Abstract P2-10-29: Time dependent breast cancer metastasis prediction using novel biological imaging, clinico-pathological and genomic data combined with Bayesian modeling to reduce over-fitting and improve on inter-cohort reproducibility.

Abstract Background: Breast cancer heterogeneity demands that prognostic models must be biologically driven and recent clinical evidence indicates that future prognostic signatures need evaluation in the context of early versus late metastatic risk prediction. The aim of our work was to identify biologically validated quantitative imaging parameters with improved correlation to clinical outcome, and to address some of the …