| Autor: |
Junyi Yan, Xufang Luo, Jiahang Xu, Dongsheng Li, Lili Qiu, Dianyou Li, Peng Cao, Chencheng Zhang |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
Insights into Imaging, Vol 15, Iss 1, Pp 1-12 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
1869-4101 |
| DOI: |
10.1186/s13244-024-01690-z |
| Popis: |
Abstract Background The efficacy of levodopa, the most crucial metric for Parkinson’s disease diagnosis and treatment, is traditionally gauged through the levodopa challenge test, which lacks a predictive model. This study aims to probe the predictive power of T1-weighted MRI, the most accessible modality for levodopa response. Methods This retrospective study used two datasets: from the Parkinson’s Progression Markers Initiative (219 records) and the external clinical dataset from Ruijin Hospital (217 records). A novel feature extraction method using MedicalNet, a pre-trained deep learning network, along with three previous approaches was applied. Three machine learning models were trained and tested on the PPMI dataset and included clinical features, imaging features, and their union set, using the area under the curve (AUC) as the metric. The most significant brain regions were visualized. The external clinical dataset was further evaluated using trained models. A paired one-tailed t-test was performed between the two sets; statistical significance was set at p |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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