Prediction and Classification of Alzheimer's Disease Based on Combined Features From Apolipoprotein-E Genotype, Cerebrospinal Fluid, MR, and FDG-PET Imaging Biomarkers
Prediction and Classification of Alzheimer's Disease Based on Combined Features From Apolipoprotein-E Genotype, Cerebrospinal Fluid, MR, and FDG-PET Imaging Biomarkers
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DOI：
10.3389/fncom.2019.00072
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发表时间：
2019-10-16
影响因子：
3.2
通讯作者：
Kwon, Goo-Rak
中科院分区：
文献类型：
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作者：
Gupta, Yubraj;Lama, Ramesh Kumar;Kwon, Goo-Rak
阿尔茨海默病（AD），包括其轻度认知障碍（MCI）阶段，其可能或可能不进展成AD，是痴呆的最常见形式。在MCI阶段正确识别患者非常重要，因为这是AD可能发展或可能不发展的阶段。因此，在这个阶段预测结果至关重要。到目前为止，许多研究人员仅致力于使用单一模式的生物标志物来诊断AD或MCI。尽管最近的研究表明，一种或多种不同生物标志物的组合可以为诊断提供补充信息，但它也增加了区分不同群体的分类准确性。在本文中，我们提出了一种新的基于机器学习的框架，利用四种不同生物标志物的组合来区分AD或MCI受试者：氟脱氧葡萄糖正电子发射断层扫描（FDG-PET），结构磁共振成像（sMRI），脑脊液（CSF）蛋白水平和载脂蛋白E（APOE）基因型。本研究使用阿尔茨海默病神经影像学倡议（ADNI）基线数据集。总共有158例受试者的所有四种生物标志物模式均可用。在158例受试者中，38例受试者属于AD组，82例受试者属于MCI组（包括46例MCIc [MCI转化;在24个月时间段内转化为AD]，36例MCI [MCI稳定;在24个月时间段内未转化为AD]），其余38例受试者属于健康对照（HC）组。对于每张图像，我们使用Brainnetome模板图像和NiftyReg工具箱提取了246个感兴趣区域（作为特征），然后我们使用早期融合技术将这些特征与从ADNI网站获得的每个受试者的三个CSF和两个APOE基因型特征相结合。在这里，一个不同的基于核的多类支持向量机（SVM）分类器与网格搜索方法。在将获得的特征传递给分类器之前，我们使用截断奇异值分解（截断SVD）降维技术将高维特征降维为低维特征。因此，我们的组合方法实现了AD与HC、MCIs与MCIc、AD与MCIs、AD与MCIc、HC与MCIc的受试者工作特征（AU-ROC）曲线下面积分别为98.33、93.59、96.83、94.64、96.43和95.24%，以及HC与MCI受试者，其相对于单一模态结果和其他最先进的方法较高。此外，组合多模态方法提高了单峰分类的分类性能。
Alzheimer's disease (AD), including its mild cognitive impairment (MCI) phase that may or may not progress into the AD, is the most ordinary form of dementia. It is extremely important to correctly identify patients during the MCI stage because this is the phase where AD may or may not develop. Thus, it is crucial to predict outcomes during this phase. Thus far, many researchers have worked on only using a single modality of a biomarker for the diagnosis of AD or MCI. Although recent studies show that a combination of one or more different biomarkers may provide complementary information for the diagnosis, it also increases the classification accuracy distinguishing between different groups. In this paper, we propose a novel machine learning-based framework to discriminate subjects with AD or MCI utilizing a combination of four different biomarkers: fluorodeoxyglucose positron emission tomography (FDG-PET), structural magnetic resonance imaging (sMRI), cerebrospinal fluid (CSF) protein levels, and Apolipoprotein-E (APOE) genotype. The Alzheimer's Disease Neuroimaging Initiative (ADNI) baseline dataset was used in this study. In total, there were 158 subjects for whom all four modalities of biomarker were available. Of the 158 subjects, 38 subjects were in the AD group, 82 subjects were in MCI groups (including 46 in MCIc [MCI converted; conversion to AD within 24 months of time period], and 36 in MCIs [MCI stable; no conversion to AD within 24 months of time period]), and the remaining 38 subjects were in the healthy control (HC) group. For each image, we extracted 246 regions of interest (as features) using the Brainnetome template image and NiftyReg toolbox, and later we combined these features with three CSF and two APOE genotype features obtained from the ADNI website for each subject using early fusion technique. Here, a different kernel-based multiclass support vector machine (SVM) classifier with a grid-search method was applied. Before passing the obtained features to the classifier, we have used truncated singular value decomposition (Truncated SVD) dimensionality reduction technique to reduce high dimensional features into a lower-dimensional feature. As a result, our combined method achieved an area under the receiver operating characteristic (AU-ROC) curve of 98.33, 93.59, 96.83, 94.64, 96.43, and 95.24% for AD vs. HC, MCIs vs. MCIc, AD vs. MCIs, AD vs. MCIc, HC vs. MCIc, and HC vs. MCIs subjects which are high relative to single modality results and other state-of-the-art approaches. Moreover, combined multimodal methods have improved the classification performance over the unimodal classification.