title: Multiclass classification of FDG PET scans for the distinction between Parkinson's disease and atypical parkinsonian syndromes.
abstract: Most available pattern recognition methods in neuroimaging address binary classification problems. Here, we used relevance vector machine (RVM) in combination with booststrap resampling ('bagging') for non-hierarchical multiclass classification. The method was tested on 120 cerebral (18)fluorodeoxyglucose (FDG) positron emission tomography (PET) scans performed in patients who exhibited parkinsonian clinical features for 3.5 years on average but that were outside the prevailing perception for Parkinson's disease (PD). A radiological diagnosis of PD was suggested for 30 patients at the time of PET imaging. However, at follow-up several years after PET imaging, 42 of them finally received a clinical diagnosis of PD. The remaining 78 APS patients were diagnosed with multiple system atrophy (MSA, N = 31), progressive supranuclear palsy (PSP, N = 26) and corticobasal syndrome (CBS, N = 21), respectively. With respect to this standard of truth, classification sensitivity, specificity, positive and negative predictive values for PD were 93% 83% 75% and 96%, respectively using binary RVM (PD vs. APS) and 90%, 87%, 79% and 94%, respectively, using multiclass RVM (PD vs. MSA vs. PSP vs. CBS). Multiclass RVM achieved 45%, 55% and 62% classification accuracy for, MSA, PSP and CBS, respectively. Finally, a majority confidence ratio was computed for each scan on the basis of class pairs that were the most frequently assigned by RVM. Altogether, the results suggest that automatic multiclass RVM classification of FDG PET scans achieves adequate performance for the early differentiation between PD and APS on the basis of cerebral FDG uptake patterns when the clinical diagnosis is felt uncertain. This approach cannot be recommended yet as an aid for distinction between the three APS classes under consideration.
authors.affiliation: Cyclotron Research Centre, Sart Tilman B30, University of Liège, 4000 Liège, Belgium ; Department of Neurology, University Hospital Centre, Sart Tilman B35, 4000 Liège, Belgium.
authors.forename: Gaëtan
authors.initials: G
authors.lastname: Garraux
authors.forename: Christophe
authors.initials: C
authors.lastname: Phillips
authors.forename: Jessica
authors.initials: J
authors.lastname: Schrouff
authors.forename: Alexandre
authors.initials: A
authors.lastname: Kreisler
authors.forename: Christian
authors.initials: C
authors.lastname: Lemaire
authors.forename: Christian
authors.initials: C
authors.lastname: Degueldre
authors.forename: Christian
authors.initials: C
authors.lastname: Delcour
authors.forename: Roland
authors.initials: R
authors.lastname: Hustinx
authors.forename: André
authors.initials: A
authors.lastname: Luxen
authors.forename: Alain
authors.initials: A
authors.lastname: Destée
authors.forename: Eric
authors.initials: E
authors.lastname: Salmon
keywords: Bagging; Boostrap resampling; Computer-aided diagnosis; Corticobasal syndrome; Data mining; Error-Correcting Output Code; FDG PET; Multiclass classification; Multiple system atrophy; Parkinson's disease; Pattern recognition; Progressive supranuclear palsy; Relevance vector machine