A comparison of classification methods for differentiating fronto-temporal dementia from Alzheimer's disease using FDG-PET imaging
A comparison of classification methods for differentiating fronto-temporal dementia from Alzheimer's disease using FDG-PET imaging
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DOI：
10.1002/sim.1719
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发表时间：
2004-01-30
影响因子：
2
通讯作者：
Minoshima, S
中科院分区：
文献类型：
--
作者：
Higdon, R;Foster, NL;Minoshima, S
氟脱氧葡萄糖正电子发射断层扫描（FDG-PET）正在探索，以确定其区分阿尔茨海默病（AD）和额颞叶痴呆（FTD）诊断的能力。我们已经研究了统计歧视程序，以帮助实现这一目的，并比较结果的FDG-PET图像的视觉评级。将该方法应用于48例尸检确诊AD或FTD的受试者的数据集（这些受试者来自国家阿尔茨海默病协调中心资助的多中心合作研究）。FDG-PET图像由成千上万的体素（体积元素）组成，因此存在比受试者多得多的变量的情况。因此，有必要在应用统计程序之前进行数据简化。方法使用整个图像和汇总统计计算的数量的卷的兴趣（VOI）进行检查。我们对整个图像进行了主成分分析（PCA）和偏最小二乘（PLS）的数据简化技术，然后使用线性判别分析（LDA），二次（QDA）或逻辑回归（LR）将受试者分类为AD或FTD。这些方法中的一些实现了诊断准确性（如通过留一交叉验证评估的），这类似于专家评级员的视觉评级。使用PLS的方法似乎更成功。平均或使用VOI数据也可能有帮助。版权所有（C）2004约翰威利父子有限公司。
Flurodeoxyglucose positron emission tomography (FDG-PET) is being explored to determine its ability to differentiate between a diagnosis of Alzheimer's disease (AD) and fronto-temporal dementia (FTD). We have examined statistical discrimination procedures to help achieve this purpose and compared the results to visual ratings of FDG-PET images. The methods are applied to a data set of 48 subjects with autopsy confirmed diagnoses of AD or FTD (these subjects come from a multi-centre collaborative study funded by the National Alzheimer's Coordinating Center). FDG-PET images are composed of thousands of voxels (volume elements) so one is left with a situation where there are vastly more variables than subjects. Therefore, it is necessary to perform a data reduction before a statistical procedure can be applied. Approaches using both the entire image and summary statistics calculated on a number of volumes of interest (VOI) are examined. We performed the data reduction techniques of principal components analysis (PCA) and partial least-squares (PLS) on the entire image and then used linear discriminant analysis (LDA), quadratic (QDA) or logistic regression (LR) to classify subjects as having AD or FTD. Some of these methods achieve diagnostic accuracy (as assessed by leave-one-out cross-validation) that is similar to visual ratings by expert raters. Methods using PLS appear to be more successful. Averaging or using VOI data may also be helpful. Copyright (C) 2004 John Wiley Sons, Ltd.