ArticleFrontiers in aging neuroscience2020
MRI-Based Radiomics of Basal Nuclei in Differentiating Idiopathic Parkinson's Disease From Parkinsonian Variants of Multiple System Atrophy: A Susceptibility-Weighted Imaging Study.
Article in Frontiers in aging neuroscience, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled 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
- A Brainstem Radiomics Framework to Distinguish Progressive Supranuclear Palsy from Parkinson's Disease.Movement disorders : official journal of the Movement Disorder Society · 2026Article
- Ultrasound-based radiomics and machine learning for enhanced diagnosis of knee osteoarthritis: Evaluation of diagnostic accuracy, sensitivity, specificity, and predictive value.European journal of radiology open · 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
- Predicting the invasiveness of ground-glass opacity predominant lung adenocarcinoma with clinical stage Ia: a CT-based semantic and radiomics analysis.Journal of thoracic disease · 2024Article
- Automatic substantia nigra segmentation with Swin-Unet in susceptibility- and T2-weighted imaging: application to Parkinson disease diagnosis.Quantitative imaging in medicine and surgery · 2024Article
- Analysis of cognitive dysfunction in Parkinson's disease using voxel based morphometry and radiomics.Cognitive processing · 2024Article
- 3D-CAM: a novel context-aware feature extraction framework for neurological disease classification.Frontiers in neuroscience · 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
- Using histogram analysis of the intrinsic brain activity mapping to identify essential tremor.Frontiers in neurology · 2023Article
- Multimodal striatal neuromarkers in distinguishing parkinsonian variant of multiple system atrophy from idiopathic Parkinson's disease.CNS neuroscience & therapeutics · 2022Article
- A triple-classification for differentiating renal oncocytoma from renal cell carcinoma subtypes and CK7 expression evaluation: a radiomics analysis.BMC urology · 2022Article
- A triple-classification for the evaluation of lung nodules manifesting as pure ground-glass sign: a CT-based radiomic analysis.BMC medical imaging · 2022Article
- Diffusion Tensor Imaging Radiomics for Diagnosis of Parkinson's Disease.Brain sciences · 2022Article
- Neuroimaging at 7 Tesla: a pictorial narrative review.Quantitative imaging in medicine and surgery · 2022Review
- Automated Differentiation of Atypical Parkinsonian Syndromes Using Brain Iron Patterns in Susceptibility Weighted Imaging.Diagnostics (Basel, Switzerland) · 2022Article
- Modified Iron Deposition in Nigrosomes by Pharmacotherapy for the Management of Parkinson's Disease.Frontiers in molecular biosciences · 2022Article
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5 authors.
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Abstract
objectivesTo investigate the value of MRI-based radiomic model based on the radiomic features of different basal nuclei in differentiating idiopathic Parkinson's disease (IPD) from Parkinsonian variants of multiple system atrophy (MSA-P).
methodsRadiomics was applied to the 3T susceptibility- weighted imaging (SWI) from 102 MSA-P patients and 83 IPD patients (allocated to a training and a testing cohort, 7:3 ratio). The substantia nigra (SN), caudate nucleus (CN), putamen (PUT), globus pallidus (GP), red nucleus (RN), and subthalamic nucleus (STN) were manually segmented, and 396 features were extracted. After feature selection, support vector machine (SVM) was generated, and its predictive performance was calculated in both the training and testing cohorts using the area under receiver operating characteristic curve (AUC).
resultsSeven radiomic features were selected from the PUT, by which the SVM classifier achieved the best diagnostic performance with an AUC of 0.867 in the training cohort and an AUC of 0.862 in the testing cohort. Furthermore, the combined model, which incorporating part III of the Parkinson's Disease Rating Scale (UPDRSIII) scores into radiomic features of the PUT, further improved the diagnostic performance. However, radiomic features extracted from RN, SN, GP, CN, and STN had moderate to poor diagnostic performance, with AUC values that ranged from 0.610 to 0.788 in the training cohort and 0.583 to 0.766 in the testing cohort.
conclusionRadiomic features derived from the PUT had optimal value in differentiating IPD from MSA-P. A combined radiomic model, which contained radiomic features of the PUT and UPDRSIII scores, further improved performance and may represent a promising tool for distinguishing between IPD and MSA-P.
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