Miglioretti, C.B. Schechter, D.S.M. Buist, A. Trentham-Dietz, H.D. Nelson, J.S. Mandelblatt, H.J. de Koning.

Administrative, technical, or logistic support: D.S.M. Buist, J.S. Mandelblatt, H.J. de Koning.

Collection and assembly of data: N.T. van Ravesteyn, D.L. Miglioretti, N.K. Stout, C.B. Schechter, D.S.M. Buist, A.M. Near.


Ann Intern Med. 2012;156(9):609-617. doi:10.7326/0003-4819-156-9-201205010-00002
Text Size: A A A

Background: Timing of initiation of screening for breast cancer is controversial in the United States.

Objective: To determine the threshold relative risk (RR) at which the harm–benefit ratio of screening women aged 40 to 49 years equals that of biennial screening for women aged 50 to 74 years.

Design: Comparative modeling study.

Data Sources: Surveillance, Epidemiology, and End Results program, Breast Cancer Surveillance Consortium, and medical literature.

Target Population: A contemporary cohort of women eligible for routine screening.

Time Horizon: Lifetime.

Perspective: Societal.

Intervention: Mammography screening starting at age 40 versus 50 years with different screening methods (film, digital) and screening intervals (annual, biennial).

Outcome Measures: Benefits: life-years gained, breast cancer deaths averted; harms: false-positive mammography findings; harm–benefit ratios: false-positive findings/life-years gained, false-positive findings/deaths averted.

Results of Base-Case Analysis: Screening average-risk women aged 50 to 74 years biennially yields the same false-positive findings/life-years gained as biennial screening with digital mammography starting at age 40 years for women with a 2-fold increased risk above average (median threshold RR, 1.9 [range across models, 1.5 to 4.4]). The threshold RRs are higher for annual screening with digital mammography (median, 4.3 [range, 3.3 to 10]) and when false-positive findings/deaths averted is used as an outcome measure instead of false-positive findings/life-years gained. The harm–benefit ratio for film mammography is more favorable than for digital mammography because film has a lower false-positive rate.

Results of Sensitivity Analysis: The threshold RRs changed slightly when a more comprehensive measure of harm was used and were relatively insensitive to lower adherence assumptions.

Limitation: Risk was assumed to influence onset of disease without influencing screening performance.

Conclusion: Women aged 40 to 49 years with a 2-fold increased risk have similar harm–benefit ratios for biennial screening mammography as average-risk women aged 50 to 74 years. Threshold RRs required for favorable harm–benefit ratios vary by screening method, interval, and outcome measure.

Primary Funding Source: National Cancer Institute.

Figures

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Appendix Figure.

Schematic overview of simulated life histories and effect of screening.

