Diagnostic and prognostic value of amyloid PET textural and shape features: comparison with classical semi-quantitative rating in 760 patients from the ADNI-2 database

AbstractWe evaluated the performance of amyloid PET textural and shape features in discriminating normal and Alzheimer ’s disease (AD) subjects, and in predicting conversion to AD in subjects with mild cognitive impairment (MCI) or significant memory concern (SMC). Subjects from the Alzheimer’s Disease Neuroimaging Initiative with available baseline18F-florbetapir and T1-MRI scans were included. The cross-sectional cohort consisted of 181 controls and 148  AD subjects. The longitudinal cohort consisted of 431 SMC/MCI subjects, 85 of whom converted to AD during follow-up. PET images were normalized to MNI space and post-processed using in-house software. Relative retention indices (SUVr) were computed with respect to pontine, cerebellar, and composit e reference regions. Several textural and shape features were extracted then combined using a support vector machine (SVM) to build a predictive model of AD conversion. Diagnostic and prognostic performance was evaluated using ROC analysis and survival analysis with the Cox proportional hazard model . The three SUVr and all the tested features effectively discriminated AD subjects in cross-sectional analysis (all p <  0.001). In longitudinal analysis, the variables with the highest prognostic value were composite SUVr (AUC 0.86; accuracy 81%), skewness (0.87; 83%), local minima (0.85; 79%), Geary’s index (0.86; 81%), gradient norm maximal argument (0.83; 82%), and the SVM model (0.91; 86%). The adjusted haza rd ratio...
Source: Brain Imaging and Behavior - Category: Neurology Source Type: research