Prospective Breast Cancer Risk Prediction Model for Women Undergoing Screening Mammography

Department of Medicine, University of California, San Francisco, San Francisco, California, United States
Journal of the National Cancer Institute (Impact Factor: 12.58). 09/2006; 98(17):1204-14. DOI: 10.1093/jnci/djj331
Source: PubMed


Risk prediction models for breast cancer can be improved by the addition of recently identified risk factors, including breast density and use of hormone therapy. We used prospective risk information to predict a diagnosis of breast cancer in a cohort of 1 million women undergoing screening mammography.
There were 2,392,998 eligible screening mammograms from women without previously diagnosed breast cancer who had had a prior mammogram in the preceding 5 years. Within 1 year of the screening mammogram, 11,638 women were diagnosed with breast cancer. Separate logistic regression risk models were constructed for premenopausal and postmenopausal examinations by use of a stringent (P<.0001) criterion for the inclusion of risk factors. Risk models were constructed with 75% of the data and validated with the remaining 25%. Concordance of the predicted with the observed outcomes was assessed by a concordance (c) statistic after logistic regression model fit. All statistical tests were two-sided.
Statistically significant risk factors for breast cancer diagnosis among premenopausal women included age, breast density, family history of breast cancer, and a prior breast procedure. For postmenopausal women, the statistically significant factors included age, breast density, race, ethnicity, family history of breast cancer, a prior breast procedure, body mass index, natural menopause, hormone therapy, and a prior false-positive mammogram. The model may identify high-risk women better than the Gail model, although predictive accuracy was only moderate. The c statistics were 0.631 (95% confidence interval [CI] = 0.618 to 0.644) for premenopausal women and 0.624 (95% CI = 0.619 to 0.630) for postmenopausal women.
Breast density is a strong additional risk factor for breast cancer, although it is unknown whether reduction in breast density would reduce risk. Our risk model may be able to identify women at high risk for breast cancer for preventive interventions or more intensive surveillance.

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Available from: William E Barlow, Nov 16, 2015
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    • "Many studies have investigated the association of mammographic density with breast cancer risk and found it to be a strong independent risk factor [1] [2] [3] [4]. However, the conclusion is mainly reached on the basis of large epidemiology studies from a statistical perspective, and the most noticeable difference in risks is between women with extremely dense breast compared to women with almost entirely fatty breast [24] [25] [26] [27]. The current knowledge and direct evidence regarding whether breast tumors arise directly within the dense breast tissue is still lacking [2] [6] [7]. "
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    • "Restricting cost-effectiveness analysis to genetically susceptible women offers limited information to decision makers who are making policy recommendations for all high-risk women because breast cancer incidence in BRCA1/2 mutation carriers is just B4% per annum in the age group of 40–49 years (Antoniou et al, 2003). In addition, ongoing efforts have been made to improve existing prediction models for women at high risk using risk factors other than BRCA mutational status that can be obtained during routine screening examinations (Barlow et al, 2006; Cummings et al, 2009). All of this evidence supports the importance of assessing the cost-effectiveness of MRI in a broader group of patients than BRCA1/2 mutation carriers. "
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