“This study highlights the risk of a blanket approach to using risk prediction models to inform individual-level medical screening and treatment decisions,” said Dr. Joann Elmore, the paper’s senior author and a professor of medicine in the division of general internal medicine and health services research at the David Geffen School of Medicine at UCLA. “All three of the models we looked at had similar accuracy at the population level, but in our analyses, there was marked disagreement between who was identified as ‘high risk’ by all three models.”
The authors say their findings highlight the tradeoff of sensitivity and inaccurate classification of “high risk” when using the two different thresholds currently recommended. For example, when using the ≥ 1.67% cutoff for considering chemoprevention, about half of the women diagnosed with a future breast cancer might be correctly identified as high risk, yet many more women would be falsely classified as high risk. While using the more conservative ≥ 3.0% cutoff would lead to far fewer women incorrectly classified as high risk, most of the women with a future breast cancer diagnosis would be missed.
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The study has some limitations. For example, the cohort was drawn from women enrolled in a longitudinal screening study. And although the authors had extensive risk factor data on many participants, some family history was missing as was data on polygenetic risk scores.
The authors point out that newer risk models are being developed that include information on breast cancer susceptibility genes and genetic susceptibility variants, which may improve predictability. Meanwhile several recent studies suggest that quantitative imaging biomarkers and artificial intelligence algorithms might also supplement or supplant the current, subjective clinical risk assessment tools.
Additional authors were Jeremy S. Paige MD, PhD, Christoph I. Lee MD, MS, MBA, Pin-Chieh Wang PhD, William Hsu PhD, Adam R. Brentnall PhD, Anne C. Hoyt MD, and Arash Naeim MD, PhD.
The study was supported by the Program Leader Vision Award from the UCLA Jonsson Comprehensive Cancer Center, the National Cancer Institute (R37 CA240403), UCLA Jonsson Comprehensive Cancer Center study recruitment funding, University of California Office of the President Multi-campus Research Programs and Initiatives Grant, and a generous donation from the Safeway Foundation.
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