||3R37CA256810-03S1 Interpret this number
||Dynamic Prediction Incorporating Time-Varying Covariates for the Onset of Breast Cancer
Triple negative breast cancer (TNBC) is an aggressive subtype of breast cancer with limited
treatment options and poor survival. Approximately 12% to 17% of women with breast cancer are
diagnosed with TNBC. Women with TNBC have relatively poor outcomes and cannot be treated
with targeted therapies. However, the risk of TNBC is not uniform across all race-ethnic groups of
the US population. Review of epidemiologic risk factors and TNBC incidence shows limited insight
to variation in risk or risk reduction with the exception of history of breast feeding and higher
vegetable and grain intake. We aim to bring personalized dynamic prediction to improve the
current TNBC risk classification paradigm to make full use of the longitudinal information, in
addition to the baseline information, where risk prediction/stratification can be updated as new
observations are gathered to reflect the woman’s latest health- and behavioral-related status.
Specifically, we aim to investigate 5- and 10-year TNBC risk prediction performance by proposing
novel statistical methods that fully utilize the personalized mammogram-based risk factors from
repeated mammogram images. The proposed study capitalizes on the WashU TNBC cohort with
rich digital mammograms with well-studied BC risk factors, 10 years of follow-up and pathology
confirmed incident TNBC. All proposed statistical methods will be supplemented by R code that we
will make publicly available.
None. See parent grant details.