title: Preliminary investigation of a Bayesian network for mammographic diagnosis of breast cancer.
abstract: Bayesian networks use the techniques of probability theory to reason under conditions of uncertainty. We investigated the use of Bayesian networks for radiological decision support. A Bayesian network for the interpretation of mammograms (MammoNet) was developed based on five patient-history features, two physical findings, and 15 mammographic features extracted by experienced radiologists. Conditional-probability data, such as sensitivity and specificity, were derived from peer-reviewed journal articles and from expert opinion. In testing with a set of 77 cases from a mammography atlas and a clinical teaching file, MammoNet performed well in distinguishing between benign and malignant lesions, and yielded a value of 0.881 (+/- 0.045) for the area under the receiver operating characteristic curve. We conclude that Bayesian networks provide a potentially useful tool for mammographic decision support.
authors.affiliation: Department of Radiology, Medical College of Wisconsin, Milwaukee, Wisconsin 53226-0509, USA.
authors.forename: C E
authors.initials: CE
authors.lastname: Kahn
authors.forename: L M
authors.initials: LM
authors.lastname: Roberts
authors.forename: K
authors.initials: K
authors.lastname: Wang
authors.forename: D
authors.initials: D
authors.lastname: Jenks
authors.forename: P
authors.initials: P
authors.lastname: Haddawy
mesh_terms: D001499:Bayes Theorem; D001943:Breast Neoplasms / Q000000981:diagnostic imaging*; D003936:Diagnosis, Computer-Assisted; D005260:Female; D006801:Humans; D008327:Mammography; D016571:Neural Networks, Computer; D012680:Sensitivity and Specificity