Meta analysis of the use of Bayesian networks in breast cancer diagnosis

• Published in: Cadernos de saúde pública Vol. 31; no. 1; pp. 26 - 38
• Main Authors: Simões, Priscyla Waleska, Silva, Geraldo Doneda da, Moretti, Gustavo Pasquali, Simon, Carla Sasso, Winnikow, Erik Paul, Nassar, Silvia Modesto, Medeiros, Lidia Rosi, Rosa, Maria Inês
• Format: Journal Article
• Language: Portuguese
• Published: Brazil 01.01.2015
• Subjects: Bayes Theorem; Breast Neoplasms - diagnosis; Diagnosis, Computer-Assisted; Female; Humans; Mammography; Medical Informatics; Sensitivity and Specificity
• ISSN: 1678-4464
• DOI: 10.1590/0102-311X00205213

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.