## 3 Results
### \"Real-Life\" Data Sample  
The patients' characteristics from the \"real-life\" test set (4964 PETs) are provided in Table 3.  
Figure 4: The heatmap shows the area of interest used by the algorithm for prediction, which corresponds well with the visualized right lung hypermetabolic tumor. The red arrows show the metabolic anomaly of interest. On the right, the continuous color scale represents the weight assigned to each voxel to make the prediction (dark blue: low weight, to red: maximum weight). The training procedure took 110 h and 30 min (with a mean of 22 h 06 min per block). The results of the 5-fold cross validation are provided in Table 4.  
Fold 4 provided the best results, with a Sen of 62%, Spe of 77%, PPV and NPV of 89.1% and 39.8%, respectively, corresponding to an F1 score of 73%. Finally, when this 3D-CNN was applied to the real-life independent test set (744 PETs), it showed moderate decreases in Sen (61%), NPV (40%), LR+ (2.97) and LR\(-\) (0.49), whereas the Spe and PPV remained good (79% and 90%, respectively). The F1 score showed an almost 20% decrease (from 90% in the \"unambiguous\" test set to 73% in the \"real-life\" test set) and the accuracy was equivalent (64%-IC95% [62.5-69.3]). There was no statistically significant difference in its performance based on gender. An illustrative heatmap of a wrong classification is provided in Figure 5. \begin{table}
\begin{tabular}{l c c c} \hline  & **Overall** & **Training Set** & **Test Set** \\ \hline n & 4964 & 4220 & 744 \\ \hline Age in years & 60.7 \(\pm\) 14.7 & 60.5 \(\pm\) 14.5 & 62.0 \(\pm\) 15.5 \\ \hline Weight in kg & 70.5 \(\pm\) 16 & 70.4 \(\pm\) 16 & 71.1 \(\pm\) 16.4 \\ \hline Activity in MBq & 246.5 \(\pm\) 58 & 246 \(\pm\) 58 & 249.2 \(\pm\) 58.7 \\ \hline \multirow{2}{*}{Sex} & F & 2453 (49.4\%) & 2111 (50.0\%) & 342 (46\%) \\ \cline{2-4}  & M & 2511 (50.6\%) & 2109 (50.0\%) & 402 (54\%) \\ \hline \multirow{2}{*}{Clinical context} & Oncological & 4370 (88.0\%) & 3739 (88.6\%) & 631 (84.8\%) \\ \cline{2-4}  & Non-oncological & 594 (12.0\%) & 481 (11.4\%) & 113 (15.2\%) \\ \hline \multirow{2}{*}{Label} & Normal & 1226 (24.7\%) & 1042 (24.7\%) & 184 (24.7\%) \\ \cline{2-4}  & Abnormal & 3738 (75.3\%) & 3178 (75.3\%) & 560 (75.3\%) \\ \hline \end{tabular}
\end{table} 
Table 3: Patients’ characteristics from the “real-life” data sample. Figure 5: Heatmap of a wrong classification scan from the “real-life” data sample. Here, the heatmaps of two false-positive examinations do not correspond to the metabolic abnormalities on the PET scan. Grey scale images show a coronal slice of a normal FDG-PET scan. Continuous color scale images represents the weight assigned to each voxel to make the prediction (dark blue: low weight, to red: maximum weight). \begin{table}
\begin{tabular}{l c c c c c c c c c c c c c} \hline  & **TP** & **TN** & **FP** & **FN** & **NPV (\%)** & **PPV (\%)** & **Sensitivity (\%)** & **Specificity (\%)** & **Accuracy (\%)** & **F1 Score (\%)** & **LR+** & **LR\(-\)** \\ \hline fold 1 & 340 & 181 & 27 & 269 & 37.9 & 92.6 & 53.5 & 87.0 & 61.7 & 67.8 & 4.1183 & 0.5348 \\ \hline fold 2 & 367 & 161 & 47 & 269 & 37.4 & 88.6 & 57.7 & 77.4 & 62.6 & 69.9 & 2.5507 & 0.5464 \\ \hline fold 3 & 422 & 147 & 61 & 214 & 40.7 & 87.4 & 66.4 & 70.7 & 67.4 & 75.4 & 2.2625 & 0.4761 \\ \hline fold 4 & 391 & 161 & 48 & 244 & 39.8 & 89.1 & 61.6 & 77.0 & 65.4 & 72.8 & 2.6811 & 0.4988 \\ \hline fold 5 & 406 & 139 & 70 & 229 & 37.8 & 85.3 & 63.9 & 66.5 & 64.6 & 73.1 & 1.909 & 0.5422 \\ \hline Mean & 385.2 & 157.8 & 50 & 250.4 & 38.7 & 88.6 & 60.6 & 75.7 & 64.3 & 71.8 & 2.705 & 0.52 \\ \hline Variance & 1048.7 & 257.2 & 265 & 1062.3 & 0.0 & 0.1 & 0.3 & 0.6 & 0.1 & 0.1 & 0.713 & 0.001 \\ \hline Std deviation & 32.38 & 16.04 & 16.29 & 32.59 & 1.4 & 2.7 & 5.1 & 7.8 & 2.3 & 3.0 & 0.844 & 0.031 \\ \hline \end{tabular}
\end{table} 
Table 4: Results of the 5-fold cross validation of the “real-life” data sample.