Alzheimer's disease diagnosis in individual subjects using structural MR images: Validation studies
Alzheimer's disease diagnosis in individual subjects using structural MR images: Validation studies
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DOI：
10.1016/j.neuroimage.2007.09.073
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发表时间：
2008-02-01
期刊：
影响因子：
5.7
通讯作者：
Jack, Clifford R., Jr.
中科院分区：
文献类型：
--
作者：
Vemuri, Prashanthi;Gunter, Jeffrey L.;Jack, Clifford R., Jr.
目的：开发和验证基于支持向量机的结构性磁共振(SMR)图像分类诊断阿尔茨海默病(AD)的工具。临床特征良好的受试者的SMR扫描库可用于诊断新来的受试者。方法：190名疑似AD患者与190名认知正常(CN)受试者年龄和性别匹配。实施了三种不同的分类模型：模型I使用SMR扫描获得的组织密度给出结构异常指数(STAND)评分；模型II和III使用组织密度以及协变量(人口学和载脂蛋白E基因)给出调整后标准(ASTAND)评分。训练使用的是公元140年和140年CN的数据。通过四重交叉验证(CV)对支持向量机进行参数优化和训练。结果：模型II和模型III aSTAND-Score的CV准确率分别为88.5%和89.3%，所建立的模型在独立测试数据集上的泛化效果良好。最能区分不同组的解剖模式与已知的神经纤维性AD病理分布一致。结论：本文提供了初步证据，应用基于支持向量机的单个SMR扫描相对于扫描库的分类可以在单个受试者中为AD诊断提供有用的信息。在分类算法中包括人口统计和遗传信息略微提高了诊断的准确性。(C)2007 Elsevier Inc.保留所有权利。
Objective: To develop and validate a tool for Alzheimer's disease (AD) diagnosis in individual subjects using support vector machine (SVM)-based classification of structural MR (sMR) images.Background. Libraries of sMR scans of clinically well characterized subjects can be harnessed for the purpose of diagnosing new incoming subjects.Methods: One hundred ninety patients with probable AD were age-and gender-matched with 190 cognitively normal (CN) subjects. Three different classification models were implemented: Model I uses tissue densities obtained from sMR scans to give STructural Abnormality iNDex (STAND)-score; and Models II and III use tissue densities as well as covariates (demographics and Apolipoprotein E genotype) to give adjusted-STAND (aSTAND)-score. Data from 140 AD and 140 CN were used for training. The SVM parameter optimization and training were done by four-fold cross validation (CV). The remaining independent sample of 50 AD and 50 CN was used to obtain a minimally biased estimate of the generalization error of the algorithm.Results: The CV accuracy of Model II and Model III aSTAND-scores was 88.5% and 89.3%, respectively, and the developed models generalized well on the independent test data sets. Anatomic patterns best differentiating the groups were consistent with the known distribution of neurofibrillary AD pathology.Conclusions: This paper presents preliminary evidence that application of SVM-based classification of an individual sMR scan relative to a library of scans can provide useful information in individual subjects for diagnosis of AD. Including demographic and genetic information in the classification algorithm slightly improves diagnostic accuracy. (c) 2007 Elsevier Inc. All rights reserved.