Genetic Variants Improve Breast Cancer Risk Prediction on Mammograms
作者： David Page , Catherine A. McCarty , Peggy L. Peissig , Adedayo A. Onitilo , Jude W. Shavlik
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摘要: Several recent genome-wide association studies have identified genetic variants associated with breast cancer. However, how much these may help advance cancer risk prediction based on other clinical features, like mammographic findings, is unknown. We conducted a retrospective case-control study, collecting findings and high-frequency/low-penetrance from an existing personalized medicine data repository. A Bayesian network was developed using Tree Augmented Naive Bayes (TAN) by training the without 22 collected. analyzed predictive performance area under ROC curve, found that significantly improved mammograms. also interaction effect between collected in attempt to link genotype phenotype better understand disease patterns, mechanisms, and/or natural history.
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