FDG-PET improves accuracy in distinguishing frontotemporal dementia and Alzheimer's disease

University of California, Davis, Davis, California, United States
Brain (Impact Factor: 9.2). 11/2007; 130(Pt 10):2616-35. DOI: 10.1093/brain/awm177
Source: PubMed


Distinguishing Alzheimer's disease (AD) and frontotemporal dementia (FTD) currently relies on a clinical history and examination, but positron emission tomography with [(18)F] fluorodeoxyglucose (FDG-PET) shows different patterns of hypometabolism in these disorders that might aid differential diagnosis. Six dementia experts with variable FDG-PET experience made independent, forced choice, diagnostic decisions in 45 patients with pathologically confirmed AD (n = 31) or FTD (n = 14) using five separate methods: (1) review of clinical summaries, (2) a diagnostic checklist alone, (3) summary and checklist, (4) transaxial FDG-PET scans and (5) FDG-PET stereotactic surface projection (SSP) metabolic and statistical maps. In addition, we evaluated the effect of the sequential review of a clinical summary followed by SSP. Visual interpretation of SSP images was superior to clinical assessment and had the best inter-rater reliability (mean kappa = 0.78) and diagnostic accuracy (89.6%). It also had the highest specificity (97.6%) and sensitivity (86%), and positive likelihood ratio for FTD (36.5). The addition of FDG-PET to clinical summaries increased diagnostic accuracy and confidence for both AD and FTD. It was particularly helpful when raters were uncertain in their clinical diagnosis. Visual interpretation of FDG-PET after brief training is more reliable and accurate in distinguishing FTD from AD than clinical methods alone. FDG-PET adds important information that appropriately increases diagnostic confidence, even among experienced dementia specialists.

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    • "Both PET, using 18-F-FDG, and SPECT, using 99mTc- hexamethylpropyleneamine, have been used to show hypometabolism in the frontal and temporal lobes in FTD and correlated to patterns of brain atrophy (Kipps, Hodges, Fryer, & Nestor, 2009). PET may improve the diagnostic accuracy between Alzheimer's disease and FTD (Foster et al., 2007). New PET imaging compounds include Pittsburgh compound B and florbetapir that are radioactively labeled compounds that bind to amyloid plaques and can be used to differentiate FTD and AD. "

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    • "The metrics chosen to evaluate hypometabolism may carry variability in accuracy as high as the difference in accuracy between different biomarkers (Frisoni et al., 2013). The commonest way is the visual reading that is the cornerstone of any report but it may not be accurate enough (Foster et al., 2007; Patterson et al., 2010) particularly at the early stages of the disease (i.e. MCI) or when expert readers are not available on site. "
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    ABSTRACT: An emerging issue in neuroimaging is to assess the diagnostic reliability of PET and its application in clinical practice. We aimed at assessing the accuracy of brain FDG-PET in discriminating patients with MCI due to Alzheimer's disease and healthy controls. Sixty-two patients with amnestic MCI and 109 healthy subjects recruited in five centers of the European AD Consortium were enrolled. Group analysis was performed by SPM8 to confirm metabolic differences. Discriminant analyses were then carried out using the mean FDG uptake values normalized to the cerebellum computed in 45 anatomical volumes of interest (VOIs) in each hemisphere (90 VOIs) as defined in the Automated Anatomical Labeling (AAL) Atlas and on 12 meta-VOIs, bilaterally, obtained merging VOIs with similar anatomo-functional characteristics. Further, asymmetry indexes were calculated for both datasets. Accuracy of discrimination by a Support Vector Machine and the AAL VOIs was tested against a validated method (PALZ). At the voxel level SMP8 showed a relative hypometabolism in the bilateral precuneus, and posterior cingulate, temporo-parietal and frontal cortices. Discriminant analysis classified subjects with an accuracy ranging between .91 and .83 as a function of data organization. The best values were obtained from a subset of 6 meta-VOIs plus 6 asymmetry values reaching an area under the ROC curve of .947, significantly larger than the one obtained by the PALZ score. High accuracy in discriminating MCI converters from healthy controls was reached by a non-linear classifier based on SVM applied on predefined anatomo-functional regions and inter-hemispheric asymmetries. Data pre-processing was automated and simplified by an in-house created Matlab-based script encouraging its routine clinical use. Further validation toward nonconverter MCI patients with adequately long follow-up is needed.
    Full-text · Article · Nov 2014 · Clinical neuroimaging
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    • "Recently, a number of machine learning and pattern classification methods have been widely used in neuroimaging analysis of AD and MCI, including both group comparison (i.e., between clinically different groups) and individual classification [Jie et al., 2014b; Orru et al., 2012; Ye et al., 2011]. Early studies mainly focus on extracting features [e.g., based on regions of interest (ROIs) or voxels] from single imaging modality such as structural magnetic resonance imaging (MRI) [Chincarini et al., 2011; Fan et al., 2008a,b; Liu et al., 2012; Oliveira et al., 2010; Westman et al., 2011] and fluorodeoxyglucose positron emission tomography (FDG-PET) [Drzezga et al., 2003; Foster et al., 2007; Higdon et al., 2004; Hinrichs et al., 2009], and so forth. More recently, researchers have begun to integrate multiple imaging modalities to further improve the accuracy of disease diagnosis [Hinrichs et al., 2011; Zhang et al., 2011; Zhou et al., 2013]. "
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    ABSTRACT: Multimodality based methods have shown great advantages in classification of Alzheimer's disease (AD) and its prodromal stage, that is, mild cognitive impairment (MCI). Recently, multitask feature selection methods are typically used for joint selection of common features across multiple modalities. However, one disadvantage of existing multimodality based methods is that they ignore the useful data distribution information in each modality, which is essential for subsequent classification. Accordingly, in this paper we propose a manifold regularized multitask feature learning method to preserve both the intrinsic relatedness among multiple modalities of data and the data distribution information in each modality. Specifically, we denote the feature learning on each modality as a single task, and use group-sparsity regularizer to capture the intrinsic relatedness among multiple tasks (i.e., modalities) and jointly select the common features from multiple tasks. Furthermore, we introduce a new manifold-based Laplacian regularizer to preserve the data distribution information from each task. Finally, we use the multikernel support vector machine method to fuse multimodality data for eventual classification. Conversely, we also extend our method to the semisupervised setting, where only partial data are labeled. We evaluate our method using the baseline magnetic resonance imaging (MRI), fluorodeoxyglucose positron emission tomography (FDG-PET), and cerebrospinal fluid (CSF) data of subjects from AD neuroimaging initiative database. The experimental results demonstrate that our proposed method can not only achieve improved classification performance, but also help to discover the disease-related brain regions useful for disease diagnosis. Hum Brain Mapp, 2014. © 2014 Wiley Periodicals, Inc.
    Full-text · Article · Oct 2014 · Human Brain Mapping
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