Unlocking the potential: T1-weighed MRI as a powerful predictor of levodopa response in Parkinson’s disease
- Open Access
- 01-12-2024
- Levodopa
- Original Article
Published in:
- Authors
- Junyi Yan
- Xufang Luo
- Jiahang Xu
- Dongsheng Li
- Lili Qiu
- Dianyou Li
- Peng Cao
- Chencheng Zhang
- Published in
- Insights into Imaging | Issue 1/2024
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 < 0.001.
Results
For 46 test set records (mean age, 62 ± 9 years, 28 men), MedicalNet-extracted features demonstrated a consistent improvement in all three machine learning models (SVM 0.83 ± 0.01 versus 0.73 ± 0.01, XgBoost 0.80 ± 0.04 versus 0.74 ± 0.02, MLP 0.80 ± 0.03 versus 0.70 ± 0.07, p < 0.001). Both feature sets were validated on the clinical dataset using SVM, where MedicalNet features alone achieved an AUC of 0.64 ± 0.03. Key responsible brain regions were visualized.
Conclusion
The T1-weighed MRI features were more robust and generalizable than the clinical features in prediction; their combination provided the best results. T1-weighed MRI provided insights on specific regions responsible for levodopa response prediction.
Critical relevance statement
This study demonstrated that T1w MRI features extracted by a deep learning model have the potential to predict the levodopa response of PD patients and are more robust than widely used clinical information, which might help in determining treatment strategy.
Key Points
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This study investigated the predictive value of T1w features for levodopa response.
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MedicalNet extractor outperformed all other previously published methods with key region visualization.
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T1w features are more effective than clinical information in levodopa response prediction.
Graphical Abstract
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- Title
- Unlocking the potential: T1-weighed MRI as a powerful predictor of levodopa response in Parkinson’s disease
- Authors
-
Junyi Yan
Xufang Luo
Jiahang Xu
Dongsheng Li
Lili Qiu
Dianyou Li
Peng Cao
Chencheng Zhang
- Publication date
- 01-12-2024
- Publisher
- Springer Vienna
- Keywords
-
Levodopa
Idiopathic Parkinson Disease - Published in
-
Insights into Imaging / Issue 1/2024
Electronic ISSN: 1869-4101 - DOI
- https://doi.org/10.1186/s13244-024-01690-z
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