ArticleEuropean radiology2024
Identification of Parkinson's disease and multiple system atrophy using multimodal PET/MRI radiomics.
Article in European radiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
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Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it, 36 citations in OpenAlex.
- Diagnostic performance of PET-based artificial intelligence for differentiating Parkinson's disease from normal controls or atypical parkinsonism: a systematic review and meta-analysis.Journal of neurology · 2025Pooled it
- Multimodal PET Imaging Unveils Early Metabolic Compensation Mechanisms and Dopaminergic Degeneration in Parkinson's Disease.Cerebellum (London, England) · 2026Article
- Advancing fungal sinusitis diagnosis: a radiomics and machine learning approach.BMC medical imaging · 2026Article
- A stacked multi-classifier for multi-modal data fusion in transcranial sonography-based Parkinson's disease assessment.NPJ Parkinson's disease · 2026Article
- Prediction of depression development in Parkinson's disease patients: a radiomics-based machine learning study.BMC neurology · 2026Article
- Habitat-based MRI radiomics for enhanced parkinson's diagnosis.Scientific reports · 2026Article
- Enhancing interpretability of AI with radiomics-based deep neural network: proof of concept in the classification of Parkinsonian syndromes withEuropean journal of nuclear medicine and molecular imaging · 2026Article
- Review
- Multimodal MRI Radiomics and Machine Learning Identify Node-Level Structural and Functional Alterations in HIV-Associated Asymptomatic Neurocognitive Impairment.Neuropsychiatric disease and treatment · 2026Article
- Enhancing 3D dopamine transporter imaging as a biomarker for Parkinson's disease via self-supervised learning with diffusion models.Cell reports. Medicine · 2025Article
- The combination of theScientific reports · 2025Article
- Overlooked and underpowered: a meta-research addressing sample size in radiomics prediction models for binary outcomes.European radiology · 2025Article
- Combined with multimodal medical imaging and artificial intelligence for early diagnosis of Parkinson's disease.Frontiers in neurology · 2025Article
- Explainable graph neural network based on metabolic brain imaging for differential diagnosis of parkinsonism.Frontiers in aging neuroscience · 2025Article
- Individualized diagnosis of Parkinson's disease based on multivariate magnetic resonance imaging radiomics and clinical indexes.Frontiers in aging neuroscience · 2025Article
- Progress of radiomics research on white matter hyperintensity lesions.Frontiers in neurology · 2025Review
- Radiomics-Guided Deep Learning Networks Classify Differential Diagnosis of Parkinsonism.Brain sciences · 2024Article
- Diagnostic performance of artificial intelligence-assisted PET imaging for Parkinson's disease: a systematic review and meta-analysis.NPJ digital medicine · 2024Article
- A more objective PD diagnostic model: integrating texture feature markers of cerebellar gray matter and white matter through machine learning.Frontiers in aging neuroscience · 2024Article
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Authors and funding
9 authors at 2 institutions in 1 country.
Funding
Abstract
objectivesTo construct a machine learning model for differentiating Parkinson's disease (PD) and multiple system atrophy (MSA) by using multimodal PET/MRI radiomics and clinical characteristics.
methodsOne hundred and nineteen patients (81 with PD and 38 with MSA) underwent brain PET/CT and MRI to obtain metabolic images ([
resultsThe combined PET/MRI radiomics model of five sequences outperformed monomodal radiomics models alone. Further, PET/MRI radiomics-clinical combined model could perfectly distinguish PD from MSA (AUC = 0.993), which outperformed the clinical model (AUC = 0.923, p = 0.028) in training set, with no significant difference in test set (AUC = 0.860 vs 0.917, p = 0.390). However, no significant difference was found between PET/MRI radiomics-clinical model and PET/MRI radiomics model in training (AUC = 0.988, p = 0.276) and test sets (AUC = 0.860 vs 0.845, p = 0.632). DCA demonstrated the highest clinical benefit of PET/MRI radiomics-clinical model.
conclusionsOur study indicates that multimodal PET/MRI radiomics could achieve promising performance to differentiate between PD and MSA in clinics. CLINICAL RELEVANCE STATEMENT: This study developed an optimal radiomics signature and construct model to distinguish PD from MSA by multimodal PET/MRI imaging methods in clinics for parkinsonian syndromes, which achieved an excellent performance. KEY POINTS: •Multimodal PET/MRI radiomics from putamina and caudate nuclei increase the diagnostic efficiency for distinguishing PD from MSA. •The radiomics-based nomogram was developed to differentiate between PD and MSA. •Combining PET/MRI radiomics-clinical model achieved promising performance to identify PD and MSA.
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