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Automatic non-invasive assessment of hepatocellular carcinoma (HCC)malignancy has the potential to substantially enhance tumor treatmentstrategies for HCC patients. In this work we present a novel framework toautomatically characterize the malignancy of HCC lesions from DWI images. Wepredict HCC malignancy in two steps: As a first step we automatically segmentHCC tumor lesions using cascaded fully convolutional neural networks (CFCN). A3D neural network (SurvivalNet) then predicts the HCC lesions' malignancy fromthe HCC tumor segmentation. We formulate this task as a classification problemwith classes being "low risk" and "high risk" represented by longer or shortersurvival times than the median survival. We evaluated our method on DWI of 31HCC patients. Our proposed framework achieves an end-to-end accuracy of 65%with a Dice score for the automatic lesion segmentation of 69% and an accuracyof 68% for tumor malignancy classification based on expert annotations. Wecompared the SurvivalNet to classical handcrafted features such as Histogramand Haralick and show experimentally that SurvivalNet outperforms thehandcrafted features in HCC malignancy classification. End-to-end assessment oftumor malignancy based on our proposed fully automatic framework corresponds toassessment based on expert annotations with high significance (p>0.95).

SurvivalNet: Predicting patient survival from diffusion weighted  magnetic resonance images using cascaded fully convolutional and 3D  convolutional neural networks