PDF(Pubmed)
Abstract：
UNASSIGNED: 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.
UNASSIGNED: 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).
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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).
UNASSIGNED: 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.
UNASSIGNED: 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.
摘要：
■这项研究的目的是探索机器学习是否可以用于通过使用从小脑灰质和白质中提取的纹理特征来建立诊断帕金森病（PD）的有效模型，从而识别肉眼无法观察到的细微变化。
■这项研究涉及2010年6月至2023年3月的数据收集期，其中包括来自两个队列的374名受试者。帕金森进展标志物倡议(PPMI)作为训练集，来自24个全球站点的对照组和PD患者（HC：102和PD：102）。我们机构的数据被用作测试集(HC:91和PD:79)。利用机器学习建立基于小脑灰质和白质纹理特征的多模型进行PD诊断。结果通过5倍交叉验证分析进行了评估，计算每个模型的受试者工作特征曲线下面积(AUC)。使用Delong测试比较了每个模型的性能，通过采用Shapley加法解释（SHAP）进一步增强了优化模型的可解释性。
■使用FeAtureExplorer(FAE)软件比较验证数据集中所有管道的AUC。在Kruskal-Wallis(KW)和Lasso(LRLasso)逻辑回归建立的模型中，使用“一标准误差”规则,AUC最高.\'WM_original_glrlm_GrayLevelNonUniformity\'被认为是最稳定和预测功能。
■小脑灰质和白质的纹理特征结合机器学习可能对帕金森病的诊断具有潜在价值，其中白质的异质性可能是更有价值的成像标记。
■这项研究涉及2010年6月至2023年3月的数据收集期，其中包括来自两个队列的374名受试者。帕金森进展标志物倡议(PPMI)作为训练集，来自24个全球站点的对照组和PD患者（HC：102和PD：102）。我们机构的数据被用作测试集(HC:91和PD:79)。利用机器学习建立基于小脑灰质和白质纹理特征的多模型进行PD诊断。结果通过5倍交叉验证分析进行了评估，计算每个模型的受试者工作特征曲线下面积(AUC)。使用Delong测试比较了每个模型的性能，通过采用Shapley加法解释（SHAP）进一步增强了优化模型的可解释性。
■使用FeAtureExplorer(FAE)软件比较验证数据集中所有管道的AUC。在Kruskal-Wallis(KW)和Lasso(LRLasso)逻辑回归建立的模型中，使用“一标准误差”规则,AUC最高.\'WM_original_glrlm_GrayLevelNonUniformity\'被认为是最稳定和预测功能。
■小脑灰质和白质的纹理特征结合机器学习可能对帕金森病的诊断具有潜在价值，其中白质的异质性可能是更有价值的成像标记。
