Automated Computer Differential Classification in Parkinsonian Syndromes via Pattern Analysis on MRI

Rationale and Objectives. Reported error rates for initial clinical diagnosis of idiopathic Parkinson's disease (IPD) against other Parkinson Plus Syndromes (PPS) can reach up to 35%. Reducing this initial error rate is an important research goal. We evaluated the ability of an automated technique, based on structural, cross-sectional T1-weighted (T1w) magnetic resonance imaging, to perform differential classification of IPD patients versus those with either progressive supranuclear palsy (PSP) or multiple systems atrophy (MSA). Materials and Methods. A total of 181 subjects were included in this retrospective study: 149 healthy controls, 16 IPD patients, and 16 patients diagnosed with either probable PSP (n = 8) or MSA (n = 8). Cross-sectional T1w magnetic resonance imagers were acquired and subsequently corrected, scaled, resampled, and aligned within a common referential space. Tissue composition and deformation features in the hindbrain region were then automatically extracted. Classification of patients was performed using a Support vector machine with least-squares optimization within a Multidimensional composition/deformation feature space built from the healthy Subjects' data. Leave-one-out classification was used to avoid over-determination. Results. There were no age difference between groups. The automated system obtained 91% accuracy (agreement with long-term clinical follow-up), 88% specificity, and 93% sensitivity. Conclusion. These results demonstrate that a classification approach based oil quantitative parameters of three-dimensional hindbrain morphology extracted automatically from T1w magnetic resonance imaging has the potential to assist in the differential diagnosis of IPD versus PSP and MSA with high accuracy, therefore reducing the initial clinical error rate.