ArticleEuropean radiology2021
Radiomics on routine T1-weighted MRI can delineate Parkinson's disease from multiple system atrophy and progressive supranuclear palsy.
Article in European radiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 3 of them syntheses that pooled it.
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Who cites it
24 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Detection of parkinson's disease with neuroimaging modalities using machine learning and artificial intelligence: a systematic review.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2026Pooled it
- The differential diagnosis value of radiomics-based machine learning in Parkinson's disease: a systematic review and meta-analysis.Frontiers in aging neuroscience · 2023Pooled it
- Diagnostic performance of T2* gradient echo, susceptibility-weighted imaging, and quantitative susceptibility mapping for patients with multiple system atrophy-parkinsonian type: a systematic review and meta-analysis.European radiology · 2022Pooled it
- A Brainstem Radiomics Framework to Distinguish Progressive Supranuclear Palsy from Parkinson's Disease.Movement disorders : official journal of the Movement Disorder Society · 2026Article
- Comparison of Three Automated Measurements of Ventricular Volumes of the Brain.Journal of imaging informatics in medicine · 2026Article
- Explainable 3D vision transformer framework for detecting brain anomalies in neuropsychiatric subtypes of Parkinson's disease.Frontiers in neuroinformatics · 2026Article
- Differentiating early Parkinson's disease from multiple system atrophy on multimodal PET/MRI using atlas-guided segmentation and vision transformer classification.Frontiers in aging neuroscience · 2026Article
- Predicting mild cognitive impairment in patients with Parkinson's disease by integrating striatal MRI radiomics with clinical features.BMC medical imaging · 2025Article
- Catecholaminergic nucleus integrity and Alzheimer's pathology, symptoms, and progression.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Article
- Predicting Parkinson's disease and its progression based on radiomics in T1-weight images and α‑synuclein in cerebrospinal fluid.NPJ Parkinson's disease · 2025Article
- Differentiating idiopathic Parkinson's disease from multiple system atrophy-P using brain MRI-based radiomics: a multicenter study.Therapeutic advances in neurological disorders · 2025Article
- Leveraging TabTransformer Deep Learning on Conventional MRI Radiomics for Accessible and Interpretable Diagnosis of Autism Spectrum Disorder.Neuropsychiatric disease and treatment · 2025Article
- Radiomics score derived from T1-w/T2-w ratio image can predict motor symptom progression in Parkinson's disease.European radiology · 2024Article
- Prediction of cognitive decline in Parkinson's disease based on MRI radiomics and clinical features: A multicenter study.CNS neuroscience & therapeutics · 2024Article
- Free water imaging in Parkinson's disease and atypical parkinsonian disorders.Journal of neurology · 2024Article
- Identification of Parkinson's disease and multiple system atrophy using multimodal PET/MRI radiomics.European radiology · 2024Article
- Multi-parametric radiomics of conventional T1 weighted and susceptibility-weighted imaging for differential diagnosis of idiopathic Parkinson's disease and multiple system atrophy.BMC medical imaging · 2023Article
- Accurate measurement of magnetic resonance parkinsonism index by a fully automatic and deep learning quantification pipeline.European radiology · 2023Article
- Radiomics and Hybrid Models Based on Machine Learning to Predict Levodopa-Induced Dyskinesia of Parkinson's Disease in the First 6 Years of Levodopa Treatment.Diagnostics (Basel, Switzerland) · 2023Article
- Automatic diagnosis of Parkinson's disease using artificial intelligence base on routine T1-weighted MRI.Frontiers in medicine · 2023Article
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Authors and funding
6 authors.
Funding
Abstract
objectivesThis study aimed to explore the feasibility of radiomics features extracted from T1-weighted MRI images to differentiate Parkinson's disease (PD) from atypical parkinsonian syndromes (APS).
methodsRadiomics features were computed from T1 images of 65 patients with PD, 61 patients with APS (31: progressive supranuclear palsy and 30: multiple system atrophy), and 75 healthy controls (HC). These features were extracted from 19 regions of interest primarily from subcortical structures, cerebellum, and brainstem. Separate random forest classifiers were applied to classify different groups based on a reduced set of most important radiomics features for each classification as determined by the random forest-based recursive feature elimination by cross-validation method.
resultsThe PD vs HC classifier illustrated an accuracy of 70%, while the PD vs APS classifier demonstrated a superior test accuracy of 92%. Moreover, a 3-way PD/MSA/PSP classifier performed with 96% accuracy. While first-order and texture-based differences like Gray Level Co-occurrence Matrix (GLCM) and Gray Level Difference Matrix for the substantia nigra pars compacta and thalamus were highly discriminative for PD vs HC, textural features mainly GLCM of the ventral diencephalon were highlighted for APS vs HC, and features extracted from the ventral diencephalon and nucleus accumbens were highlighted for the classification of PD and APS.
conclusionsThis study establishes the utility of radiomics to differentiate PD from APS using routine T1-weighted images. This may aid in the clinical diagnosis of PD and APS which may often be indistinguishable in early stages of disease. KEY POINTS: • Radiomics features were extracted from T1-weighted MRI images. • Parkinson's disease and atypical parkinsonian syndromes were classified at an accuracy of 92%. • This study establishes the utility of radiomics to differentiate Parkinson's disease and atypical parkinsonian syndromes using routine T1-weighted images.
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