Improving Breast Cancer Risk Models with Mammographic Density
Author Information
Author(s): Mulder Charlotta V., Yang Xin, Jee Yon Ho, Scott Christopher G., Gao Chi, Cao Yu, Hurson Amber N., Eriksson Mikael, Vachon Celine M., Hall Per, Antoniou Antonis C., Kraft Peter, Gierach Gretchen L., Garcia-Closas Montserrat, Choudhury Parichoy Pal
Hypothesis
Incorporating mammographic density into breast cancer risk models will improve risk stratification.
Conclusion
Integrating mammographic density with other risk factors could help identify more women at elevated risk of breast cancer.
Supporting Evidence
- The model with density showed an area under the curve of 67.0% for younger women and 66.1% for older women.
- In the US, 18.4% of women aged 50-70 years were identified as having a ≥3% 5-year predicted risk.
- In Sweden, 10.3% of women were identified as having a ≥3% 5-year predicted risk.
Takeaway
This study found that adding mammographic density to breast cancer risk models can help find more women who might get breast cancer.
Methodology
The study used the iCARE tool to build and validate a risk model incorporating mammographic density, questionnaire-based risk factors, and a polygenic risk score across three cohorts.
Limitations
Further investigations in non-European ancestry populations are needed before clinical applications can be considered.
Participant Demographics
European-ancestry women, with analyses for those younger and older than 50 years.
Statistical Information
Confidence Interval
95% CI: 63.5–70.6% for younger women; 95% CI: 64.4–67.8% for older women.
Digital Object Identifier (DOI)
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