Evidence map›Paper›PMID 36040832›Full record

ArticleThe Journal of clinical investigation2022

Predicting cognitive decline in Parkinson's disease using FDG-PET-based supervised learning.

Samuel Booth, Kye Won Park, Chong Sik Lee, Ji Hyun Ko

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in The Journal of clinical investigation, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
25citing papers in PubMed, 3 pooled it
5.2field-weighted citation impact, top 3% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

25 citing papers in PubMed, 3 syntheses or guidelines pooled it, 37 citations in OpenAlex.

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  13. Cognitive disorders and parkinson's disease: a literature review.Journal of neural transmission (Vienna, Austria : 1996) · 2025
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  14. The combination of theScientific reports · 2025
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  15. Correlation of Parkinson's disease severity andQuantitative imaging in medicine and surgery · 2025
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors at 2 institutions in 2 countries.

Samuel BoothDepartment of Human Anatomy and Cell Science, Max Rady College of Medicine, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, Manitoba, Canada.
Kye Won ParkDepartment of Neurology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea.
Chong Sik LeeDepartment of Neurology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea.
Ji Hyun KoDepartment of Human Anatomy and Cell Science, Max Rady College of Medicine, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, Manitoba, Canada.
Health Sciences Centre · CAUlsan College · KR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

BackgroundCognitive impairment is a common symptom of Parkinson's disease (PD) that increases in risk and severity as the disease progresses. An accurate prediction of the risk of progression from the mild cognitive impairment (MCI) stage to the dementia (PDD) stage is an unmet clinical need.MethodsWe investigated the use of a supervised learning algorithm called the support vector machine (SVM) to retrospectively stratify patients on the basis of brain fluorodeoxyglucose-PET (FDG-PET) scans. Of 43 patients with PD-MCI according to the baseline scan, 23 progressed to PDD within a 5-year period, whereas 20 maintained stable MCI. The baseline scans were used to train a model, which separated patients identified as PDD converters versus those with stable MCI with 95% sensitivity and 91% specificity.ResultsIn an independent validation data set of 19 patients, the AUC was 0.73, with 67% sensitivity and 80% specificity. The SVM model was topographically characterized by hypometabolism in the temporal and parietal lobes and hypermetabolism in the anterior cingulum and putamen and the insular, mesiotemporal, and postcentral gyri. The performance of the SVM model was further tested on 2 additional data sets, which confirmed that the model was also sensitive to later-stage PDD (17 of 19 patients; 89% sensitivity) and dementia with Lewy bodies (DLB) (16 of 17 patients; 94% sensitivity), but not to normal cognition PD (2 of 17 patients). Finally, anti-PD medication status did not change the SVM classification of the other set of 10 patients with PD who were scanned twice, ON and OFF medication.ConclusionsThese results potentially indicate that the proposed FDG-PET-based SVM classifier has utility for providing an accurate prognosis of dementia development in patients with PD-MCI.

Indexed as

Cognitive DysfunctionDementiaParkinson DiseaseFluorodeoxyglucose F18HumansRetrospective StudiesSupervised Machine LearningFluorodeoxyglucose F18DementiaGlucose metabolismNeuroscienceParkinson disease

Identifiers

PMID36040832
PMCPMC9566889
OpenAlexW4293582093

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.