Background The American Cancer Society (ACS) suggests using a stratifed strategy for breast cancer screening. The strategy includes assessing risk of breast cancer, screening women at high risk with both MRI and mammography, and screening women at low risk with mammography alone. The ACS chose their cutof for high risk using expert consensus. Methods We propose instead an analytic approach that maximizes the diagnostic accuracy (AUC/ROC) of a risk-based stratifed screening strategy in a population. The inputs are the joint distribution of screening test scores, and the odds of disease, for the given risk score. Using the approach for breast cancer screening, we estimated the optimal risk cutof for two diferent risk models: the Breast Cancer Screening Consortium (BCSC) model and a hypothetical model with much better discriminatory accuracy. Data on mammography and MRI test score distributions were drawn from the Magnetic Resonance Imaging Screening Study Group. Results A risk model with an excellent discriminatory accuracy (c-statistic = 0.947) yielded a reasonable cutof where only about 20% of women had dual screening. However, the BCSC risk model (c-statistic = 0.631) lacked the discriminatory accuracy to diferentiate between women who needed dual screening, and women who needed only mammography. Conclusion Our research provides a general approach to optimize the diagnostic accuracy of a stratifed screening strategy in a population, and to assess whether risk models are sufciently accurate to guide stratifed screening. For breast cancer, most risk models lack enough discriminatory accuracy to make stratifed screening a reasonable recommendation.

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Keywords Cancer screening · Stratifed screening · Risk assessment · ROC analysis
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Journal Cancer Causes & Control: an international journal of studies of cancer in human populations
Brinton, J.T., Hendrick, R.E., Ringham, B.M., Kriege, M., & Glueck, D.H. (2019). Improving the diagnostic accuracy of a stratified screening strategy by identifying the optimal risk cutoff. Cancer Causes & Control: an international journal of studies of cancer in human populations, 30(10), 1145–1155. doi:10.1007/s10552-019-01208-9