- Research article
- Open Access
- Open Peer Review
Cost-effectiveness of MRI compared to mammography for breast cancer screening in a high risk population
- Susan G Moore1,
- Pareen J Shenoy†1,
- Laura Fanucchi1,
- John W Tumeh†1, 2 and
- Christopher R Flowers†1Email author
© Moore et al; licensee BioMed Central Ltd. 2009
- Received: 27 February 2008
- Accepted: 13 January 2009
- Published: 13 January 2009
Breast magnetic resonance imaging (MRI) is a sensitive method of breast imaging virtually uninfluenced by breast density. Because of the improved sensitivity, breast MRI is increasingly being used for detection of breast cancer among high risk young women. However, the specificity of breast MRI is variable and costs are high. The purpose of this study was to determine if breast MRI is a cost-effective approach for the detection of breast cancer among young women at high risk.
A Markov model was created to compare annual breast cancer screening over 25 years with either breast MRI or mammography among young women at high risk. Data from published studies provided probabilities for the model including sensitivity and specificity of each screening strategy. Costs were based on Medicare reimbursement rates for hospital and physician services while medication costs were obtained from the Federal Supply Scale. Utilities from the literature were applied to each health outcome in the model including a disutility for the temporary health state following breast biopsy for a false positive test result. All costs and benefits were discounted at 5% per year. The analysis was performed from the payer perspective with results reported in 2006 U.S. dollars. Univariate and probabilistic sensitivity analyses addressed uncertainty in all model parameters.
Breast MRI provided 14.1 discounted quality-adjusted life-years (QALYs) at a discounted cost of $18,167 while mammography provided 14.0 QALYs at a cost of $4,760 over 25 years of screening. The incremental cost-effectiveness ratio of breast MRI compared to mammography was $179,599/QALY. In univariate analysis, breast MRI screening became < $50,000/QALY when the cost of the MRI was < $315. In the probabilistic sensitivity analysis, MRI screening produced a net health benefit of -0.202 QALYs (95% central range: -0.767 QALYs to +0.439 QALYs) compared to mammography at a willingness-to-pay threshold of $50,000/QALY. Breast MRI screening was superior in 0%, < $50,000/QALY in 22%, > $50,000/QALY in 34%, and inferior in 44% of trials.
Although breast MRI may provide health benefits when compared to mammographic screening for some high risk women, it does not appear to be cost-effective even at willingness to pay thresholds above $120,000/QALY.
- Breast Cancer
- National Comprehensive Cancer Network
- National Comprehensive Cancer Network
- Quality Adjusted Life Year
- Probabilistic Sensitivity Analysis
In the United States, one in eight women will be diagnosed with breast cancer during her lifetime . In 2008, an estimated 182,460 cases of breast cancer will occur, accounting for 26% of all cancer cases in women  Current consensus screening recommendations divide women into normal and high-risk categories after using physical examination and clinical judgment as a starting point . According to the National Comprehensive Cancer Network (NCCN) Clinical Practice Guidelines [2, 3] women at increased risk of breast cancer include those with (i) a history of thoracic or mantle irradiation, (ii) a strong family history or genetic predisposition, (iii) lobular carcinoma in situ or atypical hyperplasia, (iv) a prior history of breast cancer, and/or (v) those over 35 years of age with a 5-year risk of invasive breast cancer ≥ 1.7% according to the modified Gail Model. This model calculates risk based on current age, age at menarche, age at first live birth, nulliparity, previous breast biopsies, atypical hyperplasia, and race, though it has not been conclusively validated in non-Caucasian women . The 5-year risk of ≥ 1.7% is the average risk of a women at the median age of breast cancer diagnosis in the United States . Women with a strong family history or genetic disposition are defined as those with BRCA1/BRCA2 mutations, or a personal family history of breast cancer and one of several other familial risk categories, including being diagnosed before age 40, or before age 50 with one or more close blood relative with breast cancer, or a close family member meeting any of the other criteria . It has been estimated that the risk of developing breast cancer in those with BRCA1 or BRCA2 mutations is 45% to 65% respectively .
