Combining quantitative and qualitative breast density measures to assess breast cancer risk

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  • Kerlikowske, Karla
    • Other Affiliation: Department of Epidemiology and Biostatistics, University of California, San Francisco
  • Ma, Lin
    • Other Affiliation: Department of Medicine, University of California, San Francisco
  • Scott, Christopher G.
    • Other Affiliation: Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic College of Medicine
  • Mahmoudzadeh, Amir P.
    • Other Affiliation: Department of Radiology, University of California, San Francisco
  • Jensen, Matthew R.
    • Other Affiliation: Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic College of Medicine
  • Sprague, Brian L.
    • Other Affiliation: Department of Surgery, University of Vermont
  • Henderson, Louise
    • Affiliation: School of Medicine, Department of Radiology
  • Pankratz, V. Shane
    • Other Affiliation: Department of Internal Medicine, University of New Mexico
  • Cummings, Steven R.
    • Other Affiliation: San Francisco Coordinating Center, California Pacific Medical Center Research Institute
  • Miglioretti, Diana L.
    • Other Affiliation: Department of Public Health Sciences, University of California, Davis
  • Vachon, Celine M.
    • Other Affiliation: Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic College of Medicine
  • Shepherd, John A.
    • Other Affiliation: Department of Radiology, University of California, San Francisco
Abstract
  • Background Accurately identifying women with dense breasts (Breast Imaging Reporting and Data System [BI-RADS] heterogeneously or extremely dense) who are at high breast cancer risk will facilitate discussions of supplemental imaging and primary prevention. We examined the independent contribution of dense breast volume and BI-RADS breast density to predict invasive breast cancer and whether dense breast volume combined with Breast Cancer Surveillance Consortium (BCSC) risk model factors (age, race/ethnicity, family history of breast cancer, history of breast biopsy, and BI-RADS breast density) improves identifying women with dense breasts at high breast cancer risk. Methods We conducted a case-control study of 1720 women with invasive cancer and 3686 control subjects. We calculated ORs and 95% CIs for the effect of BI-RADS breast density and Volpara™ automated dense breast volume on invasive cancer risk, adjusting for other BCSC risk model factors plus body mass index (BMI), and we compared C-statistics between models. We calculated BCSC 5-year breast cancer risk, incorporating the adjusted ORs associated with dense breast volume. Results Compared with women with BI-RADS scattered fibroglandular densities and second-quartile dense breast volume, women with BI-RADS extremely dense breasts and third- or fourth-quartile dense breast volume (75% of women with extremely dense breasts) had high breast cancer risk (OR 2.87, 95% CI 1.84–4.47, and OR 2.56, 95% CI 1.87–3.52, respectively), whereas women with extremely dense breasts and first- or second-quartile dense breast volume were not at significantly increased breast cancer risk (OR 1.53, 95% CI 0.75–3.09, and OR 1.50, 95% CI 0.82–2.73, respectively). Adding continuous dense breast volume to a model with BCSC risk model factors and BMI increased discriminatory accuracy compared with a model with only BCSC risk model factors (C-statistic 0.639, 95% CI 0.623–0.654, vs. C-statistic 0.614, 95% CI 0.598–0.630, respectively; P < 0.001). Women with dense breasts and fourth-quartile dense breast volume had a BCSC 5-year risk of 2.5%, whereas women with dense breasts and first-quartile dense breast volume had a 5-year risk ≤ 1.8%. Conclusions Risk models with automated dense breast volume combined with BI-RADS breast density may better identify women with dense breasts at high breast cancer risk than risk models with either measure alone.
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  • Article
Rights statement
  • In Copyright
Rights holder
  • The Author(s).
Journal title
  • Breast Cancer Research
Journal volume
  • 19
Journal issue
  • 1
Page start
  • 97
Language
  • English
Bibliographic citation
  • Breast Cancer Research. 2017 Aug 22;19(1):97
Publisher
  • BioMed Central

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