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, Laura D. Scherer

Research output: Contribution to journalReview articlepeer-review

6 Scopus citations

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.

Original languageEnglish
Pages (from-to)2389-2394
Number of pages6
JournalCancer Epidemiology Biomarkers and Prevention
Volume29
Issue number12
DOIs
StatePublished - Dec 2020

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