Chen Xueqi, Zhou Yun, Wang Rongfu, Cao Haoyin, Reid Savina, Gao Rui, Han Dong
Department of Nuclear Medicine, Peking University First Hospital, Beijing, China.
The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America.
PLoS One. 2016 May 16;11(5):e0154406. doi: 10.1371/journal.pone.0154406. eCollection 2016.
To evaluate the potential clinical value of quantitative functional FDG PET and pathological amyloid-β PET with cerebrospinal fluid (CSF) biomarkers and clinical assessments in the prediction of Alzheimer's disease (AD) progression.
We studied 82 subjects for up to 96 months (median = 84 months) in a longitudinal Alzheimer's Disease Neuroimaging Initiative (ADNI) project. All preprocessed PET images were spatially normalized to standard Montreal Neurologic Institute space. Regions of interest (ROI) were defined on MRI template, and standard uptake values ratios (SUVRs) to the cerebellum for FDG and amyloid-β PET were calculated. Predictive values of single and multiparametric PET biomarkers with and without clinical assessments and CSF biomarkers for AD progression were evaluated using receiver operating characteristic (ROC) analysis and logistic regression model.
The posterior precuneus and cingulate SUVRs were identified for both FDG and amyloid-β PET in predicating progression in normal controls (NCs) and subjects with mild cognitive impairment (MCI). FDG parietal and lateral temporal SUVRs were suggested for monitoring NCs and MCI group progression, respectively. 18F-AV45 global cortex attained (78.6%, 74.5%, 75.4%) (sensitivity, specificity, accuracy) in predicting NC progression, which is comparable to the 11C-PiB global cortex SUVR's in predicting MCI to AD. A logistic regression model to combine FDG parietal and posterior precuneus SUVR and Alzheimer's Disease Assessment Scale-Cognitive (ADAS-Cog) Total Mod was identified in predicating NC progression with (80.0%, 94.9%, 93.9%) (sensitivity, specificity, accuracy). The selected model including FDG posterior cingulate SUVR, ADAS-Cog Total Mod, and Mini-Mental State Exam (MMSE) scores for predicating MCI to AD attained (96.4%, 81.2%, 83.6%) (sensitivity, specificity, accuracy). 11C-PiB medial temporal SUVR with MMSE significantly increased 11C-PiB PET AUC to 0.915 (p<0.05) in predicating MCI to AD with (77.8%, 90.4%, 88.5%) (sensitivity, specificity, accuracy).
Quantitative FDG and 11C-PiB PET with clinical cognitive assessments significantly improved accuracy in the predication of AD progression.
评估定量功能氟代脱氧葡萄糖正电子发射断层扫描（FDG PET）和病理性淀粉样蛋白-β正电子发射断层扫描（amyloid-β PET）联合脑脊液（CSF）生物标志物及临床评估在预测阿尔茨海默病（AD）进展方面的潜在临床价值。
在一项纵向的阿尔茨海默病神经影像学倡议（ADNI）项目中，我们对82名受试者进行了长达96个月（中位数 = 84个月）的研究。所有预处理后的PET图像均在空间上归一化至标准的蒙特利尔神经病学研究所空间。在MRI模板上定义感兴趣区域（ROI），并计算FDG和淀粉样蛋白-β PET相对于小脑的标准摄取值比率（SUVRs）。使用受试者操作特征（ROC）分析和逻辑回归模型评估单参数和多参数PET生物标志物联合或不联合临床评估及CSF生物标志物对AD进展的预测价值。
在预测正常对照（NCs）和轻度认知障碍（MCI）受试者的病情进展方面，FDG和淀粉样蛋白-β PET均识别出了后楔前叶和扣带回的SUVRs。建议分别用FDG顶叶和外侧颞叶的SUVRs来监测NCs组和MCI组的病情进展。18F-AV45全脑皮质在预测NCs病情进展时达到了（78.6%，74.5%，75.4%）（敏感性、特异性、准确性），这与11C-PiB全脑皮质SUVRs在预测MCI向AD转化时相当。在预测NCs病情进展方面，一个结合FDG顶叶和后楔前叶SUVR以及阿尔茨海默病评估量表 - 认知部分（ADAS-Cog）总分的逻辑回归模型达到了（80.0%，94.9%，93.9%）（敏感性、特异性、准确性）。所选择的包括FDG扣带回后叶SUVR、ADAS-Cog总分和简易精神状态检查表（MMSE）分数的模型在预测MCI向AD转化时达到了（96.4%，81.2%，83.6%）（敏感性、特异性、准确性）。11C-PiB内侧颞叶SUVR联合MMSE在预测MCI向AD转化时，显著将11C-PiB PET的曲线下面积（AUC）提高到了0.915（p<0.05），达到了（77.8%，90.4%，88.5%）（敏感性、特异性、准确性）。
定量FDG和11C-PiB PET联合临床认知评估显著提高了AD进展预测的准确性。
