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: 17.81). 07/2011; 155(1):10-20. DOI: 10.1059/0003-4819-155-1-201107050-00003
Source: PubMed


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 · 2.38 Impact Factor
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    • "Nevertheless, breast density appears to play a lesser role in false negatives, in line with previous series [13,37]. Breast density remains a major issue in breast cancer screening because it is one of the variables proposed to tailor screening [38]. Information on its role among interval cancer categories along with data on its relationship with tumor phenotypes may be useful to estimate the potential benefit of personalizing screening strategies on the basis of this factor. "
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    ABSTRACT: Interval cancers are tumors arising after a negative screening episode and before the next screening invitation. They can be classified into true interval cancers, false-negatives, minimal-sign cancers, and occult tumors based on mammographic findings in a retrospective review of screening and diagnostic mammograms. This study aimed to describe tumor-related characteristics and the association of breast density and tumor phenotype within four interval cancer categories. We included 2,245 invasive tumors (1,297 screen-detected and 948 interval cancers) diagnosed from 2000-2009 among 645,764 women aged 45-69 who underwent biennial screening in Spain. Interval cancers were classified by a semi-informed retrospective review of screening and diagnostic mammograms into true interval cancers (n = 455), false-negatives (n = 224), minimal-sign (n = 166), and occult tumors (n = 103). Breast density was evaluated using Boyd's scale and was conflated into: <25%; 25-50%; 50-75%; >75%. Tumor-related information was obtained from cancer registries and clinical records. Tumor phenotype was defined as follows: luminal A: ER+/HER2- or PR+/HER2-; luminal B: ER+/HER2+ or PR+/HER2+; HER2: ER-/PR-/HER2+; triple-negative: ER-/PR-/HER2-. The association of tumor phenotype and breast density in each type of interval cancer was assessed using a multinomial logistic regression model. Adjusted odds ratios (OR) and 95% confidence intervals (95%CI) were calculated. All statistical tests were two-sided. Forty-eight percent of interval cancers were true interval cancers and 23.6% false-negatives. True interval cancers were associated with HER2 and triple-negative phenotypes [OR = 1.91 (95%CI:1.22-2.96), OR = 2.07 (95%CI:1.42-3.01), respectively] and extremely dense breasts (>75%) [OR = 1.67 (95%CI:1.08-2.56)]. However, among true interval cancers the highest proportion of triple-negative tumors was observed in predominantly fatty breasts (<25%) than in denser breasts (28.7%, 21.4%, 11.3% and 14.3%, respectively;<0.001). False-negatives and occult tumors had similar phenotypic characteristics to screen-detected cancers, extreme breast density being strongly associated with occult tumors [OR = 6.23 (95%CI:2.65-14.66)]. Minimal-sign cancers were biologically close to true interval cancers but showed no association with breast density. Our findings revealed that both the distribution of tumor phenotype and breast density play specific and independent roles in each category of interval cancer. Further research is needed to understand the biological basis of the overrepresentation of triple-negative phenotype among predominantly fatty breasts in true interval cancers.
    Breast cancer research: BCR 01/2014; 16(1):R3. DOI:10.1186/bcr3595 · 5.49 Impact Factor
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    • "In the USA, the National Cancer Institute started an initiative, the Cancer Intervention and Surveillance Modeling Network (CISNET), with the goal of evaluating the impact of screening and adjuvant treatments on BC incidence and mortality [6-8]. Recently, in a cost-effectiveness study, Schousboe et al. [9] have proposed different screening periodicities based on BC risk, measured as a function of breast density, family history of BC and previous breast biopsy. "
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    ABSTRACT: The aim of this study was to evaluate the calibration and discriminatory power of three predictive models of breast cancer risk. We included 13,760 women who were first-time participants in the Sabadell-Cerdanyola Breast Cancer Screening Program, in Catalonia, Spain. Projections of risk were obtained at three and five years for invasive cancer using the Gail, Chen and Barlow models. Incidence and mortality data were obtained from the Catalan registries. The calibration and discrimination of the models were assessed using the Hosmer-Lemeshow C statistic, the area under the receiver operating characteristic curve (AUC) and the Harrell's C statistic. The Gail and Chen models showed good calibration while the Barlow model overestimated the number of cases: the ratio between estimated and observed values at 5 years ranged from 0.86 to 1.55 for the first two models and from 1.82 to 3.44 for the Barlow model. The 5-year projection for the Chen and Barlow models had the highest discrimination, with an AUC around 0.58. The Harrell's C statistic showed very similar values in the 5-year projection for each of the models. Although they passed the calibration test, the Gail and Chen models overestimated the number of cases in some breast density categories. These models cannot be used as a measure of individual risk in early detection programs to customize screening strategies. The inclusion of longitudinal measures of breast density or other risk factors in joint models of survival and longitudinal data may be a step towards personalized early detection of BC.
    BMC Cancer 12/2013; 13(1):587. DOI:10.1186/1471-2407-13-587 · 3.36 Impact Factor
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