Personalizing Mammography by Breast Density and Other Risk Factors for Breast Cancer: Analysis of Health Benefits and Cost-Effectiveness

University of California, San Francisco, San Francisco, California, United States
Annals of internal medicine (Impact Factor: 16.1). 07/2011; 155(1):10-20. DOI: 10.1059/0003-4819-155-1-201107050-00003
Source: PubMed

ABSTRACT Current guidelines recommend mammography every 1 or 2 years starting at age 40 or 50 years, regardless of individual risk for breast cancer.
To estimate the cost-effectiveness of mammography by age, breast density, history of breast biopsy, family history of breast cancer, and screening interval.
Markov microsimulation model.
Surveillance, Epidemiology, and End Results program, Breast Cancer Surveillance Consortium, and the medical literature.
U.S. women aged 40 to 49, 50 to 59, 60 to 69, and 70 to 79 years with initial mammography at age 40 years and breast density of Breast Imaging Reporting and Data System (BI-RADS) categories 1 to 4.
National health payer.
Mammography annually, biennially, or every 3 to 4 years or no mammography.
Costs per quality-adjusted life-year (QALY) gained and number of women screened over 10 years to prevent 1 death from breast cancer.
Biennial mammography cost less than $100,000 per QALY gained for women aged 40 to 79 years with BI-RADS category 3 or 4 breast density or aged 50 to 69 years with category 2 density; women aged 60 to 79 years with category 1 density and either a family history of breast cancer or a previous breast biopsy; and all women aged 40 to 79 years with both a family history of breast cancer and a previous breast biopsy, regardless of breast density. Biennial mammography cost less than $50,000 per QALY gained for women aged 40 to 49 years with category 3 or 4 breast density and either a previous breast biopsy or a family history of breast cancer. Annual mammography was not cost-effective for any group, regardless of age or breast density.
Mammography is expensive if the disutility of false-positive mammography results and the costs of detecting nonprogressive and nonlethal invasive cancer are considered.
Results are not applicable to carriers of BRCA1 or BRCA2 mutations.
Mammography screening should be personalized on the basis of a woman's age, breast density, history of breast biopsy, family history of breast cancer, and beliefs about the potential benefit and harms of screening.
Eli Lilly, Da Costa Family Foundation for Research in Breast Cancer Prevention of the California Pacific Medical Center, and Breast Cancer Surveillance Consortium.