The italicized words in the descriptions below refer to the words outlined in the figure. Sojourn time is the duration of the preclinical, screen-detectable phase of the tumor, and lead time is the interval from screen detection to the time of clinical diagnosis, when the tumor would have surfaced without screening. Model D is a state transition model where potential benefit from early detection arises because of a stage shift. The natural history of breast cancer is modeled analytically by using stochastic models. The model assumes that breast cancer (invasive) progresses from a no-disease (S0) state to preclinical (Sp) state and to clinical (Sc) state. Some cases will continue to the disease-specific death (Sd) state. Death due to other causes is treated as a competing risk. The Sp state begins when cancer is detectable at screening, and Sc begins when cancer is diagnosed in absence of screening. For a given birth cohort, age-specific invasive breast cancer incidence rate and age-dependent sojourn time in Sp (published values) are used to estimate the transition probabilities from S0 to Sp. The transition probabilities from Sp to Sc are estimated on the basis of the age-specific breast cancer incidence rate. The other basic assumption is that any reduction in mortality associated with screening is from the stage-shift: that is, screen-detected cases have a better stage distribution with a higher proportion of cases in earlier stages. The stage distribution data for screen-detected cases are obtained from BCSC and directly incorporated in constructing breast cancer–specific survival. In addition, the lead time for screen-detected cases is treated as a random variable and is adjusted in constructing the breast cancer–specific survival for screen-detected cases. When cancer is diagnosed, a treatment is applied by age, stage, and estrogen receptor status and treatment reduces the hazard of breast cancer–specific mortality by age, stage, and estrogen receptor status. Model E is a microsimulation model based on continuous tumor growth. The natural history of breast cancer is modeled as a continuously growing tumor from onset of cancer (starting with a tumor diameter of 0.1 mm). The moments that events happen are determined by tumor sizes. The screening threshold diameter determines the moment that the cancer is detectable at screening, and the diameter of clinical detection determines when the cancer will be diagnosed in the absence of screening. Each tumor has a size (the fatal diameter, which differs between tumors) at which diagnosis and treatment will no longer result in cure given available treatment options. If the tumor is diagnosed (either on the basis of clinical presentation with symptoms or by screening) and treated before the tumor reaches the fatal diameter, the woman will be cured and will die of non–breast cancer causes (death from other causes). Variation between tumors is modeled by probability distributions of parameters. Screening might detect tumors at a smaller tumor size with a larger probability of cure (because the tumor has not yet reached the fatal diameter) than when the cancer is diagnosed in the absence of screening. Model G-E is an event-driven continuous time–state transition model. On the basis of birth cohort–specific incidence curves, the date at which progressive breast cancer will appear clinically (if ever) is sampled, and the stage, estrogen receptor, and HER2 are then sampled according to age- and period-specific stage distributions for these parameters. A sojourn time is sampled from an age-specific distribution, and the beginning of the sojourn period is defined as the clinical incidence date minus the sojourn time. If a screening event takes place during the sojourn period, it may detect the tumor with probability equal to the age-specific mammography sensitivity. If the tumor is screen detected, a stage at detection is sampled from a probability distribution calculated from the observed lead time, the distributions of dwell times in the clinical stages, and the stage at the clinical detection date. Whether clinically detected or screen detected, treatment is sampled from an age-, stage-, estrogen receptor–, and HER2-period–specific distribution of possible treatment regimens. Each particular treatment regimen reduces the hazard of breast cancer mortality by a ratio that depends on age and stage at diagnosis, estrogen receptor, and HER2. The date of breast cancer death (which may turn out to be after the date of death from other causes) is then sampled from the corresponding age-, stage-, estrogen receptor–, HER2-treatment regimen–specific survival function. Simulated women who do not have progressive breast cancer may have limited malignant potential (LMP) breast cancer. Breast cancer with LMP cancer is modeled as never being clinically detected and is never fatal. However, it is screen detectable for 5 years and, if screen detected, its stage is always ductal carcinoma in situ (DCIS). These screen-detected LMP DCIS are then treated the same way as progressive breast cancer diagnosed during the DCIS stage, but treatment has no effect on mortality because these LMP tumors are never fatal. Model W is a discrete-event, stochastic tumor growth simulation model. It simulates the natural history of breast cancer using a continuous time growth model for tumor size and a Poisson process for tumor extent with a randomly assigned growth rate from a population-level distribution. In the model, breast cancer is assumed to be a progressive disease arising in the in situ stage. Model W further assumes that a fraction of all tumors have LMP. This subtype is nonfatal, is limited in size and stage to in situ and early localized disease, and is predominantly detected by screening mammography. If undetected for a fixed dwell period, such tumors are assumed to regress. Breast cancer can be detected by 1 of 2 methods: breast imaging (screen detected), or by symptoms, where the likelihoods of detection are functions of a woman's age and tumor size. Upon detection, a woman will receive standard treatment and, depending on calendar year and woman- and tumor-level characteristics, may also receive adjuvant treatment. Treatment effectiveness, a function of treatment type, is independent of the method of detection and is modeled as a “cure/no-cure” process.

