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Comparison of global and voxel-based diagnostic classification using [18F]florbetapir ROC estimates

      Background

      Accurate diagnosis of Alzheimer’s disease and its prodromal state is of paramount importance for effective intervention. Recent studies have shown that imaging biomarkers provides excellent knowledge for classification but failed to account to a compelling classifier due to usage of consolidated information (global SUVR measurements). We hypothesize that using voxel-based information we would be able to better classify Alzheimer’s individuals from cognitively normal individuals.

      Methods

      [18F]Florbetapir PET images were acquired from 83 subjects (65 Cognitively Normal [CN], 18 Alzheimer’s Disease [AD]) from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. The respective standardized uptake value ratio (SUVR) maps were subsequently generated using cerebellar grey-matter as reference region. Corresponding cortical uptake values (vertex based) were extracted using an average mid-surface structure generated using the study subjects and vertex based Receiver Operating Characteristic (ROC) analysis was carried out to identify the brain regions that best discriminates patients from cognitively normal individuals. ROC analysis based on the consolidated measurements was also carried out as a comparison study.

      Results

      Based on the Area Under the Curve (AUC) values, brain regions including precuneus, posterior cingulate cortex, medial orbitofrontal cortex and temporal lobe showed the best separation between patients and normal individuals with an AUC value of over 0.8. The same regions show sensitivity values of over 0.7 and specificity values of over 0.8 (Figure 1). In the study done using consolidated measures, the best separation resulted in AUC of 0.6872 with a specificity of 0.8 and sensitivity of 0.722.

      Conclusions

      ROC estimate of regional concentrations of brain [18F]Florbetapir may contribute to the identification of pathological patterns of amyloidosis in predementia population and will enable accurate and effective classification in to the disease stages. The preliminary data reported here support the hypothesis that regional amyloidosis might contribute to a better discrimination between AD patients and cognitively normal individuals when compared with a global measurement, and the regions that allows best separation need to be used to generate a better consolidated measurement.
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      Figure 1Image shows Area Under the Curve (AUC), sensitivity and specificity of the regional ROC analysis. Regions including the precuneus, posterior cingulate cortex, medial orbitofrontal cortex and temporal lobe showed the highest AUC value indicating the best separation between AD patients and CN individuals. These regions also show a high sensitivity and specificity.