IC‐P‐025: [¹⁸F]Florbetapir Roc Curve at Every Voxel Revels a Wide Range of Cortical Suvr Cut‐Offs

During the last decades researchers have been using global measurements of amyloid−PET ligands to dichotomize subjects into amyloid-β (Aβ) positive or negative groups. The Aβ dichotomization is desirable to enrich clinical trials population and to assess the influences of Aβ abnormalities on Alzheimer's disease (AD) progression. However, dichotomizations using global measurements do not provide information regarding the regional pattern of Aβ abnormalities, which may be important to identifying nondemented individuals fated to AD clinical progression. Here, we tested the framework that cut-off analysis performed at every voxel may provide additional information as compared to global estimates. We assessed cognitively normal (n=209), mild cognitive impairment (MCI; n=311) and AD (n=81) individuals from ADNI cohort who underwent [18F]Florbetapir PET at baseline (Table 1). The standardized uptake value ratio (SUVR) maps were then generated using the cerebellum grey matter and the global white matter as reference regions. First, a receiver operating characteristic (ROC) curve was performed at every voxel contrasting controls and AD participants. Second, the optimal cut-off value at every voxel was calculated using the least distance from (0,1) point to the ROC curve (best operating point) (Figure 1). Third, parametric maps of diagnostic sensitivity and specificity were generated (Figure 2). Finally, probabilistic maps for baseline Aβ positivity at every voxel were generated for MCI converters (n= 55) and non-converters (n= 256) over 2 years (Figure 3). [18F]Florbetapir SUVR cut-off values at every voxel. [18F]Florbetapir SUVR cut-off values sensitivity and specificity for a diagnostic of probable Alzheimer's disease at every voxel. Probabilistic maps of [18F]Florbetapir SUVR positivity at every voxel for MCI Non-converters and converters. The highest SUVR cut-off values were found in the precuneus, anterior and posterior cingulate cortices, whereas the lowest were found in clusters in the temporal lobe (Figure 1). Diagnostic sensitivity and specificity were the highest in clusters in the precuneus, posterior cingulate, temporal, and frontal cortices (Figure 2). Probabilistic maps showed that MCI non-converters did not present a specific pattern of amyloid deposition at baseline, whereas MCI converters reached 100% of positivity in voxels in the posterior cingulate, precuneus, frontal and temporal cortices (Figure 3). Our results revealed that the analysis of amyloid-PET cut-offs at every voxel might provide important information regarding the patterns of regional Aβ abnormalities associated with the clinical progression to AD.

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