ArticleBrain sciences2022
Diffusion Tensor Imaging Radiomics for Diagnosis of Parkinson's Disease.
Article in Brain sciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 2 of them syntheses that pooled it.
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Who cites it
10 citing papers in PubMed, 2 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
- Deep-learning saturation transfer magnetic resonance fingerprinting (ST-MRF) in patients with Parkinson's disease.NeuroImage · 2026Article
- Prediction of Motor Symptom Progression of Parkinson's Disease Through Multimodal Imaging-Based Machine Learning.Journal of imaging informatics in medicine · 2026Article
- Habitat-based MRI radiomics for enhanced parkinson's diagnosis.Scientific reports · 2026Article
- A DTI-Radiomics and Clinical Integration Model for Predicting MCI-to-AD Progression Using Corpus Callosum Features.Current Alzheimer research · 2026Article
- Predicting motor function improvement following deep brain stimulation of the subthalamic nucleus for Parkinson's disease based on STN-T2MRI radiomics.Journal of Parkinson's disease · 2025Article
- Progress of radiomics research on white matter hyperintensity lesions.Frontiers in neurology · 2025Review
- Radiomics score derived from T1-w/T2-w ratio image can predict motor symptom progression in Parkinson's disease.European radiology · 2024Article
- Neuroinflammation and Mitochondrial Dysfunction in Parkinson's Disease: Connecting Neuroimaging with Pathophysiology.Antioxidants (Basel, Switzerland) · 2023Review
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Authors and funding
6 authors.
Funding
Abstract
backgroundDiagnosis of Parkinson's Disease (PD) based on clinical symptoms and scale scores is mostly objective, and the accuracy of neuroimaging for PD diagnosis remains controversial. This study aims to introduce a radiomic tool to improve the sensitivity and specificity of diagnosis based on Diffusion Tensor Imaging (DTI) metrics.
methodsIn this machine learning-based retrospective study, we collected basic clinical information and DTI images from 54 healthy controls (HCs) and 56 PD patients. Among them, 60 subjects (30 PD patients and 30 HCs) were assigned to the training group, whereas the test cohort was 26 PD patients and 24 HCs. After the feature extraction and selection using newly developed image processing software Ray-plus, LASSO regression was used to finalize radiomic features.
resultsA total of 4600 radiomic features were extracted, of which 12 were finally selected. The values of the AUC (area under the subject operating curve) in the training group, the validation group, and overall were 0.911, 0.931, and 0.919, respectively.
conclusionThis study introduced a novel radiometric and computer algorithm based on DTI images, which can help increase the sensitivity and specificity of PD screening.
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