UNASSIGNED: The differentiation of @entity191 ( @entity191 ) from @entity9576 ( @entity9576 ) and @entity7463 ( @entity7463 ), the most common @entity191 ( @entity191 ), can be clinically challenging. In these disorders, diagnostic inaccuracy is more frequent early in the clinical course when signs and symptoms are mild. Diagnostic inaccuracy may be particularly relevant in trials of potential disease-modifying agents, which typically involve @entity1 with early clinical manifestations. In an initial study (Tang et al., Lancet Neurol 2010), we developed a probabilistic algorithm to classify subjects with clinical @entity304 but uncertain diagnosis based upon the expression of metabolic covariance patterns for @entity191 , @entity9576 , and @entity7463 . Classifications based on this algorithm agreed closely with final clinical diagnosis. Nonetheless, blinded prospective validation is required before routine use of the algorithm can be considered. METHODS: We used metabolic imaging to study an independent cohort of 129 @entity191 subjects with uncertain diagnosis; 77 (60%) had symptoms for 2 years or less at the time of imaging. After imaging, subjects were followed by a blinded @entity717 specialist for an average of 2.2 years before a final diagnosis was made. By applying the algorithm to the individual scan data, the probabilities of @entity191 , @entity9576 and @entity7463 were computed and used to classify each of the subjects. The resulting image-based classifications were then compared to the final clinical diagnosis. RESULTS: Using the original two-level logistical classification algorithm, @entity191 subjects were distinguished from @entity191 with 94% specificity and 96% positive predictive value ( @entity2286 ). The algorithm achieved 90% specificity and 85% @entity2286 for @entity9576 , and 94% specificity and 94% @entity2286 for @entity7463 . Diagnostic accuracy was similarly high (specificity and @entity2286 > 90%) for @entity191 subjects with short symptom duration. In addition, 25 subjects were classified as Level-I Indeterminate @entity304 and four more subjects as Level-II Indeterminate @entity191 . CONCLUSION: Automated pattern-based image classification can improve diagnostic accuracy in @entity1 with @entity304 , even at early disease stages.

Automated Differential Diagnosis of Early XXXX using Metabolic Brain Networks: A Validation Study.

@entity1 :: ('9606', 'Species') :: ['participants', 'patients'] @entity304 :: ('MESH:D010302', 'Disease') :: ['Parkinsonism', 'parkinsonism'] @entity2286 :: ('12211', 'Species') :: ['PPV'] @entity9576 :: ('MESH:D019578', 'Disease') :: ['MSA', 'multiple system atrophy'] @entity191 :: ('MESH:D010300', 'Disease') :: ["idiopathic Parkinson's disease", 'APS', 'atypical parkinsonian "look-alike" syndromes', 'IPD', 'parkinsonian'] @entity717 :: ('MESH:D009069', 'Disease') :: ['movement disorders'] @entity7463 :: ('MESH:D013494', 'Disease') :: ['progressive supranuclear palsy', 'PSP']
@entity304 :: (MESH:D010302,Disease) :: ['Parkinsonism']
