ArticleEJNMMI physics2024
Radiomics incorporating deep features for predicting Parkinson's disease in
Article in EJNMMI physics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Machine Learning Prediction of Hoehn and Yahr Scores at 5-Years Post-Movement disorders clinical practice · 2026Article
- Interpretable machine learning for Parkinson's disease diagnosis, staging, and biological mechanism exploration: a multicenter analysis.BioData mining · 2026Article
- 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
- Hippocampal texture asymmetry features onFrontiers in immunology · 2026Article
- Spatial associations between esophageal lesions and surrounding tissues in esophageal fistula: a CAM-guided radiomics study.Frontiers in oncology · 2026Article
- AI in SPECT Imaging: Opportunities and Challenges.Seminars in nuclear medicine · 2025Review
- Combined with multimodal medical imaging and artificial intelligence for early diagnosis of Parkinson's disease.Frontiers in neurology · 2025Article
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Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
purpose
methodsIn this study, 161 subjects from the Parkinson's Progressive Marker Initiative database underwent baseline 3T MRI and
resultsFor the diagnostic accuracy at year 0, DL (0.696) outperformed most models, except DF + IF in SPECT-V (0.704), significantly superior based on paired t-test. For year 4, accuracy of DF + RaF model in MRI-based method is the highest (0.835), significantly better than DF + IF, IF + RaF, RaF and IF models. And DL (0.820) surpassed models in both SPECT-based methods. The area under the ROC curve (AUC) highlighted DF + RaF model (0.854) in MRI-based method at year 0 and DF + RaF model (0.869) in SPECT-T method at year 4, outperforming DL models, respectively. And then, there was no significant differences between SPECT-based and MRI-based segmentation methods except for the imaging feature models.
conclusionThe combination of radiomic and deep features enhances the prediction accuracy of PD HYS compared to only radiomics or DL. This suggests the potential for further advancements in predictive model performance for PD HYS at year 0 and year 4 after first diagnosis using
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.