Predictive Value of 18F-Florbetapir and 18F-FDG PET for Conversion from Mild Cognitive Impairment to Alzheimer Dementia

Abstract

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.

Keywords: 18F-FDG; 18F-florbetapir; Cox model; PCA; PET; amyloid load; mild cognitive impairment.

FIGURE 1.

ADCRPs of amyloid binding and glucose metabolism. (A) Aβ-ADCRP derived by PCA on ¹⁸F-florbetapir PET data. (B) ADCRP derived by PCA on ¹⁸F-FDG PET data. Voxels with negative region weights (coded as z score) are given in cool colors, and regions with positive region weights are depicted in warm colors. Data on neurologic orientation (i.e., left image side corresponds to patients’ left body side) are presented. Regions with increased amyloid load (A) and decreased metabolism (B) show positive and negative weights, respectively, whereas relatively spared regions are loaded with opposite weights due to data normalization.

FIGURE 2.

Kaplan–Meier curves of validation dataset. Risk strata using PI are based on amyloid PET model (A), ¹⁸F-FDG PET model (B), nonimaging model (C), amyloid PET + nonimaging model (D), ¹⁸F-FDG PET + nonimaging model (E), and amyloid PET + ¹⁸F-FDG PET + nonimaging model (F).