Application of PET-based neuroimaging ATN framework in the diagnosis of Alzheimer′s disease
10.3760/cma.j.cn321828-20240117-00024
- VernacularTitle:基于PET的神经影像学ATN框架在阿尔茨海默病诊断中的应用
- Author:
Min XIONG
1
;
Hongji YOU
;
Xiaoming LUO
;
Yipei LIU
;
Shengnan JIANG
Author Information
1. 广州医科大学附属第二医院核医学科，广州 510260
- Keywords:
Alzheimer disease;
Cognitive dysfunction;
Ethylene glycols;
Carbolines;
Fluorodeoxyglucose F18;
Positron-emission tomography
- From:
Chinese Journal of Nuclear Medicine and Molecular Imaging
2024;44(12):705-711
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To explore the value of the amyloid-tau-neurodegeneration (ATN) framework in neuroimaging based on PET for diagnosing mild cognitive impairment (MCI) and Alzheimer′s disease (AD), and analyze its relationship with clinical cognition.Methods:From May 2022 to March 2024, a total of 98 cases (23 males and 75 females, age (67.8±8.6) years) with a diagnosis of AD, MCI, or non-AD (control patients, CP) who underwent 18F-FDG, 18F-AV45, and 18F-AV1451 PET/CT imaging in the Second Affiliated Hospital of Guangzhou Medical University were included retrospectively. The clinical data, Mini-Mental State Examination (MMSE), and Montreal Cognitive Assessment (MoCA) scores were recorded. Cases were divided into MCI group, mild AD group, moderate AD group, moderate-severe AD group, and CP group. PET images were visually and semi-quantitatively evaluated. SUV mean and SUV ratio (SUVR) were obtained from independent brain regions of 18F-FDG ( n=8), 18F-AV45 ( n=14) and 18F-AV1451 ( n=14). ROC curve analysis was performed with clinical diagnosis as a criterion. The consistency between visual assessment and the clinical diagnosis was analyzed by Cohen′s Kappa coefficient. Semi-quantitative comparisons between groups were performed using the independent-sample t test, one-way analysis of variance, Mann-Whitney U test, or Kruskal-Wallis rank sum test. Age was used as a covariate to calculate the partial correlation coefficient between SUVR and cognitive scores. Results:The sensitivity and specificity of comprehensive visual assessment in diagnosing AD+ MCI were 87.65%(71/81) and 14/17 respectively, showing a moderate consistency with clinical diagnosis ( Kappa=0.60, P<0.001). Semi-quantitative analysis showed that 18F-FDG uptakes in all independent brain regions of MCI patients were higher than those of AD patients, whereas the uptakes of 18F-AV45 and 18F-AV1451 were lower ( t values: 2.66-3.95, z values: 4.98-15.04, all P<0.05). The difference in 18F-AV45 uptake among the three subgroups of AD was relatively small ( H values: 0.46-4.06, F values: 0.03-0.08, all P>0.05). Except for the medial temporal and occipital lobes, the 18F-AV1451 uptake in the moderate-severe AD group tended to be higher than that in the moderate and mild AD groups, though not statistically significant ( H values: 0.20-5.17, all P>0.05). 18F-FDG PET semi-quantitatively distinguished MCI from CP with a high sensitivity (13/14), 18F-AV45 demonstrated a high sensitivity for diagnosing AD+ MCI (92.59%, 75/81), and 18F-AV1451 had a high specificity for distinguishing AD from MCI (14/14) (AUCs: 0.87, 0.90 and 0.92). The uptakes of 18F-FDG in gray matter of AD and MCI patients were positively correlated with MMSE and MoCA scores ( r values: 0.30-0.43, 0.29-0.45, all P<0.05), while the uptakes of 18F-AV45 and 18F-AV1451 were negatively correlated with MMSE and MoCA scores ( 18F-AV45, r values: from -0.39 to -0.30, from -0.38 to -0.30, all P<0.05; 18F-AV1451, r values: from -0.50 to -0.28, from -0.53 to -0.28, except for medial temporal lobe P>0.05, all others P<0.05). Conclusion:The PET-based neuroimaging ATN framework is helpful for early diagnosis of MCI and AD, as well as for AD staging, and may reflect the disease progression and clinical cognitive status of AD to a certain extent.
