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Breast Cancer Res Treat. 2019 Feb 22. doi: 10.1007/s10549-019-05167-2. [Epub ahead of print]

Validation of the breast cancer surveillance consortium model of breast cancer risk.

Author information

1
Division of General Internal Medicine, Department of Medicine, University of California, San Francisco, 1545 Divisadero Street, Suite 309, San Francisco, CA, 94143-0320, USA. jeff.tice@ucsf.edu.
2
Department of Public Health Sciences, University of California, Davis, Davis, CA, USA.
3
Kaiser Permanente Washington Health Research Institute, Seattle, WA, USA.
4
Department of Economics, Applied Statistics, and International Business, New Mexico State University, Las Cruces, NM, USA.
5
Division of Epidemiology and Biostatistics, University of Illinois at Chicago, Chicago, IL, USA.
6
Advocate Health Care, Downers Grove, IL, USA.
7
General Internal Medicine Section, Department of Veteran Affairs and Departments of Medicine and Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USA.

Abstract

PURPOSE:

In order to use a breast cancer prediction model in clinical practice to guide screening and prevention, it must be well calibrated and validated in samples independent from the one used for development. We assessed the accuracy of the breast cancer surveillance consortium (BCSC) model in a racially diverse population followed for up to 10 years.

METHODS:

The BCSC model combines breast density with other risk factors to estimate a woman's 5- and 10-year risk of invasive breast cancer. We validated the model in an independent cohort of 252,997 women in the Chicago area. We evaluated calibration using the ratio of expected to observed (E/O) invasive breast cancers in the cohort and discrimination using the area under the receiver operating characteristic curve (AUROC).

RESULTS:

In an independent cohort of 252,997 women (median age 50 years, 26% non-Hispanic Black), the BCSC model was well calibrated (E/O = 0.94, 95% confidence interval [CI] 0.90-0.98), but underestimated the incidence of invasive breast cancer in younger women and in women with low mammographic density. The AUROC was 0.633, similar to that observed in prior validation studies.

CONCLUSIONS:

The BCSC model is a well-validated risk assessment tool for breast cancer that may be particularly useful when assessing the utility of supplemental screening in women with dense breasts.

KEYWORDS:

Breast cancer surveillance consortium; Breast density; Breast neoplasms; Predictive value of tests; ROC curve; Risk assessment

PMID:
30796654
DOI:
10.1007/s10549-019-05167-2

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