1. [Meta analysis of the use of Bayesian networks in breast cancer diagnosis].
- Author
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Simões PW, Silva GD, Moretti GP, Simon CS, Winnikow EP, Nassar SM, Medeiros LR, and Rosa MI
- Subjects
- Diagnosis, Computer-Assisted, Female, Humans, Mammography, Medical Informatics, Sensitivity and Specificity, Bayes Theorem, Breast Neoplasms 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.
- Published
- 2015
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