title: [Meta analysis of the use of Bayesian networks in breast cancer diagnosis].
abstract: The aim of this study was to determine the accuracy of Bayesian networks in supporting breast cancer diagnoses. Systematic review and meta-analysis were carried out, including articles and papers published between January 1990 and March 2013. We included prospective and retrospective cross-sectional studies of the accuracy of diagnoses of breast lesions (target conditions) made using Bayesian networks (index test). Four primary studies that included 1,223 breast lesions were analyzed, 89.52% (444/496) of the breast cancer cases and 6.33% (46/727) of the benign lesions were positive based on the Bayesian network analysis. The area under the curve (AUC) for the summary receiver operating characteristic curve (SROC) was 0.97, with a Q* value of 0.92. Using Bayesian networks to diagnose malignant lesions increased the pretest probability of a true positive from 40.03% to 90.05% and decreased the probability of a false negative to 6.44%. Therefore, our results demonstrated that Bayesian networks provide an accurate and non-invasive method to support breast cancer diagnosis.
authors.forename: Priscyla Waleska
authors.initials: PW
authors.lastname: Simões
authors.forename: Geraldo Doneda da
authors.initials: GD
authors.lastname: Silva
authors.forename: Gustavo Pasquali
authors.initials: GP
authors.lastname: Moretti
authors.forename: Carla Sasso
authors.initials: CS
authors.lastname: Simon
authors.forename: Erik Paul
authors.initials: EP
authors.lastname: Winnikow
authors.forename: Silvia Modesto
authors.initials: SM
authors.lastname: Nassar
authors.forename: Lidia Rosi
authors.initials: LR
authors.lastname: Medeiros
authors.forename: Maria Inês
authors.initials: MI
authors.lastname: Rosa
mesh_terms: D001499:Bayes Theorem; D001943:Breast Neoplasms / Q000175:diagnosis*; D003936:Diagnosis, Computer-Assisted; D005260:Female; D006801:Humans; D008327:Mammography; D008490:Medical Informatics; D012680:Sensitivity and Specificity