Improving Breast Cancer Risk Prediction 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
Primary Institution: National Cancer Institute
Hypothesis
Incorporating mammographic density into breast cancer risk models will improve risk stratification.
Conclusion
Integrating mammographic density with questionnaire-based risk factors and polygenic risk scores can identify more women at elevated risk of breast cancer.
Supporting Evidence
- The model with density identified 18.4% of US women aged 50-70 years at ≥ 3% 5-year predicted risk.
- 42.4% of future cases were expected to occur in the identified group.
- 5.3% of Swedish women were reclassified with the addition of density, leading to the identification of an additional 4.4% of future cases.
Takeaway
This study shows that adding mammographic density to existing breast cancer risk models helps find more women who might get breast cancer, so they can get help sooner.
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 of European-ancestry women.
Potential Biases
Potential misclassification of mammographic density due to subjective assessments and variability in measurement methods.
Limitations
The study primarily included women of European ancestry, which may limit the applicability of the findings to other populations.
Participant Demographics
European-ancestry women aged 50-70 years from the US and Sweden.
Statistical Information
Confidence Interval
95% CI: 63.5–70.6%
Statistical Significance
p<0.05
Digital Object Identifier (DOI)
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