Journal
FRONTIERS IN NEUROSCIENCE
Volume 15, Issue -, Pages -Publisher
FRONTIERS MEDIA SA
DOI: 10.3389/fnins.2021.646617
Keywords
Parkinson's disease; magnetic resonance imaging; machine learning; substantia nigra; neuropsychological tests
Categories
Funding
- Qingdao Key Health Discipline Development Fund
- National Key Research and Development Program of China [2016YFC0105901SDZ]
- Qingdao Science and Technology Demonstration and Guidance Project [20-3-4-37-nsh]
- Flexible Talent Project of Qilu Hospital of Shandong University (Qingdao) [QDKY2019RX05, QDKY2019RX13]
Ask authors/readers for more resources
Radiomic features based on magnitude images can reflect the Hoehn-Yahr stage of PD to some extent. The LASSO logistic regression model showed good prediction efficacy in both the training and validation sets.
Background It is reported that radiomic features extracted from quantitative susceptibility mapping (QSM) had promising clinical value for the diagnosis of Parkinson's disease (PD). We aimed to explore the usefulness of radiomics features based on magnitude images to distinguish PD from non-PD controls. Methods We retrospectively recruited PD patients and controls who underwent brain 3.0T MR including susceptibility-weighted imaging (SWI). A total of 396 radiomics features were extracted from the SN of 95 PD patients and 95 non-PD controls based on SWI. Intra-/inter-observer correlation coefficients (ICCs) were applied to measure the observer agreement for the radiomic feature extraction. Then the patients were randomly grouped into training and validation sets in a ratio of 7:3. In the training set, the maximum correlation minimum redundancy algorithm (mRMR) and the least absolute shrinkage and selection operator (LASSO) were conducted to filter and choose the optimized subset of features, and a radiomics signature was constructed. Moreover, radiomics signatures were constructed by different machine learning models. Area under the ROC curves (AUCs) were applied to evaluate the predictive performance of the models. Then correlation analysis was performed to evaluate the correlation between the optimized features and clinical factors. Results The intro-observer CC ranged from 0.82 to 1.0, and the inter-observer CC ranged from 0.77 to 0.99. The LASSO logistic regression model showed good prediction efficacy in the training set [AUC = 0.82, 95% confidence interval (CI, 0.74-0.88)] and the validation set [AUC = 0.81, 95% CI (0.68-0.91)]. One radiomic feature showed a moderate negative correlation with Hoehn-Yahr stage (r = -0.49, P = 0.012). Conclusion Radiomic predictive features based on SWI magnitude images could reflect the Hoehn-Yahr stage of PD to some extent.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available