title: A Bayesian diagnostic system to differentiate glioblastomas from solitary brain metastases.
abstract: This paper aimed to construct a Bayesian network-based decision support system to differentiate glioblastomas from solitary metastases, based on multimodality MR examination. We enrolled 51 patients with solitary brain tumors (26 with glioblastomas and 25 with solitary brain metastases). These patients underwent contrast-enhanced T1-weighted magnetic resonance (MR) examination, diffusion tensor imaging (DTI), dynamic susceptibility contrast (DSC) MRI, and fluid-attenuated inversion recovery (FLAIR). We generated a set of MR biomarkers, including relative cerebral blood volume in the enhancing region, and fractional anisotropy measured in the immediate peritumoral area. We then generated a Bayesian network model to represent associations among these imaging-derived predictors, and the group membership variable, (glioblastoma or solitary metastasis). This Bayesian network can be used to classify new patients' tumors based on their MR appearance. The Bayesian network model accurately differentiated glioblastomas from solitary metastases. Prediction accuracy was 0.94 (sensitivity = 0.96, specificity = 0.92) based on leave-one-out cross-validation. The area under the receiver operating characteristic curve was 0.90. A Bayesian network-based decision support system accurately differentiates glioblastomas from solitary metastases, based on MR-derived biomarkers.
authors.affiliation: Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland Medical Center, Baltimore, MD 21201, USA. rchen@umm.edu
authors.forename: R
authors.initials: R
authors.lastname: Chen
authors.forename: S
authors.initials: S
authors.lastname: Wang
authors.forename: H
authors.initials: H
authors.lastname: Poptani
authors.forename: E R
authors.initials: ER
authors.lastname: Melhem
authors.forename: E H
authors.initials: EH
authors.lastname: Herskovits
mesh_terms: D000328:Adult; D000368:Aged; D001499:Bayes Theorem; D015415:Biomarkers / Q000378:metabolism; D001921:Brain / Q000473:pathology*; D001932:Brain Neoplasms / Q000175:diagnosis* / Q000556:secondary*; D038524:Diffusion Magnetic Resonance Imaging; D005260:Female; D005909:Glioblastoma / Q000175:diagnosis* / Q000556:secondary*; D006801:Humans; D007090:Image Interpretation, Computer-Assisted; D008279:Magnetic Resonance Imaging; D008297:Male; D008875:Middle Aged; D008954:Models, Biological; D012372:ROC Curve; D012680:Sensitivity and Specificity
chemical_list: D015415:Biomarkers