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    • "They found that mammography led to a reduction in breast cancer mortality among women 40e74 years of age. Schousbe et al. studied the health benefits and cost-effectiveness of mammography [22]. Using a Markov microsimulation model, they evaluated various screening policies considering risk factors such as age, breast density, and family history of breast cancer. "
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    ABSTRACT: In this paper, we study breast cancer screening policies using computer simulation. We developed a multi-state Markov model for breast cancer progression, considering both the screening and treatment stages of breast cancer. The parameters of our model were estimated through data from the Canadian National Breast Cancer Screening Study as well as data in the relevant literature. Using computer simulation, we evaluated various screening policies to study the impact of mammography screening for age-based subpopulations in Canada. We also performed sensitivity analysis to examine the impact of certain parameters on number of deaths and total costs. The analysis comparing screening policies reveals that a policy in which women belonging to the 40-49 age group are not screened, whereas those belonging to the 50-59 and 60-69 age groups are screened once every 5 years, outperforms others with respect to cost per life saved. Our analysis also indicates that increasing the screening frequencies for the 50-59 and 60-69 age groups decrease mortality, and that the average number of deaths generally decreases with an increase in screening frequency. We found that screening annually for all age groups is associated with the highest costs per life saved. Our analysis thus reveals that cost per life saved increases with an increase in screening frequency. Copyright © 2015 Elsevier Ltd. All rights reserved.
    Breast (Edinburgh, Scotland) 04/2015; 24(4). DOI:10.1016/j.breast.2015.03.012 · 2.58 Impact Factor
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    • "Mammographic density (MD), a strong risk factor for breast cancer, is increasingly used as a phenotype risk marker in clinical, genetic and epidemiological studies (Boyd et al. 2011). Recently, MD has also been proposed as a key feature to tailor screening algorithms according to individual breast cancer risk (Schousboe et al. 2011; Evans et al. 2012). "
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    ABSTRACT: We developed a semi-automated tool to assess mammographic density (MD), a phenotype risk marker for breast cancer (BC), in full-field digital images and evaluated its performance testing its reproducibility, comparing our MD estimates with those obtained by visual inspection and using Cumulus, verifying their association with factors that influence MD, and studying the association between MD measures and subsequent BC risk. Three radiologists assessed MD using DM-Scan, the new tool, on 655 processed images (craniocaudal view) obtained in two screening centers. Reproducibility was explored computing pair-wise concordance correlation coefficients (CCC). The agreement between DM-Scan estimates and visual assessment (semi-quantitative scale, 6 categories) was quantified computing weighted kappa statistics (quadratic weights). DM-Scan and Cumulus readings were compared using CCC. Variation of DM-Scan measures by age, body mass index (BMI) and other MD modifiers was tested in regression mixed models with mammographic device as a random-effect term. The association between DM-Scan measures and subsequent BC was estimated in a case–control study. All BC cases in screening attendants (2007–2010) at a center with full-field digital mammography were matched by age and screening year with healthy controls (127 pairs). DM-Scan was used to blindly assess MD in available mammograms (112 cases/119 controls). Unconditional logistic models were fitted, including age, menopausal status and BMI as confounders. DM-Scan estimates were very reliable (pairwise CCC: 0.921, 0.928 and 0.916). They showed a reasonable agreement with visual MD assessment (weighted kappa ranging 0.79-0.81). DM-Scan and Cumulus measures were highly concordant (CCC ranging 0.80-0.84), but ours tended to be higher (4%-5% on average). As expected, DM-Scan estimates varied with age, BMI, parity and family history of BC. Finally, DM-Scan measures were significantly associated with BC (p-trend=0.005). Taking MD<7% as reference, OR per categories of MD were: OR7%-17%=1.32 (95% CI=0.59-2.99), OR17%-28%=2.28 (95% CI=1.03-5.04) and OR>=29%=3.10 (95% CI=1.35-7.14). Our results confirm that DM-Scan is a reliable tool to assess MD in full-field digital mammograms. Electronic supplementary material The online version of this article (doi:10.1186/2193-1801-2-242) contains supplementary material, which is available to authorized users.
    SpringerPlus 12/2013; 2(1):242. DOI:10.1186/2193-1801-2-242
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    • "The assumptions of the model are: A four-state progressive disease in which a subject may be in a disease-free state (S 0 ), preclinical disease state (S p : capable of being diagnosed by a special exam), clinical state (S c : diagnosis by symptomatic detection), and a death from BC state (S d ) Age-dependent transitions into the different states Age-dependent examination sensitivity Age-dependent sojourn times in each state Exam-diagnosed cases have a stage-shift in the direction of more favorable prognosis relative to the distribution of stages in symptomatic detection. Risk of invasive breast cancer Based on the literature [2] we estimated the distribution of the following BC risk factors in our population: breast density (measured using the BI-RADS categories 1 to 4), family history of BC in first degree relatives (yes/no) and personal history of breast biopsy (yes/no). "
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    ABSTRACT: Background: Breast cancer (BC) causes more deaths than any other cancer among women in Catalonia. Early detection of BC reduces mortality and may improve quality of life for most of the women diagnosed early by mammographic exams. Nevertheless, screening healthy women is expensive and may cause harm in many of them. The one-size-fits-all paradigm in organized screening programs for early detection of BC is starting to shift toward personalizing screening strategies based on BC risk. The aims of this study were: (1) To perform an economic evaluation and to assess the harm-benefit ratios of screening strategies that vary in their intensity and interval ages based on BC risk; and (2) To estimate the gain in terms of cost and harm reduction using risk-based screening with respect to the usual practice. Methods: We used a probabilistic model to estimate the effects and costs over time of each scenario. The effect was measured as lives extended, years of life gained, and quality-adjusted life years gained. The incremental cost-effectiveness ratio was used to compare the relative costs and outcomes of different scenarios. Results: We have found that risk-based screening strategies are more efficient and have lower harm-benefit ratios than uniform strategies. The results show that the most used screening strategy in Europe, biennial exams in the 50-69 age interval, or the alternative biennial exams in the 45-74 age interval, are both inefficient and can be improved upon if early detection is personalized. Conclusions: Mathematical models have been useful to assess the cost-effectiveness and harm-benefit ratios of BC screening. A reduced number of risk-based screening strategies can be selected for consideration by health agents. Risk-based screening seems to reduce harm and costs. It is necessary to develop accurate measures of individual risk of BC and to work on how to implement risk-based strategies. Keywords: Breast cancer; screening; cost-effectiveness analysis; harm-benefit analysis Acknowledgments: This study has been funded by grants PS09/01340 and PS09/01153 from the Health Research Fund (Fondo de Investigación Sanitaria) of the Spanish Ministry of Health. References: Schousboe JT, Kerlikowske K, Loh A, Cummings SR: Personalizing mammography by breast density and other risk factors for breast cancer: analysis of health benefits and cost-effectiveness. Ann Intern Med 2011, 155:1020. van Ravesteyn NT, Miglioretti DL, Stout NK, Lee SJ, Schechter CB, Buist DS, Huang H, Heijnsdijk EA, Trentham-Dietz A, Alagoz O, Near AM, Kerlikowske K, Nelson HD, Mandelblatt JS, de Koning HJ: 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, 156:609617. Lee S, Zelen M: Scheduling periodic examinations for the early detection of disease: applications to breast cancer. J Am Stat Assoc 1998, (93):12711281. Lee S, Zelen M: A stochastic model for predicting the mortality of breast cancer. J Natl Cancer Inst Monogr 2006, (36):7986. Lee SJ, Zelen M: Mortality modeling of early detection programs. Biometrics 2008, 64:386-395. Barlow WE, White E, Ballard-Barbash R, Vacek PM, Titus-Ernstoff L, Carney PA, Tice JA, Buist DSM, Geller BM, Rosenberg R, Yankaskas BC, Kerlikowske K: Prospective breast cancer risk prediction model for women undergoing screening mammography. J Natl Cancer Inst. 2006; 98:1204-1214. Lidgren M, Wilking N, Jonsson B, Rehnberg C: Health related quality of life in different states of breast cancer. Qual Life Res 2007, 16:10731081. Tice JA, Cummings SR, Smith-Bindman R, Ichikawa L, Barlow WE, Kerlikowske K: Using clinical factors and mammographic breast density to estimate breast cancer risk: development and validation of a new predictive model. Ann Intern Med 2008, 148:337347
    2ª Reunión General Biostatnet, Santiago de Compostela (Spain); 01/2013
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