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Breast Cancer Research and Treatment

, Volume 164, Issue 2, pp 263–284 | Cite as

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

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

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

Risk assessment Risk Models Breast Cancer Screening 

Notes

Acknowledgements

We would like to thank Dr. Giovanni Parmigiani for his insight and advice in finalizing this manuscript.

Compliance with ethical standards

Conflicts of interest

The authors have no potential conflicts of interest to disclose.

Research involving animal and human rights

This study does not contain any studies with human participants or animals performed by any of the authors.

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Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  • Jessica A. Cintolo-Gonzalez
    • 1
  • Danielle Braun
    • 2
    • 3
  • Amanda L. Blackford
    • 4
  • Emanuele Mazzola
    • 3
  • Ahmet Acar
    • 1
  • Jennifer K. Plichta
    • 5
  • Molly Griffin
    • 1
  • Kevin S. Hughes
    • 1
  1. 1.Division of Surgical OncologyMassachusetts General HospitalBostonUSA
  2. 2.Department of BiostatisticsHarvard University T H Chan School of Public HealthBostonUSA
  3. 3.Department of Biostatistics and Computational BiologyDana-Farber Cancer InstituteBostonUSA
  4. 4.Department of OncologyJohns Hopkins University School of MedicineBaltimoreUSA
  5. 5.Duke University Health SystemsDurhamUSA

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