Liver Tumor Image Enhancement and CDK1 Gene Mutation Prediction Method

Abstract

Liver cancer is one of the most common malignancies, which has extremely high mortality rate. Gene sequencing can reveal genetic variants of hepatocytes. The CDK1 gene has the potential to target anti-tumor. Therefore, the prediction of CDK1 gene mutation is of great significance for the diagnosis and treatment. In this paper, a new method for predicting CDK1 gene mutation is proposed. A novel tumor image enhancement converts the CT images into low-exposure images, high-exposure images and tumor detail-enhanced images. These images are effective to enhance interstitial and necrotic area, tumor parenchyma, tumor texture and edge features, respectively. CDK1 gene mutation prediction is modeled with deep neural network. A multi-strategy fusion loss function, which solves the imbalance of sample categories and hard samples, is used to improve the prediction performance. Comparative experiments are designed to verify the effectiveness of the proposed methods. The CDK1 gene mutation prediction after enhancement improves the accuracy of the classifier, which was 0.2 higher than others. The model with multi-strategy fusion loss function outperformed 0.116 in AUC than compared loss function. The proposed enhancement method is capable to improve the performance of classification. The multi-strategy fusion loss function comprehensively improves the indicators of the classifier.