  This paper proposes to use deep radiomic features (DRFs) from a convolutional
neural network (CNN) to model fine-grained texture signatures in the radiomic
analysis of recurrent glioblastoma (rGBM). We use DRFs to predict survival of
rGBM patients with preoperative T1-weighted post-contrast MR images (n=100).
DRFs are extracted from regions of interest labelled by a radiation oncologist
and used to compare between short-term and long-term survival patient groups.
Random forest (RF) classification is employed to predict survival outcome
(i.e., short or long survival), as well as to identify highly group-informative
descriptors. Classification using DRFs results in an area under the ROC curve
(AUC) of 89.15% (p<0.01) in predicting rGBM patient survival, compared to
78.07% (p<0.01) when using standard radiomic features (SRF). These results
indicate the potential of DRFs as a prognostic marker for patients with rGBM.
