# Predicting Multi-Gene Mutation Based on Lung Cancer CT Images and Mut-SeResNet
## 6 Conclusions  
This paper proposed a deep learning model based on residual networks to predict EGFR and KRAS gene mutations in non-small cell lung cancer. We used the dataset of 363 patients collected from collaborating hospitals to train our Mut-SeResNet model. The prediction accuracy for EGFR and KRAS mutations was 89.7% and 88.3%, respectively.  
The reason for choosing ResNet as the base network is that the residual learning network solves the problem of gradient disappearance in deep networks. It also has the advantages of simple and modular network structure. However, the residual network has problems, such as channel interaction and restricted perceptual field, which result in the loss of local features of the model. By introducing a squeeze and excitation attention mechanism to capture the correlation between features, the channel-wise feature representation was adaptively recalibrated. To verify the rigor of this work, we (1) explored the impact of different combinations of ResNet and self-attention on the model performance; (2) to further validate the performance of the model proposed in this paper, we presented the experimental results visually, which showed that the module was able to focus more model attention on more discriminative features; and (3) we compared recent studies, which showed that this experiment achieved a high accuracy rate.  
However, this work still has limitations. Only one hospital dataset was collected, and the proposed method will be validated using at least two hospital datasets in the next study.  
The results showed that the model could predict both EGFR and KRAS mutations in non-small cell lung cancer. The main advantage of the proposed Mut-SeResnet model is that it can dynamically extract image features and change the weight of different channels during the training process to improve performance. In addition, the analysis of medical images (CT, PET, or Mr/CT) by this method may also reveal other features of the disease, including the proliferative activity of the tumor, the hypoxic state, and the surrounding tumor environment. The development of deep networks can actively advance the development of individualized medicine and clinical applications.  
There is room for improvement in CT image-based gene mutation detection methods, which may be valuable in future clinics.  
Conceptualization, L.S. and Y.D.; methodology, L.S. and Y.D.; software, L.S. and X.F. ([PERSON]); validation, Y.D. and X.F. ([PERSON]); formal analysis, Y.D. and X.F. ([PERSON]); investigation, L.S. and Y.D.; resources, Y.D., X.F. ([PERSON]) and S.X.; data curation, Y.D.; writing--original draft preparation, L.S. and Y.D.; writing--review and editing, L.S., Y.D. and S.X.; visualization, S.X. and X.F. ([PERSON]); supervision, Y.D.; project administration, L.S. and Y.D.; funding acquisition, L.S., Y.D., S.X. and X.F. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This work is supported by the National Natural Science Foundation of China (No. 62102280); the Key Research and Development Program of Shanxi Province (No. 202102020101001, 202102020101007); and the Applied Basic Research Program of Shanxi Province (NO. 20210302124167).  
The authors declare no conflict of interest.