ArticleNeuroradiology2017
Parkinson's disease: diagnostic utility of volumetric imaging.
Article in Neuroradiology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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.
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Who cites it
7 citing papers in PubMed.
- Volumetric signatures of basal ganglia-thalamo-cortical and cerebello-thalamo-cortical networks in Parkinson's disease and its motor subtypes.Frontiers in aging neuroscience · 2026Article
- Decoding Brain Development and Aging: Pioneering Insights From MRI Techniques.Investigative radiology · 2025Review
- Amplitude of low-frequency fluctuation-based regional radiomics similarity network: Biomarker for Parkinson's disease.Heliyon · 2023Article
- Classification of Parkinson's disease using a region-of-interest- and resting-state functional magnetic resonance imaging-based radiomics approach.Brain imaging and behavior · 2022Article
- Machine Learning for Detecting Parkinson's Disease by Resting-State Functional Magnetic Resonance Imaging: A Multicenter Radiomics Analysis.Frontiers in aging neuroscience · 2022Article
- Utility of Multi-Modal MRI for Differentiating of Parkinson's Disease and Progressive Supranuclear Palsy Using Machine Learning.Frontiers in neurology · 2021Article
- Extraction of large-scale structural covariance networks from grey matter volume for Parkinson's disease classification.European radiology · 2018Article
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeThis paper aims to examine the effectiveness of structural imaging as an aid in the diagnosis of Parkinson's disease (PD).
methodsHigh-resolution T
resultsSmaller volume in susceptible regions is diagnostic for Parkinson's disease. Compared with the other two classifiers, the basal ganglion prior/data-driven hybrid classifier had the highest diagnostic reliability with a sensitivity of 74%, specificity of 75%, and accuracy of 74%. Furthermore, outcome measurement using this classifier was associated with disease severity.
conclusionsBrain structural volumetric analysis with multiple logistic regression modeling can be a complementary tool for diagnosing PD.
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Registered trials
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