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Multiclass classification of FDG PET scans for the distinction between Parkinson's disease and atypical parkinsonian syndromes

Garraux, G; Phillips, C; Schrouff, J; Kreisler, A; Lemaire, C; Degueldre, C; Delcour, C; ... Salmon, E; + view all (2013) Multiclass classification of FDG PET scans for the distinction between Parkinson's disease and atypical parkinsonian syndromes. NeuroImage: Clinical , 2 pp. 883-893. 10.1016/j.nicl.2013.06.004. Green open access

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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 nonhierarchical 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.

Type: Article
Title: Multiclass classification of FDG PET scans for the distinction between Parkinson's disease and atypical parkinsonian syndromes
Open access status: An open access version is available from UCL Discovery
DOI: 10.1016/j.nicl.2013.06.004
Publisher version: http://dx.doi.org/10.1016/j.nicl.2013.06.004
Language: English
Additional information: © 2013 The Authors. Published by Elsevier Inc. All rights reserved. This article is published under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported licence (CC BY-NC-ND 3.0) (https://creativecommons.org/licenses/by-nc-nd/3.0/)
Keywords: Neuroimaging, Neurosciences & Neurology, Computer-aided diagnosis, Data mining, Pattern recognition, Boostrap resampling, Bagging, Error-Correcting Output Code, Multiclass classification, Relevance vector machine, FDG PET, Parkinson's disease, Multiple system atrophy, Progressive supranuclear palsy, Corticobasal syndrome, MULTIPLE SYSTEM ATROPHY, PROGRESSIVE SUPRANUCLEAR PALSY, CEREBRAL GLUCOSE-METABOLISM, RELEVANCE VECTOR MACHINE, CLINICAL-DIAGNOSIS, DIFFERENTIAL-DIAGNOSIS, CORTICOBASAL DEGENERATION, ACCURACY, DISORDERS, PATTERNS
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/10043760
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