The screening algorithms for women at increased risk are based on the five aforementioned categories. For women under 25 years of age with a strong family history or genetic predisposition, the recommendation is for annual clinical breast examinations and regular breast self-examination starting at age 18 years [5–7]. The NCCN screening recommendations for women ≥ 25 years in this risk category include annual mammogram and breast MRI screening starting at age 25, or based on earliest age of onset in the family, consideration of prophylactic mastectomy, consideration of chemoprevention options, and consideration of investigational imaging and screening studies . Screening mammography has been shown to reduce mortality from breast cancer by approximately 24% in women between the ages of 50 and 70 . One modelling study found that screening decreased the mortality from breast cancer by 7% – 23%, and that when combined with adjuvant therapy, the rate declined by 25% – 38% [9–12]. Despite some conflicting evidence, screening recommendations endorse annual mammography in normal risk women starting at age 40 years [9–12]. In high-risk women, who tend to develop breast cancer at earlier ages, however, mammography screening is less sensitive, largely due to problems detecting cancer in dense breast tissue. In several studies of high-risk women, including those with BRCA1 and BRCA2 mutations, yearly screening mammography had sensitivities ranging from 25% to 36% . Furthermore, observational studies of BRCA mutation carriers suggest that 50% of breast cancers in this population present between screening mammograms [4, 7].
MRI is not affected by breast density, and the recent inclusion of breast MRI in the screening guidelines is based on studies suggesting that high-risk women may benefit from MRI screening . Several studies have reported the sensitivity of MRI screening in high-risk women to be between 77% and 91% . Screening with MRI has also been shown to detect breast cancer in earlier stages in high-risk women . Unfortunately, spontaneous hormone-induced enhancement may occur, leading to false positive test results and unnecessary biopsies in women screened by MRI over mammography. Accordingly, MRI has lower specificity of 90% as compared to 95% for mammography .
Though MRI is more sensitive than mammography in a high-risk population, it has not yet been shown to reduce mortality . MRI is also approximately 10 times more expensive than mammography and, due to the comparatively lower specificity, leads to increased costs in the form of potentially unnecessary diagnostic examinations, biopsies, and anxiety . Although, based on existing evidence, current screening guidelines recommend consideration of MRI screening in this high-risk population , its use remains controversial.
Cost-effectiveness analysis, however, can play an important role to help determine the role of MRI in screening women at high-risk for breast cancer. The objective of this study is to determine the cost-effectiveness of MRI in screening women with a ≥ 15% cumulative lifetime risk of breast cancer by using a Markov decision model in a hypothetical cohort of patients.
Probabilities used in the model
True Positive BI-RADS 0/3
False Positive BI-RADS 0/3
False Negative Node Positive
Live Node Positive
Live Node Negative
Live no cancer
Costs used in the model.
