Using Clinical Factors and Mammographic Breast Density to Estimate Breast Cancer Risk: Development and Validation of a New Predictive Model

Division of General Internal Medicine, Department of Medicine, University of California, San Francisco, San Francisco, California 94143-1732, USA.
Annals of internal medicine (Impact Factor: 17.81). 04/2008; 148(5):337-47. DOI: 10.7326/0003-4819-148-5-200803040-00004
Source: PubMed


Current models for assessing breast cancer risk are complex and do not include breast density, a strong risk factor for breast cancer that is routinely reported with mammography.
To develop and validate an easy-to-use breast cancer risk prediction model that includes breast density.
Empirical model based on Surveillance, Epidemiology, and End Results incidence, and relative hazards from a prospective cohort.
Screening mammography sites participating in the Breast Cancer Surveillance Consortium.
1,095,484 women undergoing mammography who had no previous diagnosis of breast cancer.
Self-reported age, race or ethnicity, family history of breast cancer, and history of breast biopsy. Community radiologists rated breast density by using 4 Breast Imaging Reporting and Data System categories.
During 5.3 years of follow-up, invasive breast cancer was diagnosed in 14,766 women. The breast density model was well calibrated overall (expected-observed ratio, 1.03 [95% CI, 0.99 to 1.06]) and in racial and ethnic subgroups. It had modest discriminatory accuracy (concordance index, 0.66 [CI, 0.65 to 0.67]). Women with low-density mammograms had 5-year risks less than 1.67% unless they had a family history of breast cancer and were older than age 65 years.
The model has only modest ability to discriminate between women who will develop breast cancer and those who will not.
A breast cancer prediction model that incorporates routinely reported measures of breast density can estimate 5-year risk for invasive breast cancer. Its accuracy needs to be further evaluated in independent populations before it can be recommended for clinical use.

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Available from: William E Barlow
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    • "The web tool, developed by Drs. Ozanne and Esserman and colleagues at UCSF, generates tailored patient-specific risk assessments using established risk models (including Gail, BRCApro, Claus, BCSC density) based on patient data [29-32]. Consultants from MAYA Viz, a company focused on web design, provided format and style guidance and aided the development of the tool’s information architecture [33]. "
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    ABSTRACT: Breast cancer risk reduction has the potential to decrease the incidence of the disease, yet remains underused. We report on the development a web-based tool that provides automated risk assessment and personalized decision support designed for collaborative use between patients and clinicians. Under Institutional Review Board approval, we evaluated the decision tool through a patient focus group, usability testing, and provider interviews (including breast specialists, primary care physicians, genetic counselors). This included demonstrations and data collection at two scientific conferences (2009 International Shared Decision Making Conference, 2009 San Antonio Breast Cancer Symposium). Overall, the evaluations were favorable. The patient focus group evaluations and usability testing (N = 34) provided qualitative feedback about format and design; 88% of these participants found the tool useful and 94% found it easy to use. 91% of the providers (N = 23) indicated that they would use the tool in their clinical setting. represents a new approach to breast cancer prevention care and a framework for high quality preventive healthcare. The ability to integrate risk assessment and decision support in real time will allow for informed, value-driven, and patient-centered breast cancer prevention decisions. The tool is being further evaluated in the clinical setting.
    Full-text · Article · Jan 2014 · BMC Medical Informatics and Decision Making
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    • "All these facts also may explain why the Barlow model overestimates risk of breast cancer in our population. A new model for assessing 5-year risk was developed later by the Breast Cancer Surveillance Consortium [29], which would be interesting to assess in a Spanish population in future studies. "
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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.
    Full-text · Article · Dec 2013 · BMC Cancer
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    • "Mammographic breast density in clinical practice is assessed using a 4-category score defined in the American College of Radiology's Breast Imaging Reporting and Data System (BI-RADS) [11]. Several risk models have been developed using BI-RADS density including the Breast Cancer Surveillance Consortium's 1-year and 5-year models, developed using over 1 million women [12], [13]. BI-RADS density categories, however, have limitations. "
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    ABSTRACT: Clinical scores of mammographic breast density are highly subjective. Automated technologies for mammography exist to quantify breast density objectively, but the technique that most accurately measures the quantity of breast fibroglandular tissue is not known. To compare the agreement of three automated mammographic techniques for measuring volumetric breast density with a quantitative volumetric MRI-based technique in a screening population. Women were selected from the UCSF Medical Center screening population that had received both a screening MRI and digital mammogram within one year of each other, had Breast Imaging Reporting and Data System (BI-RADS) assessments of normal or benign finding, and no history of breast cancer or surgery. Agreement was assessed of three mammographic techniques (Single-energy X-ray Absorptiometry [SXA], Quantra, and Volpara) with MRI for percent fibroglandular tissue volume, absolute fibroglandular tissue volume, and total breast volume. Among 99 women, the automated mammographic density techniques were correlated with MRI measures with R(2) values ranging from 0.40 (log fibroglandular volume) to 0.91 (total breast volume). Substantial agreement measured by kappa statistic was found between all percent fibroglandular tissue measures (0.72 to 0.63), but only moderate agreement for log fibroglandular volumes. The kappa statistics for all percent density measures were highest in the comparisons of the SXA and MRI results. The largest error source between MRI and the mammography techniques was found to be differences in measures of total breast volume. Automated volumetric fibroglandular tissue measures from screening digital mammograms were in substantial agreement with MRI and if associated with breast cancer could be used in clinical practice to enhance risk assessment and prevention.
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