Parkinson's Disease Diagnosis Using Neostriatum Radiomic Features Based on T2-Weighted Magnetic Resonance Imaging
Parkinson's Disease Diagnosis Using Neostriatum Radiomic Features Based on T2-Weighted Magnetic Resonance Imaging
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DOI：
10.3389/fneur.2020.00248
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发表时间：
2020-04-08
影响因子：
3.4
通讯作者：
Zhang Xianglin
中科院分区：
文献类型：
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作者：
Liu Panshi;Wang Han;Zhang Xianglin
背景：帕金森病（PD）是一种神经退行性疾病，其中新纹状体，包括尾状核（CN）和壳核（PU），在病理生理学中具有重要作用。然而，传统的磁共振成像 (MRI) 缺乏足够的特异性来诊断 PD。因此，本研究的目的是探讨采用放射组学方法在新纹状体T2加权图像上区分PD患者与健康对照的可行性，为PD的临床诊断提供依据。方法：在同一台3.0T MRI扫描仪上获得69名PD患者和69名年龄和性别匹配的健康对照的T2加权图像。感兴趣区域 (ROI) 手动放置在切片上的 CN 和 PU 处，显示 CN 和 PU 各自的最大尺寸。我们从每个 ROI 中提取 274 个纹理特征，然后使用最小绝对收缩和选择算子回归来执行特征选择和放射组学特征构建，以识别由最佳特征组成的 CN 和 PU 放射组学特征。我们使用接受者操作特征曲线分析来评估训练组中两个放射组学特征的诊断性能，并估计测试组中的泛化性能。结果：PD患者和健康对照之间的人口统计学和临床特征没有显着差异。 CN 和 PU 放射组学特征分别使用 12 个和 7 个最佳特征构建。两种放射组学特征在区分帕金森病患者和健康对照方面的表现良好。在训练组和测试组中，CN 放射组学特征的 AUC 分别为 0.9410（95% 置信区间 [CI]：0.8986-0.9833）和 0.7732（95% CI：0.6292-0.9173），PU 放射组学特征的 AUC 为 0.8767（95% CI： 分别为 0.8066-0.9469) 和 0.7143 (95% CI: 0.5540-0.8746)。 Vertl_GlevNonU_R 作为最佳特征同时出现在 CN 和 PU 放射组学特征中。 t检验分析显示PD患者的CN和PU纹理值显着高于健康对照（P < 0.05）。结论：新纹状体放射组学特征对PD具有良好的诊断性能，有望作为PD临床诊断的基础。
Background: Parkinson's disease (PD) is a neurodegenerative disease in which the neostriatum, including the caudate nucleus (CN) and putamen (PU), has an important role in the pathophysiology. However, conventional magnetic resonance imaging (MRI) lacks sufficient specificity to diagnose PD. Therefore, the study's aim was to investigate the feasibility of using a radiomics approach to distinguish PD patients from healthy controls on T2-weighted images of the neostriatum and provide a basis for the clinical diagnosis of PD.Methods: T2-weighted images from 69 PD patients and 69 age- and sex-matched healthy controls were obtained on the same 3.0T MRI scanner. Regions of interest (ROIs) were manually placed at the CN and PU on the slices showing the largest respective sizes of the CN and PU. We extracted 274 texture features from each ROI and then used the least absolute shrinkage and selection operator regression to perform feature selection and radiomics signature building to identify the CN and PU radiomics signatures consisting of optimal features. We used a receiver operating characteristic curve analysis to assess the diagnostic performance of two radiomics signatures in a training group and estimate the generalization performance in the test group.Results: There were no significant differences in the demographic and clinical characteristics between the PD patients and healthy controls. The CN and PU radiomics signatures were built using 12 and 7 optimal features, respectively. The performance of the two radiomics signatures to distinguish PD patients from healthy controls was good. In the training and test groups, the AUCs of the CN radiomics signatures were 0.9410 (95% confidence interval [CI]: 0.8986-0.9833) and 0.7732 (95% CI: 0.6292-0.9173), respectively, and the AUCs of the PU radiomics signature were 0.8767 (95% CI: 0.8066-0.9469) and 0.7143 (95% CI: 0.5540-0.8746), respectively. Vertl_GlevNonU_R appeared simultaneously in both the CN and PU radiomics signatures as an optimal feature. A t-test analysis revealed significantly higher levels of texture values of the CN and PU in the PD patients than healthy controls (P < 0.05).Conclusion: Neostriatum radiomics signatures achieved good diagnostic performance for PD and potentially could serve as a basis for the clinical diagnosis of PD.