Date: Monday, October 8, 2018
Session Title: Parkinson's Disease: Neuroimaging And Neurophysiology
Session Time: 1:15pm-2:45pm
Location: Hall 3FG
Objective: To discriminate among parkinsonian patients using an automated network analysis of FDG-PET brain images.
Background: Clinical differentiating among parkinsonian syndromes: Parkinson’s disease (PD), multiple system atrophy (MSA) and progressive supranuclear palsy (PSP) may be challenging early in disease course. Disease specific metabolic brain patterns were identified for PD, MSA and PSP using Scaled Subprofile Modelling/Principal Component Analysis of FDG-PET images: PD related pattern (PDRP), MSA related pattern (MSARP) and PSP related pattern (PSPRP) (Poston KL, et al. 2010). Additionally, a probabilistic algorithm for the classification of individual patients with clinically uncertain parkinsonism was developed based on the expression of metabolic covariance patterns specific for PD, MSA and PSP (Tang CC, et al. 2010).
Methods: 137 patients with parkinsonism (disease duration 4.9±4 years) and uncertain clinical diagnosis underwent diagnostic FDG-PET. A network analysis was performed and the expressions of PDRP, MSARP and PSPRP were calculated using Topographic Profile Rating method. A probability for PD, MSA and PSP was calculated using the two stage logistic algorithm that distinguishes between PD and atypical parkinsonian syndrome (APS) in the first stage, and between MSA and PSP in the second. The calculated diagnosis was than compared to final clinical diagnosis made 21.4±13 months after imaging.
Results: Among 137 patients, 38 were excluded for alternative (not PD, MSA or PSP) diagnosis. Among 99 patients 66 were clinically diagnosed with PD, 17 with MSA and 16 with PSP. Automated algorithm diagnosed 57 patients with PD, 9 with MSA, 18 with PSP. Results were indeterminate in 15 cases. Discriminative measures of diagnostic accuracy for the first stage (PD vs atypical parkinsonian syndrome (APS)) and the second stage of diagnostic algorithm (MSA and PSP) are presented in table 1 (PPV – positive predictive value, NPV – negative predictive value). [table 1]
Conclusions: FDG-PET brain imaging with an automated differential diagnostic algorithm (Tang CC, et al. 2010) is a reliable tool for the improvement of early diagnosis in patients with uncertain parkinsonism.
References: 1. Poston KL, Eidelberg D. FDG PET in the Evaluation of Parkinson’s Disease. PET Clin. 2010;5(1):55-64. 2. Tang CC, Poston KL, Eckert T, et al. Differential diagnosis of parkinsonism: a metabolic imaging study using pattern analysis. Lancet Neurol. 2010;9(2):149-58.
To cite this abstract in AMA style:T. Rus, P. Tomše, L. Jensterle, M. Grmek, Z. Pirtošek, D. Eidelberg, C. Tang, M. Trošt. Automated Differential Diagnosis of Parkinsonian Syndromes Using FDG-PET Metabolic Brain Network Analysis [abstract]. Mov Disord. 2018; 33 (suppl 2). https://www.mdsabstracts.org/abstract/automated-differential-diagnosis-of-parkinsonian-syndromes-using-fdg-pet-metabolic-brain-network-analysis/. Accessed December 11, 2023.
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MDS Abstracts - https://www.mdsabstracts.org/abstract/automated-differential-diagnosis-of-parkinsonian-syndromes-using-fdg-pet-metabolic-brain-network-analysis/