Local Therapy (Node negative) – Pre-op Evaluation, Lumpectomy with SN biopsy, Lumpectomy Re-excision, WBRT-B post lumpectomy (Konski), Mastectomy with SN biopsy, Breast Reconstruction
8,387.27 – 19,405.81
Local Therapy (Node positive) – Pre-op Evaluation, Lumpectomy with SN biopsy/Axillary dissection, Lumpectomy Re-excision, WBRT-B post lumpectomy (Konski), Mastectomy with SN biopsy/Axillary dissection, Breast Reconstruction
Bilateral Mammography (Screening)
33.23 – 73.65
646.60 – 1,432.84
28.37 – 62.88
Work Up – Ultrasound of Breast, Mammogram of One Breast, FNA Without Imaging, FNA With Imaging, Ultrasound-Guided Core Biopsy
435.49 – 832.66
Systemic Node Positive – CBC, CMP Office/Outpatient Visit New and Established, Heart First Pass (Single), Doxarubicin 60 mg/m2, Cyclophosphamide 600 mg/m2, Tamoxifen 180 tabs (Node Pos), Paclitaxel 175 mg/m2, Trastuzumab 4 mg/kg × 1 = 272 mg (2/3 vial over 90 minutes)
Mammogram BI-RADS 0/3 False Positive
28.37 – 62.88
MRI BI-RADS 0/3 False Positive
Utilities and discount rate used in the model
0.907 – 0.993
0.722 – 0.878
0.829 – 0.951
0.970 – 1.000
False Negative Node Positive
0.567 – 0.753
0.00 – 0.050
Univariate sensitivity analyses were performed on each individual cost, probability and utility in order to explore the effect that variation in model parameters can have on the incremental cost-effectiveness of the MRI strategy. Probabilities and utilities were varied over the ranges derived from their 95% confidence intervals. Variations in costs were based on estimated minimums and maximums from Medicare reimbursement data for hospital, physician, and laboratory services according to the methodology described in recently published work . Costs for drugs were varied according to the minimum and maximum medication costs from the FSS. Both low and high incremental cost-effectiveness ratios (ICERs) were recorded in univariate analyses and parameters were varied across their distributions in probabilistic sensitivity analyses. Net health benefit assessments were performed using a $50,000/QALY willingness-to-pay threshold and alternative threshold values were examined. The values of individual model parameters above or below which MRI became cost-effective were recorded as thresholds. In addition to the univariate sensitivity analyses, a probabilistic sensitivity analysis was performed with 10,000 Monte Carlo simulations to assess the robustness of the findings in the base case. Confidence ranges for the incremental cost and effectiveness of both screening strategies were recorded. Normal distributions were used with base case values serving as the mean and standard deviations calculated from the high and low ranges for each parameter.
Costs, quality-adjusted life years, cost-effectiveness ratio, and incremental cost-effectiveness ratio of the screening regimens over 25 years of screening
Breast MRI may provide health benefits when compared to mammographic screening for some high-risk women; however, this approach does not appear to be cost-effective at a willingness-to-pay threshold of $50,000/QALY. This historical threshold is based on the cost of providing care to patients with end-stage renal disease in the 1970s, which now exceeds $120,000/QALY. Given the increased costs due to increased technology over this time period, and the benefits to be gained from the use of technological advancements, it follows that a higher threshold would be more appropriate and relevant. In this model, MRI screening does not approach cost-effectiveness even if a threshold of $120,000/QALY is used.
In this study, a series of univariate sensitivity analyses were conducted to explore the impact of varying all resource costs, probabilities, and utilities on the incremental cost-effectiveness of MRI screening. Our model showed that MRI screening became more cost-effective as the cost of MRI decreased and the cost of mammography increased. The cost-effectiveness of MRI screening in this model strongly depended on several factors, including the likelihood of survival with node-negative breast cancer, survival with node-positive breast cancer, positive mammography reading, and positive MRI reading. Therefore, the model suggests that screening with MRI becomes more cost-effective for patients with higher-risk profiles, and as the positive predictive value of MRI screening increases. Additionally, a probabilistic sensitivity analysis was performed to assess the robustness of the findings in the base case. The net health benefits of MRI screening relative to mammography improve as the willingness-to-pay threshold approaches $120,000/QALY, but even in this instance, it did not become cost effective for this population.
Other models have shown that MRI screening may be cost-effective in high-risk women, particularly those with BRCA1 and BRCA2 mutations. A study, by the UK Magnetic Resonance Imaging in Breast Screening Study Group, of 279 women at high familial risk for breast cancer found that the incremental cost per detected cancer in women with BRCA1 and BRCA2 mutations (n = 117) was £11,800 (2007 US $24,268) for contrast-enhanced MRI combined with mammography and £15,300 (2007 US $31,466) for contrast-enhanced MRI alone compared with mammography alone . This study included women aged 35–49 years who tested positive or had a relative with BRCA1/BRCA2/TP53 mutation or had strong family history of breast/ovarian cancer. Also this study differs from our model in that, this study evaluated the cost effectiveness of MRI alone, mammography alone, and mammography in combination with MRI.
