Abstract PR-02: Breast Cancer Risk Assessment: Genetic and Non-Genetic Risk Factors contributing to Differential Model Performance
Authors: Quante Anne S. , Whittemore Alice S. , Terry Mary Beth 
Published: January 2014

Introduction: Clinicians use different breast cancer risk models for patients considered at average and above-average risk, based largely on their family histories and genetic factors. To explore the feasibility of a single model appropriate for all women, we used longitudinal cohort data from women whose breast cancer risks span the full spectrum to determine the covariates that differentiate the performance of two commonly used models, BCRAT, or also called GAIL model, (used for patients with average risk) and IBIS, or also called Tyrer Cuzick model, (for patients with above-average risk).

Methods: We evaluated the performance of BCRAT and IBIS for ten-year absolute risk of breast cancer, using prospective data from 1,857 women over a mean follow-up length of 8.1 years, of whom 83 developed cancer. This cohort spans the continuum of breast cancer risk, with some subjects at lower than average population risk. Therefore, the wide-variation in individual risk makes it an interesting population to examine model performance across subgroups of women. For model calibration, we divided the cohort into quartiles of model-assigned risk and comparing differences between assigned and observed risks using the Hosmer-Lemeshow (HL) chi-squared statistic. For model discrimination, we computed the area under the receiver operator curve (AUC) and the case risk percentiles (CRPs).

Results: The ten-year risks assigned by BCRAT and IBIS differed (range of difference 0.001 to 79.5). The mean BCRAT- and IBIS-assigned risks of 3.18% and 5.59%, respectively, were lower than the cohort's 10-year cumulative probability of developing breast cancer (6.25%; 95% confidence interval (CI) = 5.0-7.8%). Agreement between assigned and observed risks was better for IBIS (HL X42 =7.2, P-value 0.13) than BCRAT (HL X42 = 22.0, P-value &lt; 0.001). The IBIS model also showed better discrimination (AUC = 69.5%, CI = 63.8% - 75.2%) than did the BCRAT model (AUC = 63.2%, CI = 57.6% - 68.9%). In almost all covariate-specific subgroups, BCRAT mean risks were significantly lower than the observed risks, while IBIS risks showed generally good agreement with observed risks, even in the subgroups of women considered at average risk (e.g., no family history of breast cancer, BRCA1/2 mutation negative). Conclusions: Models developed using extended family history and genetic data, such as the IBIS model, also perform well in women considered at average risk (e.g. no family history of breast cancer, BRCA1/2 mutation negative). Extending such models to include nongenetic information (e.g., breast biopsy history and race/ethnicity) may improve their performance among all women across the breast cancer risk continuum.

This abstract is also presented as Poster A109.

Citation Format: Anne S. Quante, Alice S. Whittemore, Mary Beth Terry. Breast cancer risk assessment: Genetic and nongenetic risk factors contributing to differential model performance. [abstract]. In: Proceedings of the Eleventh Annual AACR International Conference on Frontiers in Cancer Prevention Research; 2012 Oct 16-19; Anaheim, CA. Philadelphia (PA): AACR; Cancer Prev Res 2012;5(11 Suppl):Abstract nr PR-02.