Preliminary Investigation of a Bayesian Network for Mammographic Diagnosis of Breast Cancer
Charles E. Kahn, Jr., M.D., Linda M. Roberts, M.S., Kun Wang, B.S., Deb Jenks, M.S.N., Peter Haddawy, Ph.D. The Medical Informatics and Decision Science (MIDAS) Consortium, Milwaukee, Wisconsin
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 patienthistory features, two physical findings, and 15 mammographic features extracted by experienced radiologists. Conditionalprobability data, such as sensitivity and specificity, were derived from peerreviewed 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.