A recent cost-effectiveness analysis in the US by the Cancer Intervention and Surveillance Modeling Network consortium, in a simulated cohort of 25 year-old BRCA1 or BRCA2 mutation carriers born in 1980, found that using a threshold of $100,000/QALY gained resulted in MRI plus mammography screening being cost-effective from ages 35–54 in women with BRCA1 mutations ($89,661/QALY; the most cost-effective model in this group was $43,484/QALY for BRCA1 carriers ages 40–49), and for women with BRCA2 mutations < 50 years of age with extremely dense breasts on mammography ($98,454/QALY) [11, 26]. Our study differs from this study in the patient cohort; we include women at high risk as per the Claus tables whereas this study only includes women with BRCA1/BRCA2 mutations. Also, this study evaluates the cost effectiveness of mammography alone compared to mammography plus MRI screening. In addition, the probabilities and utilities used in both the above studies are different from those used in our model.
Our study has some limitations that must be addressed. There are additional issues relevant to the management of women at high-risk for breast cancer that were not incorporated in the model, and may influence the cost-effectiveness of screening with MRI. For example, although BRCA mutation carriers may choose to undergo prophylactic mastectomy, many do not choose this option, with estimates ranging from 0% to 54% of carriers [11, 26]. Furthermore, some of the women are also at increased risk for ovarian cancer. The costs of radiation exposure due to annual mammography starting at an earlier age were not incorporated, nor were the costs of possible anxiety and stress from unnecessary biopsies stemming from false positive MRI screening. Any or all of these factors might alter the cost-effectiveness estimate. Finally, the results of our model should be interpreted with care given that the results of this cost-effectiveness analysis require comparisons to data from observational studies, the Surveillance, Epidemiology and End Results Program, or clinical trials.
All probabilities and utilities used to populate the model are estimates derived from the literature. Each of these estimates carries inherent uncertainty, as does using a hypothetical cohort. Possible selection bias associated with utilizing the Claus tables may affect our base case effectiveness and resource use estimates by either over or underestimating our base case model parameters. Moreover, in our probabilistic sensitivity analysis, we did not assume that a correlation structure existed among the distributions of the parameters. However, both univariate and probabilistic sensitivity analyses were performed to address uncertainty in parameter estimates by exploring variability in each probability, cost, and outcome estimate.
Although the NCCN screening guidelines for women aged 25 years and older at high-risk for breast cancer include breast MRI as an adjunctive screening tool to mammograms, breast MRI has not yet been shown to decrease mortality. Further research into the appropriate role and cost-effectiveness of screening breast MRI will better elucidate which specific risk groups are more likely to benefit from MRI screening.
This work was supported by grant funding from Dr. Flowers' Georgia Cancer Coalition Distinguished Scholar Award, PhRMA Health Outcomes Research Award, and the Amos Medical Faculty Development Program Award from the American Society of Hematology and Robert Wood Johnson Foundation.
