Supervised two-dimensional functional principal component analysis with time-to-event outcomes and mammogram imaging data.

ABSTRACT: Screening mammography aims to identify breast cancer early and secondarily measures breast density to classify women at higher or lower than average risk for future breast cancer in the general population. Despite the strong association of individual mammography features to breast cancer risk, the statistical literature on mammogram imaging data is limited. While functional principal component analysis (FPCA) has been studied in the literature for extracting image-based features, it is conducted independently of the time-to-event response variable. With the consideration of building a prognostic model for precision prevention, we present a set of flexible methods, supervised FPCA (sFPCA) and functional partial least squares (FPLS), to extract image-based features associated with the failur

SUBMITTER: Jiang S 

PROVIDER: S-EPMC9160217 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature