The present study examined the predictive values of amyloid PET, ¹⁸F-FDG PET, and nonimaging predictors (alone and in combination) for development of Alzheimer dementia (AD) in a large population of patients with mild cognitive impairment (MCI). Methods: The study included 319 patients with MCI from the Alzheimer Disease Neuroimaging Initiative database. In a derivation dataset (n = 159), the following Cox proportional-hazards models were constructed, each adjusted for age and sex: amyloid PET using ¹⁸F-florbetapir (pattern expression score of an amyloid-β AD conversion–related pattern, constructed by principle-components analysis); ¹⁸F-FDG PET (pattern expression score of a previously defined ¹⁸F-FDG–based AD conversion–related pattern, constructed by principle-components analysis); nonimaging (functional activities questionnaire, apolipoprotein E, and mini-mental state examination score); ¹⁸F-FDG PET + amyloid PET; amyloid PET + nonimaging; ¹⁸F-FDG PET + nonimaging; and amyloid PET + ¹⁸F-FDG PET + nonimaging. In a second step, the results of Cox regressions were applied to a validation dataset (n = 160) to stratify subjects according to the predicted conversion risk. Results: On the basis of the independent validation dataset, the ¹⁸F-FDG PET model yielded a significantly higher predictive value than the amyloid PET model. However, both were inferior to the nonimaging model and were significantly improved by the addition of nonimaging variables. The best prediction accuracy was reached by combining ¹⁸F-FDG PET, amyloid PET, and nonimaging variables. The combined model yielded 5-y free-of-conversion rates of 100%, 64%, and 24% for the low-, medium- and high-risk groups, respectively. Conclusion: ¹⁸F-FDG PET, amyloid PET, and nonimaging variables represent complementary predictors of conversion from MCI to AD. Especially in combination, they enable an accurate stratification of patients according to their conversion risks, which is of great interest for patient care and clinical trials.

本研究检验了淀粉样蛋白PET，¹⁸ F-FDG PET和非影像学预测因子（单独或组合使用）在大量轻度认知障碍（MCI）患者中发展阿尔茨海默氏痴呆症（AD）的预测价值。方法：该研究包括来自阿尔茨海默氏病神经影像计划数据库的319例MCI患者。在派生数据集（n = 159）中，构建了以下Cox比例风险模型，每个模型都针对年龄和性别进行了调整：使用¹⁸ F-florbetapir的淀粉样蛋白PET （构建与淀粉样蛋白-βAD转化相关的模式的表达得分）通过主成分分析）；¹⁸ F-FDG PET（先前定义的模式表达得分¹⁸基于F-FDG的AD转换相关模式，通过主成分分析构建）；非影像学（功能活动问卷，载脂蛋白E和小精神状态检查分数）；¹⁸ F-FDG PET +淀粉样蛋白PET; 淀粉样蛋白PET +非显像; ¹⁸ F-FDG PET +非成像；和淀粉样蛋白PET + ¹⁸ F-FDG PET +非成像。第二步，将Cox回归的结果应用于验证数据集（n = 160），以根据预测的转换风险对受试者进行分层。结果：在独立验证数据集的基础上，¹⁸F-FDG PET模型产生的预测值明显高于淀粉样蛋白PET模型。但是，两者均低于非成像模型，并且通过添加非成像变量而得到了显着改善。通过组合^18个F-FDG PET，淀粉样蛋白PET和非成像变量，可以达到最佳的预测精度。组合模型得出的低，中，高风险人群的5年无转化率分别为100％，64％和24％。结论： ¹⁸ F-FDG PET，淀粉样蛋白PET和非成像变量代表从MCI到AD转化的互补预测因子。尤其是结合使用时，它们可以根据患者的转化风险对患者进行准确的分层，这对于患者护理和临床试验非常重要。