• Adelaide H McClintock, Anna L Golob, and Mary B Laya.
• Division of General Internal Medicine, Department of Medicine, University of Washington, Seattle, Washington; Women's Health Care Center, Seattle, Washington. Electronic address: ahearst@uw.edu.
• Mayo Clin. Proc. 2020 Jun 1; 95 (6): 1268-1275.

AbstractBreast cancer-screening guidelines increasingly recommend that clinicians perform a risk assessment for breast cancer to inform shared decision making for screening. Precision medicine is quickly becoming the preferred approach to cancer screening, with the aim of increased surveillance in high-risk women, while sparing lower-risk women the burden of unnecessary imaging. Risk assessment also informs clinical care by refining screening recommendations for younger women, identifying women who should be referred to genetic counseling, and identifying candidates for risk-reducing medications. Several breast cancer risk-assessment models are currently available to help clinicians categorize a woman's risk for breast cancer. However, choosing the appropriate model for a given patient requires a working knowledge of the strengths, weaknesses, and performance characteristics of each. The aim of this article is to provide a stepwise approach for clinicians to assess an individual woman's risk for breast cancer and describe the features, appropriate use, and performance characteristics of commonly encountered risk-prediction models. This approach will help primary care providers engage in shared decision making by efficiently generating an accurate risk assessment and make clear, evidence-based screening and prevention recommendations that are appropriately matched to a woman's risk for breast cancer.Copyright © 2020 Mayo Foundation for Medical Education and Research. All rights reserved.

15 keywords...

hide…