Automatic classification of idiopathic Parkinson’s disease and atypical Parkinsonian syndromes combining [11C]raclopride PET uptake and MRI grey matter morphometry Article Swipe
Ricardo Martins
,
Francisco P. M. Oliveira
,
Fradique Moreira
,
Ana Paula Moreira
,
Antero Abrunhosa
,
Cristina Januário
,
Miguel Castelo‐Branco
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1088/1741-2552/abf772
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1088/1741-2552/abf772
Objective. To explore the viability of developing a computer-aided diagnostic system for Parkinsonian syndromes using dynamic [ 11 C]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 [ 11 C]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.
Related Topics
Concepts
Corticobasal degeneration
Progressive supranuclear palsy
Dementia with Lewy bodies
Binding potential
Parkinsonism
Raclopride
Parkinson's disease
Nuclear medicine
Positron emission tomography
Magnetic resonance imaging
Atrophy
Medicine
Grey matter
Pathology
Psychology
Dementia
Radiology
White matter
Disease
Internal medicine
Striatum
Dopamine
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1088/1741-2552/abf772
- https://iopscience.iop.org/article/10.1088/1741-2552/abf772/pdf
- OA Status
- hybrid
- Cited By
- 23
- References
- 58
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3154302289
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W3154302289Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1088/1741-2552/abf772Digital Object Identifier
- Title
-
Automatic classification of idiopathic Parkinson’s disease and atypical Parkinsonian syndromes combining [11C]raclopride PET uptake and MRI grey matter morphometryWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-04-14Full publication date if available
- Authors
-
Ricardo Martins, Francisco P. M. Oliveira, Fradique Moreira, Ana Paula Moreira, Antero Abrunhosa, Cristina Januário, Miguel Castelo‐BrancoList of authors in order
- Landing page
-
https://doi.org/10.1088/1741-2552/abf772Publisher landing page
- PDF URL
-
https://iopscience.iop.org/article/10.1088/1741-2552/abf772/pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
- OA URL
-
https://iopscience.iop.org/article/10.1088/1741-2552/abf772/pdfDirect OA link when available
- Concepts
-
Corticobasal degeneration, Progressive supranuclear palsy, Dementia with Lewy bodies, Binding potential, Parkinsonism, Raclopride, Parkinson's disease, Nuclear medicine, Positron emission tomography, Magnetic resonance imaging, Atrophy, Medicine, Grey matter, Pathology, Psychology, Dementia, Radiology, White matter, Disease, Internal medicine, Striatum, DopamineTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
23Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 6, 2024: 4, 2023: 7, 2022: 6Per-year citation counts (last 5 years)
- References (count)
-
58Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.(93.3%, | 215 |
| abstract_inverted_index.(96.7%, | 204 |
| abstract_inverted_index.allowed | 53 |
| abstract_inverted_index.atrophy | 96, 176 |
| abstract_inverted_index.binding | 150 |
| abstract_inverted_index.designs | 56 |
| abstract_inverted_index.disease | 89 |
| abstract_inverted_index.dynamic | 16, 62 |
| abstract_inverted_index.explore | 3 |
| abstract_inverted_index.healthy | 80 |
| abstract_inverted_index.highest | 195, 233 |
| abstract_inverted_index.imaging | 28, 51, 292 |
| abstract_inverted_index.kinetic | 136 |
| abstract_inverted_index.regions | 157 |
| abstract_inverted_index.renders | 38 |
| abstract_inverted_index.tested. | 59 |
| abstract_inverted_index.volumes | 165 |
| abstract_inverted_index.(59.5%). | 279 |
| abstract_inverted_index.(DVR-GM) | 202 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.Datasets | 60 |
| abstract_inverted_index.Results. | 183 |
| abstract_inverted_index.accuracy | 197, 235, 264 |
| abstract_inverted_index.achieved | 193 |
