# Predicting Multi-Gene Mutation Based on Lung Cancer CT Images and Mut-SeResNet
## 5 Discussion
### Comparison with Recent Studies and Methods  
Among the studies on deep learning methods for predicting gene mutations, the comparison is shown in Table 5. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline
**Model** & **Data** & \multicolumn{2}{c}{**EGFR (Single-Task)**} & \multicolumn{2}{c}{**KRAS (Single-Task)**} & \multicolumn{2}{c}{**EGFR (Mut-Task)**} & \multicolumn{2}{c}{**KRAS (Mut-Task)**} \\ \hline  & & **Acc** & **Sen** & **Spe** & **Acc** & **Sen** & **Spe** & **Acc** & **Sen** & **Spe** & **Acc** & **Sen** & **Spe** \\ \cline{3-14}  & & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** \\ \hline \multirow{2}{*}{VGG16} & Train & 72.1 & 67.8 & 79.2 & 67.1 & 32.3 & 70.8 & 74.1 & 68.5 & 78.4 & 74.6 & 69.9 & 75.3 \\  & Test & 70.3 & 65.0 & 78.4 & 65.8 & 60.1 & 68.4 & 72.9 & 67.27 & 76.9 & 73.3 & 67.8 & 76.8 \\ \hline \multirow{2}{*}{InceptionV3} & Train & 63.7 & 60.5 & 67.1 & 62.4 & 61.0 & 64.9 & 65.18 & 64.82 & 66.2 & 64.1 & 63.2 & 65.6 \\  & Test & 60.2 & 58.6 & 64.6 & 59.8 & 58.5 & 61.9 & 62.34 & 62.13 & 62.5 & 60.9 & 59.02 & 62.4 \\ \hline \multirow{2}{*}{Inception-ResNet-v2} & Train & 71.0 & 69.2 & 71.4 & 67.5 & 66.2 & 69.0 & 71.29 & 68.56 & 73.4 & 69.2 & 67.1 & 71.1 \\  & Test & 67.9 & 67.0 & 68.1 & 64.4 & 63.5 & 66.1 & 68.03 & 65.07 & 70.0 & 66.3 & 64.2 & 67.74 \\ \hline \multirow{2}{*}{Mut-SeResnet} & Train & 84.9 & 72.0 & 75.4 & 78.2 & 75.2 & 76.5 & 96.7 & 78.2 & 81.3 & 95.8 & 71.5 & 74.2 \\  & Test & 76.1 & 68.9 & 71.8 & 74.0 & 70.3 & 73.3 & 89.7 & 71.28 & 76.6 & 88.3 & 68.2 & 75.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Predictive performance of various deep methods on the dataset. \begin{table}
\begin{tabular}{c c c c c c c c} \hline  & **Literature** & **Number of Patients** & **Images** & **Extracted Features** & **AUC** & **Acc (\%)** & **Sen (\%)** & **Spe (\%)** \\ \hline A & [PERSON] [36] & 14926 & CT & Clinical features & 0.85 & - & - & - \\ B & Eur Respir J [37] & 285 & CT & Radiomics features & EGFR (0.82) & & & \\  & [PERSON] & & & & KRAS & (0.67) & & - & - \\ [PERSON] et al [38] & 668 & CT & Radiomics features & 0.903 & 84 & - & - \\ [PERSON] [39] & 503 & CT & Deep features, clinical features & 0.838 & 77.20 & 75.80\% & 79.10\% \\ [PERSON] & & & & & & & \\  & et al [40] & DSB17 & CT & Deep features & - & 64 & - & - \\  & [PERSON] & & & & & & & \\ [PERSON] et al & - & CT & Clinical features & - & 82.89 & - & - \\  & & [PERSON] & & & & & & & \\ [PERSON] & 6306 & CT & Deep features & - & 84.13 & 91.69\% & 73.16\% \\  & et al [42] & & & & & EGFR & EGFR & EGFR (76.6) \\ \hline \multirow{2}{*}{Mut-SeResNet} & & 363 & CT & Deep features, clinical features & & (89.7\%) & (71.28) & & KRAS \\  & & & & & & & & (88.3\%) & (68.2) & (75.2) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison with recent studies. Literature A predicted EGFR gene mutations and achieved a prediction AUC (Area Under Curve) of 0.85. When compared to other studies, A was used for a larger dataset and was using deep learning techniques to predict gene mutations. However, A required experimental equipment with better performance. Literature B predicted gene mutations by using imaging omics and achieved an AUC value of 0.670, which further validated that the extraction of clinical features facilitated improved prediction performance. Literature C trained weakly supervised learning algorithms on whole lymph node images of lung cancer patients, and had an accuracy and AUC of 84% and 0.903 with a small amount of training data, respectively. Literature D used 3D CNN to predict EGFR gene mutations in lung adenocarcinoma, and achieved AUC prediction values of 0.776 and then 0.838 with the addition of clinical information, respectively. It was verified that adding a small amount of clinical information to the model could effectively improve the predictive performance of the model. Both literature E and literature F built 3D convolutional neural networks.