Metanálise do uso de redes bayesianas no diagnóstico de câncer de mama

• Autor(a) principal: Priscyla Waleska Simões
• Data de Publicação: 2015
• Outros Autores: Geraldo Doneda da Silva, Gustavo Pasquali Moretti, Carla Sasso Simon, Erik Paul Winnikow, Silvia Modesto Nassar, Lidia Rosi Medeiros, Maria Inês Rosa
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Cadernos de Saúde Pública
• Texto Completo: https://cadernos.ensp.fiocruz.br/ojs/index.php/csp/article/view/5886
• topic: Medical Informatics; Bayes Theorem; Breast Neoplasms

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.