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Breast Cancer Res Treat. 2017 Jul;164(2):263-284. doi: 10.1007/s10549-017-4247-z. Epub 2017 Apr 25.

Breast cancer risk models: a comprehensive overview of existing models, validation, and clinical applications.

Author information

1
Division of Surgical Oncology, Massachusetts General Hospital, Boston, MA, USA. jcintolo-gonzalez@partners.org.
2
Department of Biostatistics, Harvard University T H Chan School of Public Health, Boston, MA, USA.
3
Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA, USA.
4
Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
5
Division of Surgical Oncology, Massachusetts General Hospital, Boston, MA, USA.
6
Duke University Health Systems, Durham, NC, USA.

Abstract

Numerous models have been developed to quantify the combined effect of various risk factors to predict either risk of developing breast cancer, risk of carrying a high-risk germline genetic mutation, specifically in the BRCA1 and BRCA2 genes, or the risk of both. These breast cancer risk models can be separated into those that utilize mainly hormonal and environmental factors and those that focus more on hereditary risk. Given the wide range of models from which to choose, understanding what each model predicts, the populations for which each is best suited to provide risk estimations, the current validation and comparative studies that have been performed for each model, and how to apply them practically is important for clinicians and researchers seeking to utilize risk models in their practice. This review provides a comprehensive guide for those seeking to understand and apply breast cancer risk models by summarizing the majority of existing breast cancer risk prediction models including the risk factors they incorporate, the basic methodology in their development, the information each provides, their strengths and limitations, relevant validation studies, and how to access each for clinical or investigative purposes.

KEYWORDS:

Breast Cancer Screening; Risk Models; Risk assessment

PMID:
28444533
DOI:
10.1007/s10549-017-4247-z
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