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Potential impact of risk-based mammography screening
Posted on May 3, 2012
Lindsey C.Wu, HHMI Research Scholar, Barry I. Graubard and Mitchell H. Gail
DCEG, NCI
Conflict of Interest: None Declared

Mammographic screening guidelines from the U.S. Preventive Services Task Force target age groups, because "increasing age is the most important risk factor for breast cancer for most women"[1], but other factors influence breast cancer risk. van Ravesteyn and colleagues[2] found that women aged 40-49yrs with two-fold increased risk had similar harm-benefit ratios as women aged 50-74yrs. Here we estimate the number of women in their forties in the US who have harm-benefit ratios comparable to a 50-year-old woman without risk factors, for whom mammography is recommended.

Mammographic screening reduces breast cancer mortality by 15% for women aged 39-49yrs and by 14% for women aged 50-59yrs[1]. The argument against screening women in their forties is that the proportion of false positive screens is greater than for older women. Using age-specific sensitivity and specificity data from the Breast Cancer Screening Consortium[3], we calculated that women in their forties have nearly the same false positive rate as women aged 50-54yrs, provided the younger women have the same disease prevalence. Because prevalence is highly correlated with risk[4], risk models such as NCI's Breast Cancer Risk Assessment Tool(BCRAT, or "Gail Model")(http://www.cancer.gov/bcrisktool/) could be used to identify women in their forties with high enough risk to yield an acceptable false positive rate.

To estimate how many women in their forties might benefit from risk- based screening, we used nationally representative data from 2000-2005 from the National Health Interview Survey (NHIS)[5] to estimate the number of non-Hispanic white and black women in their forties who, owing to risk factors other than age, are at the same or greater risk for breast cancer as fifty-year-old women without these risk factors. We based our risk estimates on BCRAT, which uses family history, age at menarche, age at first live birth, and number of biopsies. Among US women aged 40-49yrs, we found that 73.6% of non-Hispanic whites (11.6 million (95%CI, 11.1-12.1)) and 30.9% of non-Hispanic blacks (0.85 million (95%CI, 0.73-0.97)) have risks above the fifty-year-old baseline. Hence, millions of US women in their forties may benefit from mammography screening as much as a low risk fifty-year-old, for whom routine screening is recommended.

van Ravesteyn and colleagues recognize the potential value of risk- based screening. Because risk factors other than age have substantial impact, we advocate the use of individualized absolute (not relative) risk estimates to help younger women and caregivers decide on the usefulness of mammographic screening.

1. Calonge, N., et al., Screening for breast cancer: US Preventive Services Task Force recommendation statement. Ann Intern Med, 2009. 151(10): p. 716-726.

2. van Ravesteyn, N.T., et al., Tipping the Balance of Benefits and Harms to Favor Screening Mammography Starting at Age 40 Years. Annals of Internal Medicine, 2012. 156(9): p. 609-617.

3. [cited 2011 November 3]; Available from: http://breastscreening.cancer.gov/data/performance/screening/2009/perf_age.html.

4. Gail, M.H., Value of adding single-nucleotide polymorphism genotypes to a breast cancer risk model. Journal of the National Cancer Institute, 2009. 101(13): p. 959-963

5. National Center for Health Statistics. National Health Interview Survey (NHIS). Center for Disease Control and Prevention, U.S. Department of Health and Human Services.

Conflict of Interest:

None declared

Potential impact of risk-based mammography screening - reply
Posted on July 26, 2012
Nicolien T. van Ravesteyn, MSc, Diana L. Miglioretti, PhD, Jeanne S. Mandelblatt, MD, MPH
Dept of Pub Health, Erasmus MC, Nthrlnds; Grp Health Res Inst, Grp Health Co-op/Dept of Biostats, Univ of Wash, Seattle; Dept of Oncology, Georgetown UMC/Cancer Control Prog, Lombardi Cancer Ctr
Conflict of Interest: None Declared

We thank Wu and colleagues for their letter and estimate of the number of women in their forties who might consider mammography based on risk. Their estimate is higher than that suggested by our models(1) combined with the review of risk factors in this age group(2). We found that women with a 2-fold increased risk can expect the same harm-benefit ratio from biennial screening in their forties as average-risk women starting at age 50(1). This level is found among women ages 40-49 with a first-degree relative with breast cancer (9%), women with extremely dense breasts (13%)(2) and an unknown percentage of women with combinations of lesser risk factors.

