## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**FBB-FS \({}^{*}\)**} & \multicolumn{4}{c}{**FBB-PMOD**} & \multirow{2}{*}{**R**} \\ \cline{2-2} \cline{5-10}  & **Sensitivity** & **Specificity** & **Threshold** & \(p\)**-Value** & **Sensitivity** & **Specificity** & **Threshold** & \(p\)**-Value** & **R** \\ \hline FC & 100.00 & 91.67 & 1.099 & \(1.14\times 10^{-8}\) & 91.67 & 100.00 & 1.182 & \(2.20\times 10^{-8}\) & 0.96 \\ LTC & 95.83 & 91.67 & 1.140 & \(1.22\times 10^{-9}\) & 87.50 & 100.00 & 1.225 & \(2.37\times 10^{-9}\) & 0.97 \\ MTC & 75.00 & 100.00 & 1.207 & \(9.43\times 10^{-5}\) & 70.83 & 100.00 & 1.218 & \(2.43\times 10^{-4}\) & 0.95 \\ PC & 95.83 & 100.00 & 1.210 & \(1.07\times 10^{-9}\) & 91.67 & 100.00 & 1.182 & \(2.28\times 10^{-9}\) & 0.96 \\ OC & 95.83 & 100.00 & 1.250 & \(6.82\times 10^{-9}\) & 91.67 & 100.00 & 1.275 & \(3.80\times 10^{-8}\) & 0.97 \\ GCA & 87.50 & 100.00 & 1.237 & \(4.95\times 10^{-9}\) & 87.50 & 100.00 & 1.359 & \(1.45\times 10^{-8}\) & 0.99 \\ GCP & 95.83 & 100.00 & 1.232 & \(3.06\times 10^{-9}\) & 95.83 & 91.67 & 1.341 & \(1.52\times 10^{-9}\) & 0.95 \\ CN & 91.67 & 91.67 & 1.174 & \(1.70\times 10^{-7}\) & 87.50 & 100.00 & 1.373 & \(1.52\times 10^{-8}\) & 0.44 \\ PUT & 95.83 & 83.33 & 1.471 & \(1.24\times 10^{-7}\) & 100.00 & 75.00 & 1.466 & \(7.76\times 10^{-9}\) & 0.76 \\ THA & 87.50 & 75.00 & 1.371 & \(2.92\times 10^{-3}\) & 87.50 & 100.00 & 1.206 & \(2.72\times 10^{-8}\) & 0.45 \\ PQ & 100.00 & 91.67 & 1.160 & \(1.98\times 10^{-9}\) & 100.00 & 91.67 & 1.173 & \(1.39\times 10^{-9}\) & 0.98 \\ Composite & 95.83 & 100.00 & 1.189 & \(1.43\times 10^{-9}\) & 91.67 & 100.00 & 1.210 & \(3.15\times 10^{-9}\) & 0.97 \\ \hline \hline \end{tabular} \({}^{*}\) FS: FreeSurfer.  
\end{table} 
Table 3: Sensitivity, specificity, threshold of SUvr, \(p\)-value in t-test, and correlation coefficient (R) calculated using PMOD and FreeSurfer algorithms in FBB brain PET SUvr. Each value was calculated for FC, LTC, MTC, PC, OC, GCA, GCP, PQ, and composite regions. distribution of the extracted SUVr data were not significantly different in the normal group. It was observed that the SUVr of the normal group of FBB brain PET was distributed with the narrowest value, making a comparison with the patient group the easiest. The SUVr of FPN brain PET did not have a significant difference in comparison value relative to the other two radiopharmaceuticals, and there was no significant difference in classification. The SUVr in the composite region was calculated as the sum of the seven regions (FC, LTC, MTC, PC, OC, GCA, and GCP); therefore, the distribution was reflected in proportion to the volume of the characteristics of each region. The distribution of SUVr in the composite region was not significantly different between the CN and PQ, which was not considered. However, in the CN region, the classification ability of SUVr was lowered. The PQ region presented a box-plot distribution similar to that of the composite region, with a wider data distribution than in the composite region. The sensitivity, specificity, threshold of SUVr, \(p\)-value, and correlation coefficient calculated by PMOD and FreeSurfer software using FBB, FMM, and FPN brain PET SUVr are summarized in Tables 3-5. Amyloid negative and positive diagnosis results of nuclear medicine specialists using brain PET SUVr in the FC, LTC, MTC, PC, OC, GCA, GCP, PQ, and composite regions were used. The SUVr threshold values for the quantitative analysis to classify amyloid positives and negatives are presented in Tables 3-5.