Evidence map›Paper›PMID 42798098›Full record

ArticleMedicine2026

Exploratory SWI-based texture analysis of the substantia nigra on MRI in parkinson's disease.

Duran Tavut, Murat Baykara, Hanefi Yildirim

Abstract read
In one paragraph

Article in Medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

3 authors.

Duran TavutDepartment of Radiology, Gaziantep Cengiz Gökçek Gynecology and Children's Hospital, Gaziantep, Turkey.ORCID 0000-0003-1726-6190
Murat BaykaraDepartment of Radiology, Haydarpasa Numune Training and Research Hospital, Istanbul, Turkey.ORCID 0000-0003-2588-9013
Hanefi YildirimDepartment of Radiology, Faculty of Medicine, Firat University, Elazig, Turkey.ORCID 0000-0002-6204-6392

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study aimed to compare the substantia nigra of patients with Parkinson's disease (PD) and healthy controls using magnetic resonance imaging texture analysis findings. Texture analysis was performed on the pixels within each region of interest in the substantia nigra for all participants. The entire imaging analysis workflow was implemented using an in-house algorithm developed using Matrix Laboratory. This study included 76 participants (37 patients and 39 controls). There were no significant differences between the groups in terms of age or sex (P > .05). In the unadjusted analysis, kurtosis, skewness, and uniformity differed significantly between patients and controls (P < .05); however, after correction for multiple comparisons, only kurtosis remained statistically significant. Receiver operating characteristic curve analysis demonstrated moderate discriminative performance, with kurtosis showing the most robust results. These findings suggest a potential supportive role of susceptibility-weighted imaging-based texture analysis in PD; however, given the exploratory design and the absence of external validation, the results should be interpreted with caution. The findings of this study suggest that texture analysis applied to imaging data of patients with PD may have a potential supportive role in the diagnostic evaluation of the disease.

Indexed as

Magnetic Resonance ImagingParkinson DiseaseSubstantia NigraAgedAlgorithmsCase-Control StudiesFemaleHumansImage Interpretation, Computer-AssistedImage Processing, Computer-AssistedMaleMiddle AgedROC Curvecomputer-assistedimage processingmagnetic resonance imagingParkinson’s diseasepars compactapars reticulata

Identifiers

PMID42798098
PMCPMC13619280

What OpenQuestion holds

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Registered trials

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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 OpenQuestion graph.