Differentiation between Parkinson disease and other forms of Parkinsonism using support vector machine analysis of susceptibility-weighted imaging (SWI): initial results.
OBJECTIVE
To diagnose Parkinson disease (PD) at the individual level using pattern recognition of brain susceptibility-weighted imaging (SWI).


METHODS
We analysed brain SWI in 36 consecutive patients with Parkinsonism suggestive of PD who had (1) SWI at 3 T, (2) brain (123)I-ioflupane SPECT and (3) extensive neurological testing including follow-up (16 PD, 67.4 ± 6.2 years, 11 female; 20 OTHER, a heterogeneous group of atypical Parkinsonism syndromes 65.2 ± 12.5 years, 6 female). Analysis included group-level comparison of SWI values and individual-level support vector machine (SVM) analysis.


RESULTS
At the group level, simple visual analysis yielded no differences between groups. However, the group-level analyses demonstrated increased SWI in the bilateral thalamus and left substantia nigra in PD patients versus other Parkinsonism. The inverse comparison yielded no supra-threshold clusters. At the individual level, SVM correctly classified PD patients with an accuracy above 86 %.


CONCLUSIONS
SVM pattern recognition of SWI data provides accurate discrimination of PD among patients with various forms of Parkinsonism at an individual level, despite the absence of visually detectable alterations. This pilot study warrants further confirmation in a larger cohort of PD patients and with different MR machines and MR parameters.
Service neuro-diagnostique et neuro-interventionnel DISIM, Hôpitaux Universitaires de Genève, Rue Gabrielle Perret-Gentil 4, 1211, Genève 14, Switzerland. sven.haller@hcuge.ch
S
S
Haller
S
S
Badoud
D
D
Nguyen
I
I
Barnaure
M-L
ML
Montandon
K-O
KO
Lovblad
P R
PR
Burkhard
D000368:Aged; D003937:Diagnosis, Differential; D005260:Female; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D010300:Parkinson Disease / Q000175:diagnosis; D020734:Parkinsonian Disorders / Q000175:diagnosis*; D012189:Retrospective Studies; D018709:Statistics, Nonparametric; D060388:Support Vector Machine; D015899:Tomography, Emission-Computed, Single-Photon