Predicting the onset of breast cancer using mammogram imaging data with irregular boundary.

ABSTRACT: With mammography being the primary breast cancer screening strategy, it is essential to make full use of the mammogram imaging data to better identify women who are at higher and lower than average risk. Our primary goal in this study is to extract mammogram-based features that augment the well-established breast cancer risk factors to improve prediction accuracy. In this article, we propose a supervised functional principal component analysis (sFPCA) over triangulations method for extracting features that are ordered by the magnitude of association with the failure time outcome. The proposed method accommodates the irregular boundary issue posed by the breast area within the mammogram imaging data with flexible bivariate splines over triangulations. We also provide an eigenvalue decomposi

SUBMITTER: Jiang S 

PROVIDER: S-EPMC10102887 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature