ArticleAnnals of translational medicine2019
Use of radiomic features and support vector machine to distinguish Parkinson's disease cases from normal controls.
Article in Annals of translational medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers, 4 of them syntheses that pooled it.
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Who cites it
46 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- 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
- Machine learning methods for the detection and prediction of cognitive impairment in Parkinson's disease: a systematic review and meta-analysis.Frontiers in aging neuroscience · 2025Pooled 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
- Machine Learning for the Diagnosis of Parkinson's Disease: A Review of Literature.Frontiers in aging neuroscience · 2021Pooled it
- A review of FDG-PET biomarker analysis methods in Parkinson's disease.Journal of Parkinson's disease · 2026Review
- Machine Learning-Driven Radiomic Profiling of Thalamus-Amygdala Nuclei for Prediction of Postoperative Delirium After STN-DBS in Parkinson's Disease Patients: A Pilot Study.Journal of imaging informatics in medicine · 2026Article
- A study on 18F-FDG PET/CT imaging assessment of the anti-aging efficacy of BMMSCs in the macaque brain.Stem cells translational medicine · 2026Article
- Novel applications of machine learning and computational neuroscience models to neuroimaging in Parkinson's disease and related disorders.Frontiers in aging neuroscience · 2026Review
- A radiomics model based on diffusion-weighted imaging developed using machine learning enables prediction of microsatellite instability in endometrial cancer.BMC medical imaging · 2025Article
- 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
- AI-Driven Advances in Parkinson's Disease Neurosurgery: Enhancing Patient Selection, Trial Efficiency, and Therapeutic Outcomes.Brain sciences · 2025Review
- Harnessing routine MRI for the early screening of Parkinson's disease: a multicenter machine learning study using T2-weighted FLAIR imaging.Insights into imaging · 2025Article
- Sex-Specific Imaging Biomarkers for Parkinson's Disease Diagnosis: A Machine Learning Analysis.Journal of imaging informatics in medicine · 2025Article
- A novel MRI-based deep learning-radiomics framework for evaluating cerebrospinal fluid signal in central nervous system infection.Frontiers in medicine · 2025Article
- Individualized diagnosis of Parkinson's disease based on multivariate magnetic resonance imaging radiomics and clinical indexes.Frontiers in aging neuroscience · 2025Article
- Review
- PET radiomics in lung cancer: advances and translational challenges.EJNMMI physics · 2024Review
- Analysis of cognitive dysfunction in Parkinson's disease using voxel based morphometry and radiomics.Cognitive processing · 2024Article
- White matter biomarker for predicting de novo Parkinson's disease using tract-based spatial statistics: a machine learning-based model.Quantitative imaging in medicine and surgery · 2024Article
- Gait classification for early detection and severity rating of Parkinson's disease based on hybrid signal processing and machine learning methods.Cognitive neurodynamics · 2024Article
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundParkinson's disease (PD) is an irreversible neurodegenerative disease. The diagnosis of PD based on neuroimaging is usually with low-level or deep learning features, which results in difficulties in achieving precision classification or interpreting the clinical significance. Herein, we aimed to extract high-order features by using radiomics approach and achieve acceptable diagnosis accuracy in PD.
methodsIn this retrospective multicohort study, we collected
resultsTwenty-six brain ROIs were identified. Six thousand one hundred and ten radiomic features were extracted in total. Among them 30 features were remained after feature selection. The accuracies of the proposed method achieved 90.97%±4.66% and 88.08%±5.27% in Huashan and Wuxi test sets, respectively.
conclusionsThis study showed that radiomic features and SVM could be used to distinguish between PD and NC based on 18F-FDG PET images.
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