The difference in percentages between our study and Wu’s is primarily due to the comparison measure. Wu et al. use the absolute risk of 50-year-old women with no risk factors as the comparison measure. Also, the false-positive rate is stated to be ‘nearly the same’ in the age-group 40-49 vs. 50-54. Although indeed the difference between these two adjacent age-groups is not very large, multiple studies have shown that specificity increases with age(3-4). Consequently, their approach maximizes estimates of the number “at risk”, while minimizing the differences in false-positives. If guidelines followed their approach, and women in their forties at similar risk as the lowest risk 50-year-olds were screened, then the overall population harm-benefit ratio would increase.

In contrast, we compared benefits and harms of screening for a population of 40-49-year-olds to those of screening a population aged 50-74. Our approach takes into account the lower sensitivity and specificity for younger women, yielding less benefit and more harm for women in their forties even if they have the same absolute risk as older women. When adding screening starting at age 40 for women with a 2-fold increased risk, the harm-benefit ratio in the population would stay the same.

Thus, results from each approach are not analogous due to dissimilar methods, comparison measures and perspectives. The Gail model is intended to inform individual decision-making and our models portray the population impact of screening. Our study can be seen as a first step towards informing a more risk-based screening approach. To implement such an approach, it is necessary to identify eligible subgroups of women using either relative or absolute risk measures, taking into account not only risk, but also screening performance and perhaps practical considerations to facilitate compliance with the recommended screening approach.

REFERENCES

1. van Ravesteyn NT, Miglioretti DL, Stout NK, Lee SJ, Schechter CB, Buist DS, et al. Tipping the Balance of Benefits and Harms to Favor Screening Mammography Starting at Age 40 Years: A Comparative Modeling Study of Risk. Ann Intern Med. 2012;156(9):609-617.

2. Nelson HD, Zakher B, Cantor A, Fu R, Griffin J, O'Meara ES, et al. Risk Factors for Breast Cancer for Women Aged 40 to 49 Years: A Systematic Review and Meta-analysis. Ann Intern Med. 2012;156(9):635-48.

3. 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(3):168-75.

4. 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(10):1140-7.

Tipping the balance of benefits and harms to favor screening mammography starting at age 40 years
Posted on August 1, 2012
Helena M Verkooijen MD PhD, Ruud M Pijnappel MD PhD
Imaging division, University Medical Center Utrecht, the Netherlands
Conflict of Interest: None Declared

We would like to congratulate the authors with their work on determining a threshold risk at which the harm-benefit ratio of screening mammography for women aged 40 to 49 years equals that of biennial screening of women aged 50-75 years (1). The importance of this study is obvious, as the potential benefits of early detection of breast cancer are larger among young women who have more life years to gain. The authors conclude that starting biennial screening with digital mammography at age 40 years for women with a two-fold increased risk of breast cancer yields the same harm-benefit ratio as biennial screening of average-risk women aged 50-75 years.We have some questions regarding the assumptions for model input, in particular those for sensitivity and specificity. The authors used estimates for biennial digital mammography exceeding 90%. These estimates, which were obtained from the Breast Cancer Surveillance Consortium, are relatively high as compared to estimates of sensitivity among younger women reported in the literature, which vary between 49% - 82% (2-3) In a sensitivity analysis, the authors did consider alternative screening test characteristics, but only used the upper limits of the 95% confidence intervals for both sensitivity and specificity (i.e. best case scenario). This choice is a bit unusual, since we know that sensitivity usually goes down with increasing specificity, and vice versa.Have the authors performed sensitivity analyses adopting ‘worst (or less favorable) case scenarios’, or scenarios with increased specificity and decreased sensitivity, or vice versa. It would be important to know whether, and to what extent, threshold risks would change under these assumptions.

