Deep Learning Versus Classical Regression for Brain Tumor Patient Survival Prediction

Abstract

Deep learning for regression tasks on medical imaging datahas shown promising results. However, compared to other approaches,their power is strongly linked to the dataset size. In this study, we eval-uate 3D-convolutional neural networks (CNNs) and classical regressionmethods with hand-crafted features for survival time regression of pa-tients with high-grade brain tumors. The tested CNNs for regressionshowed promising but unstable results. The best performing deep learn-ing approach reached an accuracy of 51.5% on held-out samples of thetraining set. All tested deep learning experiments were outperformed bya Support Vector Classifier (SVC) using 30 radiomic features. The inves-tigated features included intensity, shape, location and deep features.The submitted method to the BraTS 2018 survival prediction challenge isan ensemble of SVCs, which reached a cross-validated accuracy of 72.2%on the BraTS 2018 training set, 57.1% on the validation set, and 42.9%on the testing set.The results suggest that more training data is necessary for a stable per-formance of a CNN model for direct regression from magnetic resonanceimages, and that non-imaging clinical patient information is crucial alongwith imaging information