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

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 non-hierarchical 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.

18F-FDG PET in Parkinsonism: Differential Diagnosis and Evaluation of Cognitive Impairment

“…2) (72). Three studies used observer-independent automated statistical classifications of patients with parkinsonism that applied logistic regression based on the expression of metabolic covariance patterns (42,73) or a relevance vector machine in combination with bootstrap resampling for nonhierarchic multiclass classification (74).…”

Prediction of standard‐dose brain PET image by using MRI and low‐dose brain [18F]FDG PET images

“…5,6 Moreover, PET has also been widely used for clinically diagnosing brain diseases/disorders. [7][8][9][10][11] High-quality PET images play a crucial role in diagnosing brain diseases/disorders and are preferred in clinical practice for better diagnosis and assessment. Noise in PET images is mainly caused by the finite number of detected photons.…”

Visualization of Decision Tree State for the Classification of Parkinson's Disease

“…Promising results have also been obtained using other methods [9,24]. However, a decision tree is certainly a viable method for separating MSA from healthy controls as shown by the results of a set of leave-one-out cross-validations which were performed in previous work [2].…”