| abstract_inverted_index.acquired | 72 |
| abstract_inverted_index.atypical | 128 |
| abstract_inverted_index.balanced | 196, 234, 263 |
| abstract_inverted_index.category | 125, 298 |
| abstract_inverted_index.controls | 81 |
| abstract_inverted_index.dementia | 108 |
| abstract_inverted_index.designed | 211 |
| abstract_inverted_index.emission | 21 |
| abstract_inverted_index.features | 203, 214, 220, 239, 248, 270, 275, 293, 321 |
| abstract_inverted_index.followed | 207 |
| abstract_inverted_index.improved | 305 |
| abstract_inverted_index.inferior | 251 |
| abstract_inverted_index.interest | 159 |
| abstract_inverted_index.magnetic | 26 |
| abstract_inverted_index.multiple | 94 |
| abstract_inverted_index.obtained | 138, 166 |
| abstract_inverted_index.patients | 86, 120 |
| abstract_inverted_index.positron | 20 |
| abstract_inverted_index.produced | 250 |
| abstract_inverted_index.quantify | 146, 174 |
| abstract_inverted_index.revealed | 276 |
| abstract_inverted_index.weighted | 262 |
| abstract_inverted_index.Approach. | 31 |
| abstract_inverted_index.combining | 198 |
| abstract_inverted_index.contrast, | 225 |
| abstract_inverted_index.cortical, | 178 |
| abstract_inverted_index.different | 54 |
| abstract_inverted_index.molecular | 50, 291 |
| abstract_inverted_index.receptors | 153 |
| abstract_inverted_index.resonance | 27 |
| abstract_inverted_index.syndromes | 14, 37 |
| abstract_inverted_index.viability | 5 |
| abstract_inverted_index.Objective. | 1 |
| abstract_inverted_index.biological | 33 |
| abstract_inverted_index.cerebellar | 181 |
| abstract_inverted_index.challenge. | 43 |
| abstract_inverted_index.classified | 122 |
| abstract_inverted_index.classifier | 55, 227, 254 |
| abstract_inverted_index.designated | 126 |
| abstract_inverted_index.developing | 7 |
| abstract_inverted_index.diagnostic | 10 |
| abstract_inverted_index.multiclass | 323 |
| abstract_inverted_index.parameters | 137 |
| abstract_inverted_index.reversible | 148 |
| abstract_inverted_index.structural | 48, 289 |
| abstract_inverted_index.tomography | 22 |
| abstract_inverted_index.(concerning | 311 |
| abstract_inverted_index.T1-weighted | 25, 68 |
| abstract_inverted_index.classifiers | 185, 210 |
| abstract_inverted_index.combination | 46, 287 |
| abstract_inverted_index.demonstrate | 304 |
| abstract_inverted_index.integrated. | 273 |
| abstract_inverted_index.integration | 242 |
| abstract_inverted_index.performance | 278, 318 |
| abstract_inverted_index.statistical | 40 |
| abstract_inverted_index.subcortical | 156 |
| abstract_inverted_index.C]raclopride | 19, 65 |
| abstract_inverted_index.Parkinsonian | 13, 36 |
| abstract_inverted_index.Parkinsonism | 129 |
| abstract_inverted_index.corticobasal | 101 |
| abstract_inverted_index.degeneration | 102 |
| abstract_inverted_index.distribution | 132 |
| abstract_inverted_index.subcortical, | 179 |
| abstract_inverted_index.Parkinson’s | 88 |
| abstract_inverted_index.Significance. | 280 |
| abstract_inverted_index.heterogeneity | 34 |
| abstract_inverted_index.participants. | 77 |
| abstract_inverted_index.performances. | 252 |
| abstract_inverted_index.classification | 41, 307 |
| abstract_inverted_index.computer-aided | 9 |
| abstract_inverted_index.respectively), | 206, 217 |
| abstract_inverted_index.respectively). | 223 |
| abstract_inverted_index.classifications. | 299, 324 |
| abstract_inverted_index.healthy/diseased | 309 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 97 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 7 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/3 |
| sustainable_development_goals[0].score | 0.5699999928474426 |
| sustainable_development_goals[0].display_name | Good health and well-being |
| citation_normalized_percentile.value | 0.87721555 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |