Abstract #1785

Predicting patient survival in hepatocellular carcinoma (HCC) from diffusion weighted magnetic resonance imaging (DW-MRI) data using neural networks

Florian Ettlinger¹, Patrick Christ¹, Georgios Kaissis², Freba Ahmaddy², Felix Grün¹, Sebastian Schlecht¹, Alexander Valentinitsch², Seyed-Ahmad Ahmadi³, Bjoern Menze¹, and Rickmer Braren²

¹Image-Based Biomedical Modeling Group, Technical University of Munich, Munich, Germany, ²Institute of Radiology, Technical University of Munich, Munich, Germany, ³Department of Neurology, Ludwig Maximilian University of Munich

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.

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