Multi-modality radiomics of conventional T1 weighted and diffusion tensor imaging for differentiating Parkinson’s disease motor subtypes in early-stages
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| Publicado en: | Scientific Reports (Nature Publisher Group) vol. 14, no. 1 (2024), p. 20708 |
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| Acceso en línea: | Citation/Abstract Full Text - PDF |
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| 022 | |a 2045-2322 | ||
| 024 | 7 | |a 10.1038/s41598-024-71860-y |2 doi | |
| 035 | |a 3101008231 | ||
| 045 | 2 | |b d20240101 |b d20241231 | |
| 084 | |a 274855 |2 nlm | ||
| 100 | 1 | |a Panahi, Mehdi |u Payame Noor University Erbil Branch, Department of Computer Engineering, Erbil, Iraq | |
| 245 | 1 | |a Multi-modality radiomics of conventional T1 weighted and diffusion tensor imaging for differentiating Parkinson’s disease motor subtypes in early-stages | |
| 260 | |b Nature Publishing Group |c 2024 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a This study aimed to develop and validate a multi-modality radiomics approach using T1-weighted and diffusion tensor imaging (DTI) to differentiate Parkinson's disease (PD) motor subtypes, specifically tremor-dominant (TD) and postural instability gait difficulty (PIGD), in early disease stages. We analyzed T1-weighted and DTI scans from 140 early-stage PD patients (70 TD, 70 PIGD) and 70 healthy controls from the Parkinson's Progression Markers Initiative database. Radiomics features were extracted from 16 brain regions of interest. After harmonization and feature selection, four machine learning classifiers were trained and evaluated for both three-class (HC vs TD vs PIGD) and binary (TD vs PIGD) classification tasks. The light gradient boosting machine (LGBM) classifier demonstrated the best overall performance. For the three-class classification, LGBM achieved an accuracy of 85% and an area under the receiver operating characteristic curve (AUC) of 0.94 using combined T1 and DTI features. In the binary classification task, LGBM reached an accuracy of 95% and AUC of 0.95. Key discriminative features were identified in the Thalamus, Amygdala, Hippocampus, and Substantia Nigra for the three-group classification, and in the Pallidum, Amygdala, Hippocampus, and Accumbens for binary classification. The combined T1 + DTI approach consistently outperformed single-modality classifications, with DTI alone showing particularly low performance (AUC 0.55–0.62) in binary classification. The high accuracy and AUC values suggest that this approach could significantly improve early diagnosis and subtyping of PD. These findings have important implications for clinical management, potentially enabling more personalized treatment strategies based on early, accurate subtype identification. | |
| 653 | |a Radiomics | ||
| 653 | |a Hippocampus | ||
| 653 | |a Amygdala | ||
| 653 | |a Accuracy | ||
| 653 | |a Parkinson's disease | ||
| 653 | |a Magnetic resonance imaging | ||
| 653 | |a Movement disorders | ||
| 653 | |a Classification | ||
| 653 | |a Tremor | ||
| 653 | |a Globus pallidus | ||
| 653 | |a Substantia nigra | ||
| 653 | |a Neurodegenerative diseases | ||
| 653 | |a Neuroimaging | ||
| 653 | |a Machine learning | ||
| 653 | |a Social | ||
| 700 | 1 | |a Hosseini, Mahboube Sadat |u Shahid Beheshti University, Department of Medical Radiation Engineering, Tehran, Iran (GRID:grid.412502.0) (ISNI:0000 0001 0686 4748) | |
| 773 | 0 | |t Scientific Reports (Nature Publisher Group) |g vol. 14, no. 1 (2024), p. 20708 | |
| 786 | 0 | |d ProQuest |t Science Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3101008231/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3101008231/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |