Please use this identifier to cite or link to this item: http://hdl.handle.net/10316/94609
Title: Automatic classification of idiopathic Parkinson's disease and atypical parkinsonian syndromes combining [11C]raclopride PET uptake and MRI grey matter morphometry
Authors: Martins, Ricardo Filipe Alves 
Oliveira, Francisco Paulo Marques de 
Moreira, Fradique Vieira de Almeida 
Moreira, Ana Paula 
Abrunhosa, Antero José Pena Afonso de 
Santos, Maria Cristina Januário 
Castelo-Branco, Miguel 
Keywords: 11C-Raclopride positron emission tomography; Computer-aided diagnosis; Parkinsonian syndromes; machine learning; magnetic resonance imaging; multimodality imaging
Issue Date: 13-Apr-2021
Serial title, monograph or event: Journal of Neural Engineering
Volume: 18
Issue: 4
Abstract: Objective. To explore the viability of developing a computer-aided diagnostic system for Parkinsonian syndromes using dynamic [11C]raclopride positron emission tomography (PET) and T1-weighted magnetic resonance imaging (MRI) data. Approach. The biological heterogeneity of Parkinsonian syndromes renders their statistical classification a challenge. The unique combination of structural and molecular imaging data allowed different classifier designs to be tested. Datasets from dynamic [11C]raclopride PET and T1-weighted MRI scans were acquired from six groups of participants. There were healthy controls (CTRL n = 15), patients with Parkinson's disease (PD n = 27), multiple system atrophy (MSA n = 8), corticobasal degeneration (CBD n = 6), and dementia with Lewy bodies (DLB n = 5). MSA, CBD, and DLB patients were classified into one category designated as atypical Parkinsonism (AP). The distribution volume ratio (DVR) kinetic parameters obtained from the PET data were used to quantify the reversible tracer binding to D2/D3 receptors in the subcortical regions of interest (ROI). The grey matter (GM) volumes obtained from the MRI data were used to quantify GM atrophy across cortical, subcortical, and cerebellar ROI. Results. The classifiers CTRL vs PD and CTRL vs AP achieved the highest balanced accuracy combining DVR and GM (DVR-GM) features (96.7%, 92.1%, respectively), followed by the classifiers designed with DVR features (93.3%, 88.8%, respectively), and GM features (69.6%, 86.1%, respectively). In contrast, the classifier PD vs AP showed the highest balanced accuracy (78.9%) using DVR features only. The integration of DVR-GM (77.9%) and GM features (72.7%) produced inferior performances. The classifier CTRL vs PD vs AP showed high weighted balanced accuracy when DVR (80.5%) or DVR-GM features (79.9%) were integrated. GM features revealed poorer performance (59.5%). Significance. This work was unique in its combination of structural and molecular imaging features in binary and triple category classifications. We were able to demonstrate improved binary classification of healthy/diseased status (concerning both PD and AP) and equate performance to DVR features in multiclass classifications.
URI: http://hdl.handle.net/10316/94609
ISSN: 1741-2560
1741-2552
DOI: 10.1088/1741-2552/abf772
Rights: openAccess
Appears in Collections:I&D ICNAS - Artigos em Revistas Internacionais

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