Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.
PLoS One. 2011 Mar 23;6(3):e18111. doi: 10.1371/journal.pone.0018111.
Various biomarkers have been reported in recent literature regarding imaging abnormalities in different types of dementia. These biomarkers have helped to significantly improve early detection and also differentiation of various dementia syndromes. In this study, we systematically applied whole-brain and region-of-interest (ROI) based support vector machine classification separately and on combined information from different imaging modalities to improve the detection and differentiation of different types of dementia.
Patients with clinically diagnosed Alzheimer's disease (AD: n = 21), with frontotemporal lobar degeneration (FTLD: n = 14) and control subjects (n = 13) underwent both [F18]fluorodeoxyglucose positron emission tomography (FDG-PET) scanning and magnetic resonance imaging (MRI), together with clinical and behavioral assessment. FDG-PET and MRI data were commonly processed to get a precise overlap of all regions in both modalities. Support vector machine classification was applied with varying parameters separately for both modalities and to combined information obtained from MR and FDG-PET images. ROIs were extracted from comprehensive systematic and quantitative meta-analyses investigating both disorders.
Using single-modality whole-brain and ROI information FDG-PET provided highest accuracy rates for both, detection and differentiation of AD and FTLD compared to structural information from MRI. The ROI-based multimodal classification, combining FDG-PET and MRI information, was highly superior to the unimodal approach and to the whole-brain pattern classification. With this method, accuracy rate of up to 92% for the differentiation of the three groups and an accuracy of 94% for the differentiation of AD and FTLD patients was obtained.
Accuracy rate obtained using combined information from both imaging modalities is the highest reported up to now for differentiation of both types of dementia. Our results indicate a substantial gain in accuracy using combined FDG-PET and MRI information and suggest the incorporation of such approaches to clinical diagnosis and to differential diagnostic procedures of neurodegenerative disorders.
最近的文献报道了各种生物标志物，这些标志物与不同类型痴呆的影像学异常有关。这些生物标志物有助于显著提高早期检测，也有助于区分各种痴呆综合征。在这项研究中，我们分别系统地应用了全脑和感兴趣区域（ROI）基于支持向量机分类，以及来自不同成像模式的综合信息，以提高不同类型痴呆的检测和区分能力。
临床诊断为阿尔茨海默病（AD：n=21）、额颞叶变性（FTLD：n=14）和对照组（n=13）的患者接受了[F18]氟脱氧葡萄糖正电子发射断层扫描（FDG-PET）扫描和磁共振成像（MRI），以及临床和行为评估。FDG-PET 和 MRI 数据经过共同处理，使两种模态的所有区域都能精确重叠。支持向量机分类分别应用于两种模态的不同参数，并应用于从 MR 和 FDG-PET 图像获得的综合信息。ROI 是从对这两种疾病进行的全面系统和定量荟萃分析中提取出来的。
使用单模态全脑和 ROI 信息，FDG-PET 提供了 AD 和 FTLD 检测和区分的最高准确率，与 MRI 提供的结构信息相比。基于 ROI 的多模态分类，结合 FDG-PET 和 MRI 信息，高度优于单模态方法和全脑模式分类。使用这种方法，可以达到对三组进行区分的准确率高达 92%，对 AD 和 FTLD 患者进行区分的准确率为 94%。
使用两种成像模式的综合信息获得的准确率是迄今为止报告的区分两种类型痴呆的最高准确率。我们的结果表明，使用 FDG-PET 和 MRI 信息的综合信息可以显著提高准确率，并建议将这种方法纳入临床诊断和神经退行性疾病的鉴别诊断程序。
