[en] 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 18fluorodeoxyglucose (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.
Disciplines :
Neurology
Author, co-author :
Garraux, Gaëtan ✱; Université de Liège - ULiège > Département des sciences cliniques > Neurologie
Phillips, Christophe ✱; Université de Liège - ULiège > Centre de recherches du cyclotron
Schrouff, Jessica ; Université de Liège - ULiège > Centre de recherches du cyclotron
Kreisler, Alexandre
Lemaire, Christian ; Université de Liège - ULiège > Centre de recherches du cyclotron
Degueldre, Christian ; Université de Liège - ULiège > Centre de recherches du cyclotron
Delcour, Christian
Hustinx, Roland ; Université de Liège - ULiège > Département des sciences cliniques > Médecine nucléaire
Luxen, André ; Université de Liège - ULiège > Département de chimie (sciences) > Chimie organique de synthèse
Destée, Alain
Salmon, Eric ; Université de Liège - ULiège > Département des sciences cliniques > Neuroimagerie des troubles de la mémoire et révalid. cogn.
✱ These authors have contributed equally to this work.
Language :
English
Title :
Multiclass classification of FDG PET scans for the distinction between Parkinson's disease and atypical parkinsonian syndromes
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