用7特斯拉MRI和深度学习区分帕金森病不同运动亚型，提升诊断准确率。
7 Tesla Quantitative MRI and Machine Learning for Exploratory Motor Subtype Stratification and Diagnosis in Parkinson's Disease
- 结合深度学习分割与定量MRI特征选择，提取低维影像标志物。
- 在区分震颤型与姿势不稳型帕金森病时准确率达100%。
- 结果支持用可解释的影像特征辅助精准分型，适合神经影像研究者。
帕金森病（PD）具有高度异质性，不同患者以不同运动症状为主。能支持亚型分层的影像生物标志物有助于加深生物学理解、优化研究设计，并实现个性化治疗。本研究评估了基于深度学习的自动脑区分割，结合7特斯拉MRI的定量图谱，能否有效区分健康对照组（HC）、姿势不稳与步态困难型（PIGD）及震颤主导型（TD），并用于客观的帕金森病分型。机器学习分类器性能可通过特征选择提升。共纳入21名健康对照者和24名帕金森病患者。使用DSC评价U-Net分割效果，平均达0.86。采用五折交叉验证比较两种分类方法：方法A使用全部提取特征；方法B选取最优特征子集。方法A在任务1（HC vs PwP）中准确率为0.69，AUC为0.73；任务2（PIGD vs TD）准确率0.69，AUC 0.90；任务3（三类分类）准确率0.62，AUC 0.66。方法B在任务1中准确率0.82，AUC 0.93；任务2准确率1.00，AUC 1.00；任务3准确率0.73，AUC 0.91。结果显示，深度学习分割结合定量MRI特征选择优于全特征使用，表明可解释的低维影像签名在帕金森病诊断与表型分型中具有潜力。需更大规模多中心研究验证其泛化性和稳定性。
原文摘要 · Abstract (English)
Parkinson's disease (PD) is a highly heterogeneous disease, including which motor symptoms are dominating. Imaging biomarkers that support subtype stratification could also improve biological understanding and study design, and enable personalized treatment strategies. This study evaluates whether deep-learning based automatic brain segmentation, in addition to quantitative maps from 7 Tesla MRI, can highlight differences between Healthy Controls (HC), Postural Instability and Gait Difficulty (PIGD) and Tremor Dominant (TD), and subsequently be used for objective PD stratification. The performance of machine learning classifiers may be improved with feature selection. 21 HC, and 24 people with PD (PwP) were included. The U-Net training was assessed with DSC. Two classification approaches using 5-fold cross-validation were defined across three tasks: (1) HC vs PwP; (2) PIGD vs TD; (3) multiclass, HC vs PIGD vs TD. Approach A used all extracted features. Approach B found the optimal subset of features for the classification tasks. The U-Net achieved mean DSC of 0.86 for all ROIs during training. Approach A: Task 1 best accuracy of 0.69 and best AUC of 0.73. Task 2 accuracy 0.69, AUC 0.90. Task 3 accuracy 0.62, AUC 0.66. Approach B: Task 1 accuracy of 0.82 and AUC of 0.93. Task 2 accuracy 1.00, AUC 1.00. Task 3 accuracy 0.73, AUC 0.91. DL-based segmentation combined with qMRI feature selection improved classification relative to using all features, supporting the potential of interpretable, low-dimensional imaging signatures for PD diagnosis support and phenotype stratification. Larger, multi-site studies are warranted to assess generalizability and stability.
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