title: Robust automatic breast cancer staging using a combination of functional genomics and image-omics.
abstract: Breast cancer is one of the leading cancers worldwide. Precision medicine is a new trend that systematically examines molecular and functional genomic information within each patient's cancer to identify the patterns that may affect treatment decisions and potential outcomes. As a part of precision medicine, computer-aided diagnosis enables joint analysis of functional genomic information and image from pathological images. In this paper we propose an integrated framework for breast cancer staging using image-omics and functional genomic information. The entire biomedical imaging informatics framework consists of image-omics extraction, feature combination, and classification. First, a robust automatic nuclei detection and segmentation is presented to identify tumor regions, delineate nuclei boundaries and calculate a set of image-based morphological features; next, the low dimensional image-omics is obtained through principal component analysis and is concatenated with the functional genomic features identified by a linear model. A support vector machine for differentiating stage I breast cancer from other stages are learned. We experimentally demonstrate that compared with a single type of representation (image-omics), the combination of image-omics and functional genomic feature can improve the classification accuracy by 3%.
authors.forename: Hai
authors.initials: H
authors.lastname: Su
authors.forename: Yong
authors.initials: Y
authors.lastname: Shen
authors.forename: Fuyong
authors.initials: F
authors.lastname: Xing
authors.forename: Xin
authors.initials: X
authors.lastname: Qi
authors.forename: Kim M
authors.initials: KM
authors.lastname: Hirshfield
authors.forename: Lin
authors.initials: L
authors.lastname: Yang
authors.forename: David J
authors.initials: DJ
authors.lastname: Foran
mesh_terms: D000465:Algorithms; D001943:Breast Neoplasms / Q000145:classification / Q000175:diagnosis / Q000473:pathology*; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D005260:Female; D023281:Genomics; D006801:Humans; D016014:Linear Models; D009367:Neoplasm Staging; D025341:Principal Component Analysis; D060388:Support Vector Machine