title: Comparing the value of mammographic features and genetic variants in breast cancer risk prediction.
abstract: The goal of this study was to compare the value of mammographic features and genetic variants for breast cancer risk prediction with Bayesian reasoning and information theory. We conducted a retrospective case-control study, collecting mammographic findings and high-frequency/low-penetrance genetic variants from an existing personalized medicine data repository. We trained and tested Bayesian networks for mammographic findings and genetic variants respectively. We found that mammographic findings had a higher discriminative ability than genetic variants for improving breast cancer risk prediction in terms of the area under the ROC curve. We compared the value of each mammographic feature and genetic variant for breast risk prediction in terms of mutual information, with and without consideration of interactions of those risk factors. We also identified the interactions between mammographic features and genetic variants in an attempt to prioritize mammographic features and genetic variants to efficiently predict the risk of breast cancer.
authors.affiliation: University of Wisconsin, Madison, WI, USA.
authors.forename: Yirong
authors.initials: Y
authors.lastname: Wu
authors.affiliation: University of Wisconsin, Madison, WI, USA.
authors.forename: Jie
authors.initials: J
authors.lastname: Liu
authors.affiliation: University of Wisconsin, Madison, WI, USA.
authors.forename: David
authors.initials: D
authors.lastname: Page
authors.affiliation: Marshfield Clinic Research Foundation, Marshfield, WI, USA.
authors.forename: Peggy
authors.initials: P
authors.lastname: Peissig
authors.affiliation: Essentia Institute of Rural Health, Duluth, MN, USA.
authors.forename: Catherine
authors.initials: C
authors.lastname: McCarty
authors.affiliation: Marshfield Clinic Research Foundation, Marshfield, WI, USA ; Department of Hematology/Oncology, Marshfield Clinic Weston Center, Weston, WI, USA ; School of Population Health, University of Queensland, Brisbane, Australia.
authors.forename: Adedayo A
authors.initials: AA
authors.lastname: Onitilo
authors.affiliation: University of Wisconsin, Madison, WI, USA.
authors.forename: Elizabeth S
authors.initials: ES
authors.lastname: Burnside
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D019540:Area Under Curve; D001499:Bayes Theorem; D001943:Breast Neoplasms / Q000000981:diagnostic imaging / Q000235:genetics*; D005189:False Positive Reactions; D005260:Female; D006801:Humans; D007257:Information Theory; D008327:Mammography; D008875:Middle Aged; D020641:Polymorphism, Single Nucleotide; D012372:ROC Curve; D018570:Risk Assessment