AMIA Annu Symp Proc - Genetic variants improve breast cancer risk prediction on mammograms.

Tópicos

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Resumo

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

Resumo Limpo

sever recent genomewid associ studi identifi genet variant associ breast cancer howev much genet variant may help advanc breast cancer risk predict base clinic featur like mammograph find unknown conduct retrospect casecontrol studi collect mammograph find highfrequencylowpenetr genet variant exist person medicin data repositori bayesian network develop use tree augment naiv bay tan train mammograph find without genet variant collect analyz predict perform use area roc curv found genet variant signific improv breast cancer risk predict mammogram also identifi interact effect genet variant collect mammograph find attempt link genotyp mammograph phenotyp better understand diseas pattern mechan andor natur histori

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