Center for Dementia, Fukujuji Hospital, Japan Anti-Tuberculosis Association, Kiyose, 204-8522, Japan.
Department of Diagnostic Radiology, Tokyo Metropolitan Geriatric Hospital, Tokyo, 173-0015, Japan.
J Neurol. 2020 Jul;267(7):1960-1969. doi: 10.1007/s00415-020-09790-8. Epub 2020 Mar 13.
To differentiate dementia with Lewy bodies (DLB) from Alzheimer disease (AD) using a single imaging modality is challenging, because of their common hypometabolic findings. Scaled subprofile modeling/principal component analysis (SSM/PCA), an unsupervised artificial intelligence, has the potential to offer an alternative to image analysis.
We aimed to produce spatial metabolic profiles to discriminate DLB from AD and to identify the characteristics of the profiles.
Fifty individuals each with DLB, AD, and normal cognition (NL) underwent F-FDG-PET and MRI. The spatial metabolic profile to differentiate DLB from AD (DLB-AD discrimination profile) was determined using SSM/PCA with tenfold cross validation. For comparison, we also produced disease-related profiles that can discriminate AD and DLB from NL (AD- and DLB-related profiles, respectively).
The DLB-AD discrimination profile significantly differentiated DLB from AD with comparable accuracy to that of discriminating DLB and AD from NL. The AD- and DLB-related profiles comprised metabolic imaging features typical of each pathology. In contrast, the DLB-AD discrimination profile emphasized preservation in the posterior cingulate cortex (cingulate island sign) and medial temporal lobe, and occipital hypometabolism. Common hypometabolic findings between DLB and AD were less noticeable in the profile. The DLB-related profile significantly correlated with cognitive function and three core features of DLB, whereas the DLB-AD discrimination profile did not.
Spatial metabolic profile that could discriminate DLB from AD emphasized different imaging features and eliminated common findings between DLB and AD. Neither cognitive function nor core features were associated with the profile.
由于路易体痴呆（DLB）和阿尔茨海默病（AD）的常见低代谢发现，使用单一成像方式对其进行区分具有挑战性。无监督人工智能的标度子图建模/主成分分析（SSM/PCA）有可能提供一种替代图像分析的方法。
我们旨在生成空间代谢谱，以区分 DLB 和 AD，并确定谱的特征。
50 名个体分别患有 DLB、AD 和正常认知（NL），接受 F-FDG-PET 和 MRI 检查。使用 SSM/PCA 进行十折交叉验证，确定区分 DLB 和 AD 的空间代谢谱（DLB-AD 鉴别谱）。为了比较，我们还生成了可以区分 AD 和 DLB 与 NL 的疾病相关谱（分别为 AD 和 DLB 相关谱）。
DLB-AD 鉴别谱可显著区分 DLB 和 AD，其准确性与区分 DLB 和 AD 与 NL 相当。AD 和 DLB 相关谱包含每种病理学的典型代谢成像特征。相比之下，DLB-AD 鉴别谱强调后扣带回皮质（扣带回岛征）和内侧颞叶以及枕叶的代谢保留，而 DLB 和 AD 之间常见的低代谢发现则不太明显。DLB 相关谱与认知功能和 DLB 的三个核心特征显著相关，而 DLB-AD 鉴别谱则没有。
可以区分 DLB 和 AD 的空间代谢谱强调了不同的成像特征，并消除了 DLB 和 AD 之间的常见发现。认知功能和核心特征均与该谱无关。
