ArticleScientific reports2026
Habitat-based MRI radiomics for enhanced parkinson's diagnosis.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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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.
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.
Who cites it
1 citing paper in PubMed.
- Exploratory radiomics analysis of T1-weighted and FLAIR MRI in relation to Parkinson's disease motor subtypes.Frontiers in neurology · 2026Article
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Early and accurate diagnosis of Parkinson’s disease (PD) remains a clinical challenge. This study explores the potential of habitat-based radiomics as a novel approach to improve PD detection using routine clinical MRI scans. We analyzed MRI data from 308 participants (173 PD patients and 135 healthy controls) to extract detailed features from segmented habitats in the caudate nucleus and putamen. Machine learning models, trained on habitat-based radiomic features, achieved a diagnostic accuracy exceeding 94%. This superior performance, compared to traditional radiomics, highlights the ability of habitat-based radiomics to capture subtle tissue heterogeneity associated with PD. Our findings suggest that habitat-based radiomics could be a valuable tool for early and accurate PD diagnosis, enabling timely intervention and improved patient outcomes.
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Registered trials
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