## 3 Results
### Training and Testing of the Models
After structural modifications to each CNN for re-purposing, the memory size of each fold of the trained CNNs dropped to 86 MB, 3 MB, and 68 MB for ResNet-50, SqueezeNet, and DenseNet-201, respectively. The size drop is due to the different number of possible outputs (from 1000 classes to only 2 classes). Even if SqueezeNet memory size was significantly lower, the very low performances in terms of sensitivity prevented this model architecture from being a valuable option compared to the other two. Subanalysis of the results for the internal independent testing according to the CILCA type revealed that of the 43 CILCA 1, 25 (58.1%) were true positive and 18 (41.9%) were false negative, while of the 20 CILCA 2, 17 (85.9%) were true positive and only 3 (15.0%) were false negative (\(p\) = 0.046, [PERSON] exact test). Considering the whole number of 63 CILCA, of 54 cases with contrast agent administration, 35 (64.8%) were true positive, and 19 (35.2%) were false negative, while of 9 cases without contrast agent administration, 7 (77.8%) were true positive and 2 (22.2%) were false negative (\(p\) = 0.705, [PERSON] exact test). Finally, considering the whole number of 63 CILCA, of the 27 cases with slice reconstruction \(\leq\) 1.5 mm, 18 (66.7%) were true positive and 9 (33.3%) were false negative, \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & **Training** & **ROC-AUC** & **Sensitivity** & **Specificity** & **PPV** & **NPVV** & \multirow{2}{*}{**LR+**} & \multirow{2}{*}{**LR-**} \\  & **or IIT** & **(95\% CD)** & **(95\% CD)** & **(95\% CD)** & **(95\% CD)** & **(95\% CD)** & **LR-** \\ \hline \multirow{2}{*}{ResNet50} & \multirow{2}{*}{Training} & 82.3 & 66.7\% & 85.3\% & 70.2\% & 83.2\% & 4.5 & 0.4 \\  & & (75.5–88.0\%) & (53.3–78.3\%) & (77.6–91.2\%) & (56.6–81.6\%) & (75.2–89.4\%) & (2.8–7.3) & (0.3–0.6) \\ \hline \multirow{2}{*}{ResNet50} & \multirow{2}{*}{IIT} & 83.7\% & 63.5\% & 87.3\% & 83.3\% & 70.5\% & 5.0 & 0.4 \\  & & (75.2–90.9\%) & (50.4–75.3\%) & (76.5–94.4\%) & (69.8–92.5\%) & (59.1–80.3\%) & (2.6–9.8) & (0.3–0.6) \\ \hline \multirow{2}{*}{SqueezeNet} & \multirow{2}{*}{Training} & 67.5\% & 36.7\% & 84.5\% & 55.0\% & 72.1\% & 2.4 & 0.8 \\  & & (60.1–74.3\%) & (24.6–50.1\%) & (76.6–90.5\%) & (38.5–70.7\%) & (63.7–79.4\%) & (1.4–4.1) & (0.6–0.9) \\ \hline \multirow{2}{*}{SqueezeNet} & \multirow{2}{*}{IIT} & 72.3\% & 30.2\% & 96.8\% & 90.5\% & 58.1\% & 9.5 & 0.7 \\  & & (63.2–80.7\%) & (19.2–43.0\%) & (89.0–99.6\%) & (69.6–98.8\%) & (48.1–67.7\%) & (2.3–39.1) & (0.6–0.9) \\ \hline \multirow{2}{*}{DenseNet201} & \multirow{2}{*}{Training} & 83.4\% & 75.0\% & 82.8\% & 69.2\% & 86.5\% & 4.4 & 0.3 \\  & & (67.7–89.0\%) & (62.1–85.3\%) & (74.6–89.1\%) & (56.6–80.1\%) & (78.7–92.2\%) & (2.8–6.7) & (0.2–0.5) \\ \hline \multirow{2}{*}{DenseNet201} & \multirow{2}{*}{IIT} & 87.0\% & 66.7\% & 90.5\% & 87.5\% & 73.1\% & 7.0 & 0.4 \\  & & (78.8–93.8\%) & (53.7–78.0\%) & (80.4–96.4\%) & (74.8–95.3\%) & (61.8–82.5\%) & (3.2–15.3) & (0.3–0.5) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performances obtained by the three deep learning models. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Overall** & **Contrast- Enhanced** & **Unenhanced Scans** & \(\leq\)**1.5-mm Slic** & **\(>\)1.5-mm Slic** \\  & **(\(n\) = 100)** & **Scans (\(n\) = 80)** & **(\(n\) = 20)** & **Thickness** & **Thickness** \\  & 0.904 & 0.911 & 0.878 & 0.911 & 0.893 \\ Gross Dice coefficient & (0.899, 0.909) & (0.906, 0.915) & (0.866, 0.891) & (0.906, 0.917) & (0.884, 0.902) \\ \hline Refined Dice & 0.916 & 0.922 & 0.893 & 0.923 & 0.905 \\ coefficient & (0.911, 0.922) & (0.917, 0.927) & (0.880, 0.906) & (0.917, 0.929) & (0.896, 0.914) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Mean gross and refined Dice coefficients with 95% confidence intervals considering un-enhanced versus contrast-enhanced scans and \(\leq\)1.5-mm vs. \(>\)1.5-mm slice thickness (data from Center 1).