Affordable Access

Publisher Website

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

  • Blazhenets, Ganna1
  • Ma, Yilong2
  • Sörensen, Arnd1
  • Schiller, Florian1
  • Rücker, Gerta3
  • Eidelberg, David2
  • Frings, Lars1
  • Meyer, Philipp T1
  • 1 Medical Center - University of Freiburg, Germany. , (Germany)
  • 2 Center for Neurosciences, The Feinstein Institute for Medical Research, United States. , (United States)
  • 3 Institute of Medical Biometry and Statistics, Medical Center - University of Freiburg, Germany. , (Germany)
Published Article
Publication Date
Oct 18, 2019
DOI: 10.2967/jnumed.119.230797
PMID: 31628215


The present study examines the predictive values of amyloid PET, 18F-fluorodeoxyglucose (FDG) PET and non-imaging predictors (alone and in combination) for development of Alzheimer dementia (AD) in a large population of patients with mild cognitive impairment (MCI). Methods: 319 patients with MCI from the Alzheimer's disease neuroimaging initiative database were included. In a derivation dataset (n = 159), the following Cox proportional hazard models were constructed, each adjusted for age and sex: i) amyloid PET using 18F-Florbetapir: pattern expression score (PES) of an amyloid-beta AD conversion-related pattern (Aβ-ADCRP) constructed by principle components analysis (PCA); ii) 18F-FDG PET: PES of a previously defined 18F-FDG-based ADCRP, constructed by PCA; iii) non-imaging model: functional activities questionnaire, apolipoprotein E and mini-mental state examination score; iv) 18F-FDG PET + amyloid PET model, v) amyloid PET + non-imaging model, vi) 18F-FDG PET + non-imaging model, and vii) amyloid PET + 18F-FDG PET + non-imaging model. 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: Based on 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 non-imaging model and significantly improved by adding non-imaging variables. Best prediction accuracy was reached by combining 18F-FDG PET, amyloid PET, and non-imaging variables. The combined model yielded five-year free-of-conversion rates of the low-, medium- and high-risk groups of 100%, 64%, and 24%, respectively. Conclusions: 18F-FDG PET, amyloid PET, and non-imaging 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. Copyright © 2019 by the Society of Nuclear Medicine and Molecular Imaging, Inc.

Report this publication


Seen <100 times