A more objective PD diagnostic model: integrating texture feature markers of cerebellar gray matter and white matter through machine learning.
| Autor: | Yini Chen, Yiwei Qi, Tianbai Li, Andong Lin, Yang Ni, Renwang Pu, Bo Sun |
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| Předmět: |
CEREBELLUM anatomy
PARKINSON'S disease diagnosis STATISTICAL models RECEIVER operating characteristic curves RESEARCH funding LOGISTIC regression analysis MAGNETIC resonance imaging GRAY matter (Nerve tissue) LONGITUDINAL method SUPPORT vector machines WHITE matter (Nerve tissue) COMPUTER-aided diagnosis ANALYSIS of variance MACHINE learning COMPARATIVE studies DATA analysis software SENSITIVITY & specificity (Statistics) DISCRIMINANT analysis ALGORITHMS |
| Zdroj: | Frontiers in Aging Neuroscience; 2024, p1-13, 13p |
| Abstrakt: | Objective: The purpose of this study is to explore whether machine learning can be used to establish an effective model for the diagnosis of Parkinson's disease (PD) by using texture features extracted from cerebellar gray matter and white matter, so as to identify subtle changes that cannot be observed by the naked eye. Method: This study involved a data collection period from June 2010 to March 2023, including 374 subjects from two cohorts. The Parkinson's Progression Markers Initiative (PPMI) served as the training set, with control group and PD patients (HC: 102 and PD: 102) from 24 global sites. Our institution's data was utilized as the test set (HC: 91 and PD: 79). Machine learning was employed to establish multiple models for PD diagnosis based on texture features of the cerebellum's gray and white matter. Results underwent evaluation through 5-fold cross-validation analysis, calculating the area under the receiver operating characteristic curve (AUC) for each model. The performance of each model was compared using the Delong test, and the interpretability of the optimized model was further augmented by employing Shapley additive explanations (SHAP). Results: The AUCs for all pipelines in the validation dataset were compared using FeAture Explorer (FAE) software. Among the models established by Kruskal-Wallis (KW) and logistic regression via Lasso (LRLasso), the AUC was highest using the "one-standard error" rule. 'WM_original_glrlm_GrayLevelNonUniformity' was considered the most stable and predictive feature. Conclusion: The texture features of cerebellar gray matter and white matter combined with machine learning may have potential value in the diagnosis of Parkinson's disease, in which the heterogeneity of white matter may be a more valuable imaging marker. [ABSTRACT FROM AUTHOR] |
| Databáze: | Complementary Index |
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