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Research demonstrates value of Volpara Solutions’ breast density assessment to help improve breast cancer risk prediction models

The value of volumetric breast density to help improve risk prediction models and monitor clinical treatment was the focus of numerous abstracts presented at the 2014 San Antonio Symposium (SABCS). Five scientific abstracts highlight the use of VolparaDensity automated breast density software to provide insight into the impact of temporal changes of breast density, utilize volumetric density to improve risk prediction to support personalized screening models, and monitor the effectiveness of chemo-preventive treatments. For a complete summary of the volumetric research presented at SABCS, visit Volpara – SABCS Research Summary.

In the study, “Volumetric breast density improves ” (P6-09-04), researchers from University of Virginia Health System evaluated the association between risk factors and breast cancer diagnosis. The study enrolled 3,445 women; 839 cases and 2,606 controls; multivariate analysis was conducted using 860 cases and 1,683 controls. Risk factor information was collected using a self-reported electronic questionnaire and mean automated volumetric breast density from VolparaDensity was calculated for each patient as a percentage. Results of the study demonstrate that the addition of volumetric breast density improved discrimination, which is critical in model development if screening recommendations are to be individualized. The risk model used automated measurement of breast density as a continuous variable that proved to be one of the top five predictors of in the study population. Volumetric breast density demonstrated improved discrimination for both the full prediction model (0.86) and a minimal model (0.82) (13 covariates) compared with the Tyrer-Cuzick (IBIS) model (0.74).

University of Virginia researchers also presented the results of the “Association of mammographic density and molecular breast cancer subtype” (P2-04-04). This study looked at the association of mammographic density measured by VolparaDensity and molecular subtypes of breast cancer. Visual BI-RADS density scores and volumetric breast density measurements were obtained for 457 patients with invasive breast cancer. Molecular subtypes (Luminal A, Luminal B, Her-2-Neu and Triple Negative) were categorized according to hormone receptor status (i.e. estrogen receptor (ER), progesterone receptor (PR) and Her-2 receptor), tumor grade, and mitotic score. After adjusting for age, race, BMI, family history of breast cancer and lobular carcinoma in situ (LCIS), volumetric breast density was significantly associated with Her-2-neu positive tumors (p = 0.035). A similar analysis showed that this association was not seen for visual BI-RADS categories (p = 0.671 and p = 0.099 for BI-RADS 3 and 4, respectively). The results show that women with denser breasts by continuous-scaled quantitative measurements are at higher risk for Her-2+ tumors; an association not delineated using standard BIRADS density classification. The identification of risk factors that are associated with specific breast cancer subtypes could help to inform personalized risk prediction models and prevention strategies.