TY - JOUR T1 - <strong>Predictive value of quantitative F-18-Florbetapir and F-18-FDG PET for conversion from MCI to AD</strong> JF - Journal of Nuclear Medicine JO - J Nucl Med SP - 249 LP - 249 VL - 60 IS - supplement 1 AU - Ganna Blazhenets AU - Arnd Soerensen AU - Florian Schiller AU - Lars Frings AU - Philipp Meyer Y1 - 2019/05/01 UR - http://jnm.snmjournals.org/content/60/supplement_1/249.abstract N2 - 249Introduction: Cerebral β-amyloid load (Aβ) on one hand, and regional hypometabolism on the other hand were proposed as predictors of conversion from mild cognitive impairment (MCI) to Alzheimer’s dementia (AD). Given the limited availability of comparative studies, the present study examines the predictive values of F-18-Florbetapir (AV45), F-18-FDG (FDG) PET and clinical variables, separately and in combination in a large population. Methods: 319 MCI patients from the ADNI database (median follow-up: 47 [95% CI: 35-54] months) were studied. For FDG PET, we assessed the pattern expression score (PES) of a recently validated AD conversion-related pattern (ADCRP) [1], using voxel-based principal component analysis (PCA) [2]. For assessment of Aβ load with AV45 PET, we calculated the standardized uptake value ratio (SUVR) in AD-typical regions using the cerebellum as reference. In a training dataset (n=159), Cox proportional hazards regressions were applied to estimate the prognostic value of candidate predictors (adjusted for age and sex): i) clinical variables (APOE; functional activities questionnaire (FAQ)), ii) clinical variables combined with PES of ADCRP (FDG), and iii) clinical variables combined with Aβ load. For model validation, the results of each Cox regressions were applied to a test dataset (n=160) by calculating the prognostic index (PI) and stratifying each subject according to the predicted conversion risk (i.e., equally-sized groups of low-, medium- and high-risk). Results: PES (HR=2.38 per two standard deviations increase), FAQ (HR=2.12) and Aβ load (HR=2.09) were found to be significant independent predictors (all p&amp;#8804;0.001). In the training dataset, combining clinical variables with PES yielded a significantly better (p&lt;0.001) model fit than combining clinical variables with Aβ or clinical variables alone (AIC=318, 327 and 339, respectively); best prediction accuracy was reached combining PES, Aβ and clinical variables into a combined model (AIC=300). 5-year conversion-free survival rates for the low, medium and high risk groups were 96%, 77% and 19% for PES, 97%, 64% and 44% for Aβ and 100%, 70% and 28% for PES and Aβ (clinical variables always included). Conclusions: Hypometabolism, Aβ and clinical variables represent complementary predictors of conversion from MCI to AD. The present study also supports the proposed NIA-AA research framework towards a biological definition of Alzheimer’s disease. References: 1. Blazhenets G., Ma Y., Sörensen A., Rücker G., Schiller F., Eidelberg D., Frings L., Meyer P.T. Principal component analysis of brain metabolism predicts development of Alzheimer's dementia. J Nucl Med. 2018 Nov 2. pii: jnumed.118.219097. doi: 10.2967/jnumed.118.219097. 2. Eidelberg D. Metabolic brain networks in neurodegenerative disorders: a functional imaging approach. Trends Neurosci. 2009; 32:548-557. ER -