Ricardo Martins1, Francisco Paulo Marques Oliveira2, Fradique Moreira3, Ana Paula Moreira4, Antero Abrunhosa4, Cristina Januario4, Miguel Castelo-Branco4. 1. Coimbra Institute for Biomedical Imaging and Translational Research, University of Coimbra Institute of Nuclear Sciences Applied to Health, ICNAS Polo 3, Coimbra, 3000-548, PORTUGAL. 2. Nuclear Medicine - Radiopharmacology, Champalimaud Foundation, Av. Brasília, Lisboa, 1400-038, PORTUGAL. 3. Neurology, Hospital and University Centre of Coimbra, Icnas Polo 3, Coimbra, 3000-075, PORTUGAL. 4. Coimbra Institute for Biomedical Imaging and Translational Research (CIBIT), ICNAS Polo 3, Coimbra, 3000-548, PORTUGAL.
Abstract
OBJECTIVE: To explore the viability of developing a computer-aided diagnostic system for Parkinsonian syndromes using dynamic [11C]raclopride PET and T1-weighted 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: 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 PET data were used to quantify the reversible tracer binding to D2/D3 receptors in the subcortical regions of interest (ROI). 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. Creative Commons Attribution license.
OBJECTIVE: To explore the viability of developing a computer-aided diagnostic system for Parkinsonian syndromes using dynamic [11C]raclopride PET and T1-weighted 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: 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 PET data were used to quantify the reversible tracer binding to D2/D3 receptors in the subcortical regions of interest (ROI). 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. Creative Commons Attribution license.