Deep radiomic features from MRI scans predict survival outcome of recurrent glioblastoma

Deep radiomic features from MRI scans predict survival outcome of recurrent glioblastoma

Chaddad, Ahmad and Zhang, Mingli and Desrosiers, Christian and Niazi, Tamim

arXiv 2019

Abstract : 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\textless0.01) in predicting rGBM patient survival, compared to 78.07% (p\textless0.01) when using standard radiomic features (SRF). These results indicate the potential of DRFs as a prognostic marker for patients with rGBM.