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Breast Cancer Research and Treatment
Published in: Breast Cancer Research and Treatment 2/2017

01-07-2017 | Review

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

Authors: Jessica A. Cintolo-Gonzalez, Danielle Braun, Amanda L. Blackford, Emanuele Mazzola, Ahmet Acar, Jennifer K. Plichta, Molly Griffin, Kevin S. Hughes

Published in: Breast Cancer Research and Treatment | Issue 2/2017

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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.
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Metadata
Title
Breast cancer risk models: a comprehensive overview of existing models, validation, and clinical applications
Authors
Jessica A. Cintolo-Gonzalez
Danielle Braun
Amanda L. Blackford
Emanuele Mazzola
Ahmet Acar
Jennifer K. Plichta
Molly Griffin
Kevin S. Hughes
Publication date
01-07-2017
Publisher
Springer US
Published in
Breast Cancer Research and Treatment / Issue 2/2017
Print ISSN: 0167-6806
Electronic ISSN: 1573-7217
DOI
https://doi.org/10.1007/s10549-017-4247-z

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