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Record W2905038249

Intrastriatal analysis of FDOPA PET scans for differentiation of Parkinsonian Disorders

2018· article· en· W2905038249 on OpenAlexaboutno aff
Gilles N. Stormezand, Leury Max Da Silva Chaves, Janine Doorduin, David Vállez García, Klaus L. Leenders, Barry Kremer, R. A. Dierckx

Bibliographic record

VenueData Archiving and Networked Services (DANS) · 2018
Typearticle
Languageen
FieldMedicine
TopicNeurological disorders and treatments
Canadian institutionsnot available
Fundersnot available
KeywordsPutamenStriatumMedicineNuclear medicineDopamine transporterCaudate nucleusDopaminergicReceiver operating characteristicParkinson's diseaseInternal medicineDopamineDisease
DOInot available

Abstract

fetched live from OpenAlex

Aim: L-3,4-dihydroxy-6-18F-fluorophenylalanine (FDOPA) PET allows quantification of presynaptic dopaminergic functioning in vivo. It assesses the function of nigrostriatal projection neurons, particularly dopamine release in the striatum. Previously, it was postulated that Idiopathic Parkinson’s disease (IPD) preferentially affects the posterior putamen. This study aims to investigate the potential of FDOPA PET scans to differentiate atypical parkinsonian disorders (APD), idiopathic parkinson’s disease (IPD) and healthy controls. Materials & Methods: 58 patients (28 IPD, 13 APD and 17 controls) who underwent FDOPA PET scan as part of the clinical evaluation and who were diagnosed by movement disorder specialists, were retrospectively analyzed. Average age of IPD, APD and controls was 61.0, 69.6 and 65.4 respectively. All were scanned on a Siemens HR+ camera, with injected dose of 200MBq and pretreated with carbidopa, 2.5 mg/kg orally. After spatial normalization of images in standard MNI space (Montreal Neurological Institute), predefined sets of volumes of interest (VOIs) were used to sample values from striatum and the occipital reference region. Outcome values were striatal-to-occipital ratios (SOR), intrastriatal ratios and slope of multiple in-line spherical VOIs through the striatum anterior to posterior (gradient analysis). All values were compared between groups using ANOVA test and ROC curves were calculated. Results: SOR values showed no statistically significant difference between APD and IPD patients. When analyzing intrastriatal ratios, caudate-to-putamen was found to be statistically significant when comparing APD and IPD (p<0.001; anterior putamen: 0.94±0.06 vs. 1.05±0.10; posterior putamen: 1.10±0.12 vs. 1.26±0.19, respectively). Gradient analysis also showed statistically significant differences between APD and IPD (p=0.006, 0.07±0.05 vs. 0.12±0.07). In ROC curve, separating controls from parkinsonian patients, the caudate-to-posterior putamen ratio showed the highest area under the curve (AUC=0.930), while for differentiating APD from IPD the highest AUC was the caudate-to-anterior putamen ratio (0.824). Conclusion: SOR value is currently used in clinical routine to discriminate healthy from parkinsonian patients. However, results from this study have shown that the best value for this differentiation seems to be the caudate-to-posterior putamen ratio. In addition, caudate-to-anterior putamen is capable to discriminate between atypical and idiopathic parkinsonian patients. These results are in agreement with previous publications, and support the implementation of this ratios in clinical routine. Moreover, the positive results of the gradient analysis support further exploration of this approach, and its possibilities of creating a model that combines different image measurements and/or clinical information to improve the accuracy for discriminating APD and IPD

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.017
GPT teacher head0.277
Teacher spread0.260 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2018
Admission routes1
Has abstractyes

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