- Jemal A, Siegel R, Ward E, Hao Y, Xu J, Murray T, Thun MJ: Cancer Statistics, 2008. CA Cancer J Clin. 2008, 58: 71-96. 10.3322/CA.2007.0010.View ArticlePubMedGoogle Scholar
- Bevers TB, Anderson BO, Bonaccio E, Borgen PI, Buys S, Daly MB, Dempsey PJ, Farrar WB, Fleming I, Garber JE, et al: Breast cancer screening and diagnosis. J Natl Compr Canc Netw. 2006, 4: 480-508.PubMedGoogle Scholar
- Gail MH, Brinton LA, Byar DP, Corle DK, Green SB, Schairer C, Mulvihill JJ: Projecting Individualized Probabilities of Developing Breast Cancer for White Females Who Are Being Examined Annually. J Natl Cancer Inst. 1989, 81: 1879-1886. 10.1093/jnci/81.24.1879.View ArticlePubMedGoogle Scholar
- National Comprehensive Cancer Network: Practice Guidelines in Oncology – Genetic/Familial High-Risk Assessment: Breast and Ovarian. 2005, National Comprehensive Cancer Network, Inc, 1.Google Scholar
- Nystrom L, Andersson I, Bjurstam N, Frisell J, Nordenskjold B, Rutqvist LE: Long-term effects of mammography screening: updated overview of the Swedish randomised trials. Lancet. 2002, 359: 909-919. 10.1016/S0140-6736(02)08020-0.View ArticlePubMedGoogle Scholar
- Tabar L, Vitak B, Chen HH, Yen MF, Duffy SW, Smith RA: Beyond randomized controlled trials: organized mammographic screening substantially reduces breast carcinoma mortality. Cancer. 2001, 91: 1724-1731. 10.1002/1097-0142(20010501)91:9<1724::AID-CNCR1190>3.0.CO;2-V.View ArticlePubMedGoogle Scholar
- Le-Petross HT: Breast MRI as a screening tool: the appropriate role. J Natl Compr Canc Netw. 2006, 4: 523-526.PubMedGoogle Scholar
- Berry DA, Cronin KA, Plevritis SK, Fryback DG, Clarke L, Zelen M, Mandelblatt JS, Yakovlev AY, Habbema JD, Feuer EJ: Effect of screening and adjuvant therapy on mortality from breast cancer. N Engl J Med. 2005, 353: 1784-1792. 10.1056/NEJMoa050518.View ArticlePubMedGoogle Scholar
- Lehman CD, Blume JD, Weatherall P, Thickman D, Hylton N, Warner E, Pisano E, Schnitt SJ, Gatsonis C, Schnall M, DeAngelis GA, Stomper P, Rosen EL, O'Loughlin M, Harms S, Bluemke DA: Screening women at high risk for breast cancer with mammography and magnetic resonance imaging. Cancer. 2005, 103: 1898-1905. 10.1002/cncr.20971.View ArticlePubMedGoogle Scholar
- Warner E, Plewes DB, Hill KA, Causer PA, Zubovits JT, Jong RA, Cutrara MR, DeBoer G, Yaffe MJ, Messner SJ, Meschino WS, Piron CA, Narod SA: Surveillance of BRCA1 and BRCA2 mutation carriers with magnetic resonance imaging, ultrasound, mammography, and clinical breast examination. Jama. 2004, 292: 1317-1325. 10.1001/jama.292.11.1317.View ArticlePubMedGoogle Scholar
- Kriege M, Brekelmans CT, Boetes C, Besnard PE, Zonderland HM, Obdeijn IM, Manoliu RA, Kok T, Peterse H, Tilanus-Linthorst MM, Muller SH, Meijer S, Oosterwijk JC, Beex LV, Tollenaar RA, de Koning HJ, Rutgers EJ, Klijn JG: Efficacy of MRI and mammography for breast-cancer screening in women with a familial or genetic predisposition. N Engl J Med. 2004, 351: 427-437. 10.1056/NEJMoa031759.View ArticlePubMedGoogle Scholar
- Kuhl CK, Schrading S, Leutner CC, Morakkabati-Spitz N, Wardelmann E, Fimmers R, Kuhn W, Schild HH: Mammography, breast ultrasound, and magnetic resonance imaging for surveillance of women at high familial risk for breast cancer. J Clin Oncol. 2005, 23: 8469-8476. 10.1200/JCO.2004.00.4960.View ArticlePubMedGoogle Scholar
- Plevritis SK, Kurian AW, Sigal BM, Daniel BL, Ikeda DM, Stockdale FE, Garber AM: Cost-effectiveness of screening BRCA1/2 mutation carriers with breast magnetic resonance imaging. JAMA. 2006, 295: 2374-2384. 10.1001/jama.295.20.2374.View ArticlePubMedGoogle Scholar
- Tumeh JW, Moore SG, Shapiro R, Flowers CR: Practical approach for using Medicare data to estimate costs for cost-effectiveness analysis. Expert Review of Pharmacoeconomics & Outcomes Research. 2005, 5: 153-162. 10.1586/1473722.214.171.124.View ArticleGoogle Scholar
- Griebsch I, Brown J, Boggis C, Dixon A, Dixon M, Easton D, Eeles R, Evans DG, Gilbert FJ, Hawnaur J, Kessar P, Lakhani SR, Moss SM, Nerurkar A, Padhani AR, Pointon LJ, Potterton J, Thompson D, Turnbull LW, Walker LG, Warren R, Leach MO: Cost-effectiveness of screening with contrast enhanced magnetic resonance imaging vs X-ray mammography of women at a high familial risk of breast cancer. Br J Cancer. 2006, 95: 801-810. 10.1038/sj.bjc.6603356.View ArticlePubMedPubMed CentralGoogle Scholar
- Wainberg S, Husted J: Utilization of screening and preventive surgery among unaffected carriers of a BRCA1 or BRCA2 gene mutation. Cancer Epidemiol Biomarkers Prev. 2004, 13: 1989-1995.PubMedGoogle Scholar
- Arias E: United States life tables, 2002. National vital statistics reports. 2004, Hyattsville, Maryland: National Center for Health Statistics, 53.Google Scholar
- 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. 10.1016/S0140-6736(05)66544-0.View ArticleGoogle Scholar
- van Roosmalen MS, Verhoef LCG, Stalmeier PFM, Hoogerbrugge N, van Daal WAJ: Decision Analysis of Prophylactic Surgery or Screening for BRCA1 Mutation Carriers: A More Prominent Role For Oophorectomy. J Clin Oncol. 2002, 20: 2092-2100. 10.1200/JCO.2002.08.035.View ArticlePubMedGoogle Scholar
- Messecar DC: Mammography screening for older women with and without cognitive impairment. J Gerontol Nurs. 2000, 26: 14-24.View ArticlePubMedGoogle Scholar
- de Haes JCJM, de Koning HJ, van Oortmarssen GJ, van Agt HME, de Bruyn AE, van der Maas PJ: The impact of a breast cancer screening programme on quality-adjusted life-years. International Journal of Cancer. 1991, 49: 538-544. 10.1002/ijc.2910490411.View ArticlePubMedGoogle Scholar
- Bernhard J, Zahrieh D, Coates AS, Gelber RD, Castiglione-Gertsch M, Murray E, Forbes JF, Perey L, Collins J, Snyder R, Rudenstam CM, Crivellari D, Veronesi A, Thurlimann B, Fey MF, Price KN, Goldhirsch A, Hurny C: Quantifying trade-offs: quality of life and quality-adjusted survival in a randomised trial of chemotherapy in postmenopausal patients with lymph node-negative breast cancer. Br J Cancer. 2004, 91: 1893-1901. 10.1038/sj.bjc.6602230.View ArticlePubMedPubMed CentralGoogle Scholar
- Levin HM, McEwan PJ: Cost-Effectiveness Analysis: Methods and Applications. 2000, Sage Publications, Inc, 2Google Scholar
- Bonneterre J, Bercez C, Bonneterre ME, Lenne X, Dervaux B: Cost-effectiveness analysis of breast cancer adjuvant treatment: FEC 50 versus FEC 100 (FASG05 study). Ann Oncol. 2005, 16: 915-922. 10.1093/annonc/mdi195.View ArticlePubMedGoogle Scholar
- Brouwer W, van Hout B, Rutten F: A fair approach to discounting future effects: taking a societal perspective. J Health Serv Res Policy. 2000, 5: 114-118.PubMedGoogle Scholar
- van Asperen CJ, Tollenaar RAEM, Krol-Warmerdam EMM, Blom J, Hoogendoorn WE, Seynaeve CMJC, Brekelmans CTM, Devilee P, Cornelisse CJ, Klijn JGM, de Bock GH: Possible consequences of applying guidelines to healthy women with a family history of breast cancer. Eur J Hum Genet. 2003, 11: 633-636. 10.1038/sj.ejhg.5201021.View ArticlePubMedGoogle Scholar
- The pre-publication history for this paper can be accessed here:http://0-www.biomedcentral.com.brum.beds.ac.uk/1472-6963/9/9/prepub
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