In this work we present a method to predict patient survival in hepatocellular carcinoma (HCC). We automatically segment HCC from DW-MRI images using fully convolutional neural networks. In a second step we predict patient survival rates by calculating different features from ADC maps. We calculate Histogram features, Haralick features and propose new features trained by a 3D Convolutional Neural Network (SurvivalNet). Applied to 31 HCC cases, SurvivalNet accomplishes a classification accuracy of 65% at a precision and sensitivity of 64% and 65% when trained using our automatic tumor segmentation in a fully automatic fashion.