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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 GrĂ¼n1, 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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