Machine learning trained with quantitative susceptibility mapping to detect mild cognitive impairment in Parkinson's disease
Machine learning trained with quantitative susceptibility mapping to detect mild cognitive impairment in Parkinson's disease
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DOI：
10.1016/j.parkreldis.2021.12.004
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发表时间：
2021-12-11
影响因子：
4.1
通讯作者：
Matsukawa, Noriyuki
中科院分区：
文献类型：
--
作者：
Shibata, Haruto;Uchida, Yuto;Matsukawa, Noriyuki
背景：认知功能下降是帕金森病（PD）的常见症状。识别PD伴轻度认知功能障碍（PD-MCI）对于早期启动治疗干预和预防认知功能下降至关重要。目的：我们的目标是开发一个基于多图谱标签融合方法的磁化率值训练的机器学习模型，将无痴呆的PD分类为PD-MCI和正常认知（PD-CN）。研究方法：这项多中心观察性队列研究回顾性审查了内部验证队列的61例PD-MCI和59例PD-CN病例，以及外部验证队列的22例PD-MCI和21例PD-CN病例。多图谱方法将定量磁化率绘图（QSM）图像分成20个感兴趣区域，并提取基于QSM的磁化率值。随机森林、极端梯度增强和光梯度增强被选为机器学习算法。结果：所有分类器在分类任务中表现出实质性的性能，特别是随机森林模型。该模型的准确性、敏感性、特异性和受试者工作特征曲线下面积分别为79.1%、77.3%、81.0%和0.78。尾状核的QSM值是重要的特征，与蒙特利尔认知评估评分呈负相关（右侧尾状核：r =-0.573，95%CI：-0.801至-0.298，p = 0.003;左侧尾状核：r =-0.659，95%CI：-0.894至-0.392，p = 0.001）。结论：使用QSM值训练的机器学习模型成功地将PD（无痴呆）分为PD-MCI和PD-CN组，这表明QSM值有可能作为早期评估PD患者认知功能下降的辅助生物标志物。
Background: Cognitive decline is commonly observed in Parkinson's disease (PD). Identifying PD with mild cognitive impairment (PD-MCI) is crucial for early initiation of therapeutic interventions and preventing cognitive decline. Objective: We aimed to develop a machine learning model trained with magnetic susceptibility values based on the multi-atlas label-fusion method to classify PD without dementia into PD-MCI and normal cognition (PD-CN). Methods: This multicenter observational cohort study retrospectively reviewed 61 PD-MCI and 59 PD-CN cases for the internal validation cohort and 22 PD-MCI and 21 PD-CN cases for the external validation cohort. The multi-atlas method parcellated the quantitative susceptibility mapping (QSM) images into 20 regions of interest and extracted QSM-based magnetic susceptibility values. Random forest, extreme gradient boosting, and light gradient boosting were selected as machine learning algorithms. Results: All classifiers demonstrated substantial performances in the classification task, particularly the random forest model. The accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve for this model were 79.1%, 77.3%, 81.0%, and 0.78, respectively. The QSM values in the caudate nucleus, which were important features, were inversely correlated with the Montreal Cognitive Assessment scores (right caudate nucleus: r = -0.573, 95% CI: -0.801 to -0.298, p = 0.003; left caudate nucleus: r = -0.659, 95% CI: -0.894 to -0.392, p < 0.001). Conclusions: Machine learning models trained with QSM values successfully classified PD without dementia into PD-MCI and PD-CN groups, suggesting the potential of QSM values as an auxiliary biomarker for early evaluation of cognitive decline in patients with PD.