Survival Prediction of Glioma Tumors Using Feature Selection and Linear Regression

Early diagnosis of brain tumor is crucial for treatment planning. Quantitative analyses of segmentation can provide information for tumor survival prediction. The effectiveness of convolutional neural network (CNN) has been validated in medical image segmentation. In this study, we apply a widely-employed CNN namely UNet to automatically segment out glioma sub-regions, and then extract their volumes and surface areas. A sophisticated machine learning scheme, consisting of mutual information feature selection and multivariate linear regression, is then used to predict individual survival time. The proposed method achieves an accuracy of 0.475 on 369 training data based on leave-one-out cross-validation. Compared with using all features, using features obtained from the employed feature selection technology can enhance the survival prediction performance.