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J Clin Oncol. 2015 Oct 1;33(28):3137-43. doi: 10.1200/JCO.2015.60.8869. Epub 2015 Aug 17.

Breast Density and Benign Breast Disease: Risk Assessment to Identify Women at High Risk of Breast Cancer.

Author information

  • 1Jeffrey A. Tice and Karla Kerlikowske, University of California, San Francisco, San Francisco; Diana L. Miglioretti and Chin-Shang Li, University of California, Davis, Davis, CA; Diana L. Miglioretti, Group Health Research Institute, Group Health Cooperative, Seattle, WA; Celine M. Vachon, Mayo Clinic, Rochester, MN; and Charlotte C. Gard, New Mexico State University, Las Cruces, NM. jtice@medicine.ucsf.edu.
  • 2Jeffrey A. Tice and Karla Kerlikowske, University of California, San Francisco, San Francisco; Diana L. Miglioretti and Chin-Shang Li, University of California, Davis, Davis, CA; Diana L. Miglioretti, Group Health Research Institute, Group Health Cooperative, Seattle, WA; Celine M. Vachon, Mayo Clinic, Rochester, MN; and Charlotte C. Gard, New Mexico State University, Las Cruces, NM.

Abstract

PURPOSE:

Women with proliferative breast lesions are candidates for primary prevention, but few risk models incorporate benign findings to assess breast cancer risk. We incorporated benign breast disease (BBD) diagnoses into the Breast Cancer Surveillance Consortium (BCSC) risk model, the only breast cancer risk assessment tool that uses breast density.

METHODS:

We developed and validated a competing-risk model using 2000 to 2010 SEER data for breast cancer incidence and 2010 vital statistics to adjust for the competing risk of death. We used Cox proportional hazards regression to estimate the relative hazards for age, race/ethnicity, family history of breast cancer, history of breast biopsy, BBD diagnoses, and breast density in the BCSC.

RESULTS:

We included 1,135,977 women age 35 to 74 years undergoing mammography with no history of breast cancer; 17% of the women had a prior breast biopsy. During a mean follow-up of 6.9 years, 17,908 women were diagnosed with invasive breast cancer. The BCSC BBD model slightly overpredicted risk (expected-to-observed ratio, 1.04; 95% CI, 1.03 to 1.06) and had modest discriminatory accuracy (area under the receiver operator characteristic curve, 0.665). Among women with proliferative findings, adding BBD to the model increased the proportion of women with an estimated 5-year risk of 3% or higher from 9.3% to 27.8% (P<.001).

CONCLUSION:

The BCSC BBD model accurately estimates women's risk for breast cancer using breast density and BBD diagnoses. Greater numbers of high-risk women eligible for primary prevention after BBD diagnosis are identified using the BCSC BBD model.

© 2015 by American Society of Clinical Oncology.

PMID:
26282663
[PubMed - indexed for MEDLINE]
PMCID:
PMC4582144
[Available on 2016-10-01]
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