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Cancer Epidemiology, Biomarkers & Prevention
Cancer Epidemiology, Biomarkers & Prevention
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Commentary

Translating Cancer Risk Prediction Models into Personalized Cancer Risk Assessment Tools: Stumbling Blocks and Strategies for Success

Erika A. Waters, Jennifer M. Taber, Amy McQueen, Ashley J. Housten, Jamie L. Studts and Laura D. Scherer
Erika A. Waters
1Washington University School of Medicine, St. Louis, Missouri.
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  • For correspondence: waterse@wustl.edu
Jennifer M. Taber
2Kent State University, Kent, Ohio.
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  • ORCID record for Jennifer M. Taber
Amy McQueen
1Washington University School of Medicine, St. Louis, Missouri.
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Ashley J. Housten
1Washington University School of Medicine, St. Louis, Missouri.
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Jamie L. Studts
3University of Colorado School of Medicine, Denver, Colorado.
4University of Colorado Cancer Center, Denver, Colorado.
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Laura D. Scherer
3University of Colorado School of Medicine, Denver, Colorado.
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DOI: 10.1158/1055-9965.EPI-20-0861 Published December 2020
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Abstract

Cancer risk prediction models such as those published in Cancer Epidemiology, Biomarkers, and Prevention are a cornerstone of precision medicine and public health efforts to improve population health outcomes by tailoring preventive strategies and therapeutic treatments to the people who are most likely to benefit. However, there are several barriers to the effective translation, dissemination, and implementation of cancer risk prediction models into clinical and public health practice. In this commentary, we discuss two broad categories of barriers. Specifically, we assert that the successful use of risk-stratified cancer prevention and treatment strategies is particularly unlikely if risk prediction models are translated into risk assessment tools that (i) are difficult for the public to understand or (ii) are not structured in a way to engender the public's confidence that the results are accurate. We explain what aspects of a risk assessment tool's design and content may impede understanding and acceptance by the public. We also describe strategies for translating a cancer risk prediction model into a cancer risk assessment tool that is accessible, meaningful, and useful for the public and in clinical practice.

Footnotes

  • Cancer Epidemiol Biomarkers Prev 2020;29:2389–95

  • Received June 4, 2020.
  • Revision received July 20, 2020.
  • Accepted September 16, 2020.
  • Published first October 12, 2020.
  • ©2020 American Association for Cancer Research.
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Cancer Epidemiology Biomarkers & Prevention: 29 (12)
December 2020
Volume 29, Issue 12
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Translating Cancer Risk Prediction Models into Personalized Cancer Risk Assessment Tools: Stumbling Blocks and Strategies for Success
Erika A. Waters, Jennifer M. Taber, Amy McQueen, Ashley J. Housten, Jamie L. Studts and Laura D. Scherer
Cancer Epidemiol Biomarkers Prev December 1 2020 (29) (12) 2389-2394; DOI: 10.1158/1055-9965.EPI-20-0861

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Translating Cancer Risk Prediction Models into Personalized Cancer Risk Assessment Tools: Stumbling Blocks and Strategies for Success
Erika A. Waters, Jennifer M. Taber, Amy McQueen, Ashley J. Housten, Jamie L. Studts and Laura D. Scherer
Cancer Epidemiol Biomarkers Prev December 1 2020 (29) (12) 2389-2394; DOI: 10.1158/1055-9965.EPI-20-0861
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    • Translating Cancer Risk Prediction Models into Personalized Cancer Risk Assessment Tools: Stumbling Blocks and Strategies for Success
    • Benefits and Limitations of Personalized Risk Communication
    • Practical Advice for Facilitating and Optimizing the Translation of Cancer Risk Prediction Models into Cancer Risk Assessment Tools
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