 References

1. van Ravesteyn NT, Miglioretti DL, Stout NK, Lee SJ, Schechter CB, Buist DS et al. Tipping the balance of benefits and harms to favor screening mammography starting at age 40 years. Ann Int Med 2012;156:609-617

2. Pisano ED, Gatsonis C, Hendrick E, Yaffe M, Baum JK, Acharyya S et al. Diagnostic performance of digital versus film mammography for breast cancer screening. New Engl J Med 2005;353:1773-17783

3. Smith-Bindman R, Chu P, Miglioretti DL, Quale C, Rosenberg RD, Cutter G, et al. Physician predictors of mammographic accuracy. J Natl Cancer Inst 2005;358-367

Screening of young, high-risk women should not be based on statistical modeling
Posted on August 7, 2012
Karsten J Joergensen
Nordic Cochrane Center
Conflict of Interest: None Declared

Statistical models estimating effects of health care interventions often require important assumptions, which is an inherent problem. To estimate if breast screening in high-risk women aged 40-49 has a favorable harm-benefit ratio, van Ravesteyn and colleagues assumed an effect of screening on breast cancer mortality, a rather central premise [1]. Their estimate was based on a systematic review, which found a 15% reduction [1]. The authors assumed that this effect would be the same in high-risk women as in those originally randomized. However, the effect in the randomized trials probably does not hold today, and the assumption of an identical effect in high- and average-risk women is also problematic.Most randomised breast screening trials were performed before adjuvant therapy was available. Adjuvant therapy is very effective, especially in younger women, and can also work when breast cancer has metastasized [2]. With more women surviving their breast cancers thanks to adjuvant therapy, there are fewer left to be “saved” by screening. In Europe, women in their 40’s have enjoyed reductions in breast cancer mortality that are often greater than in the United States [1,3]. But only few European countries screen women in their 40s, and those that do (e.g. Sweden) have not seen larger reductions than others [3]. Indeed, whether breast screening has any effect on breast cancer mortality today, in any age group, is doubtful [4]Counter-intuitively, breast screening may be particularly ineffective in high-risk women. First, fast-growing, aggressive cancers are the least likely to be found by screening. Regular breast screening preferentially detects slow-growing disease, simply because there is more time to detect it (length bias). Contrary, fast growing lesions with doubling times of a few months easily “slip through the screen” and appear between rounds. Second, high-risk women often have dense breast tissue, which means their cancers are harder to see. Third, more women identified with increased risk, e.g. due to a family history, will be BRCA mutation carriers. They have reduced ability to repair DNA damage and are therefore considerably more susceptible to radiation-induced cancers, particularly in young women whose cells are dividing [5]. Frequent screening of young, high-risk women could therefore do more harm than good.While targeted screening may seem like a good compromise in a climate of fierce debate, we lack up-to-date evidence. This cannot be provided by modeling studies and such a policy should therefore not be implemented outside a randomized trial.

References

1. van Ravesteyn, Miglioretti DL, Stout NK, Lee SJ, Schechter CB, Buist DSM, et al. Tipping the balance of benefits and harms to favor screening mammography starting at age 40 years. Ann Int Med 2012;156:609-17.

2. Early Breast Cancer Trialists’ Collaborative Group (EBCTCG) Effects of chemotherapy and hormonal therapy for early breast cancer on recurrence and 15-year survival: an overview of the randomised trials. Lancet 2005;365:1687–1717.

3. Autier P, Boniol M, LaVecchia C, Vatten L, Gavin A, Héry C, et al. Disparities in breast cancer mortality trends between 30 European countries: retrospective trend analysis of WHO mortality database. BMJ 2010;341:c3620.

4. Autier P, Koechlin A, Smans M, Vatten L, Boniol M. Mammography screening and breast cancer mortality in Sweden. JNCI 2012;104:1080-93.

5. de Gonzalez AB, Berg CD, Visvanathan K, Robson M. Estimated risk of radiation-induced breast cancer from mammographic screening for young BRCA mutation carriers. JNCI 2012;101:205-9.

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