title: A vector machine formulation with application to the computer-aided diagnosis of breast cancer from DCE-MRI screening examinations.
abstract: This study investigates the use of a proposed vector machine formulation with application to dynamic contrast-enhanced magnetic resonance imaging examinations in the context of the computer-aided diagnosis of breast cancer. This paper describes a method for generating feature measurements that characterize a lesion's vascular heterogeneity as well as a supervised learning formulation that represents an improvement over the conventional support vector machine in this application. Spatially varying signal-intensity measures were extracted from the examinations using principal components analysis and the machine learning technique known as the support vector machine (SVM) was used to classify the results. An alternative vector machine formulation was found to improve on the results produced by the established SVM in randomized bootstrap validation trials, yielding a receiver-operating characteristic curve area of 0.82 which represents a statistically significant improvement over the SVM technique in this application.
authors.affiliation: Institute of Biomedical Engineering, Department of Engineering Science, Old Road Campus Research Building, University of Oxford, Headington, Oxford, Oxfordshire, OX1 3PJ, UK, jacob.levman@eng.ox.ac.uk.
authors.forename: Jacob E D
authors.initials: JE
authors.lastname: Levman
authors.forename: Ellen
authors.initials: E
authors.lastname: Warner
authors.forename: Petrina
authors.initials: P
authors.lastname: Causer
authors.forename: Anne L
authors.initials: AL
authors.lastname: Martel
mesh_terms: D000465:Algorithms; D001940:Breast / Q000473:pathology*; D001943:Breast Neoplasms / Q000175:diagnosis*; D003287:Contrast Media; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D005260:Female; D019786:Gadolinium DTPA; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008403:Mass Screening / Q000379:methods*; D012372:ROC Curve; D012680:Sensitivity and Specificity; D060388:Support Vector Machine
chemical_list: D003287:Contrast Media; D019786:Gadolinium DTPA