title: A Bayesian network for diagnosis of primary bone tumors.
abstract: The authors developed a Bayesian network to differentiate among five benign and five malignant neoplasms of the appendicular skeleton using the patient's age and sex and 17 radiographic characteristics. In preliminary evaluation with physicians in training, the model identified the correct diagnosis in 19 cases (68%), and included the correct diagnosis among the two most probable diagnoses in 25 cases (89%). Bayesian networks can capture and apply knowledge of primary bone neoplasms. Further testing and refinement of the model are underway.
authors.affiliation: Department of Radiology, Medical College of Wisconsin, Milwaukee 53226, USA. kahn@mcw.edu
authors.forename: C E
authors.initials: CE
authors.lastname: Kahn
authors.forename: J J
authors.initials: JJ
authors.lastname: Laur
authors.forename: G F
authors.initials: GF
authors.lastname: Carrera
mesh_terms: D001499:Bayes Theorem; D001859:Bone Neoplasms / Q000000981:diagnostic imaging*; D003937:Diagnosis, Differential; D005260:Female; D006801:Humans; D008297:Male; D015233:Models, Statistical; D011859:Radiography