  We proposed a fully automatic workflow for glioblastoma (GBM) survival
prediction using deep learning (DL) methods. 285 glioma (210 GBM, 75 low-grade
glioma) patients were included. 163 of the GBM patients had overall survival
(OS) data. Every patient had four pre-operative MR scans and manually drawn
tumor contours. For automatic tumor segmentation, a 3D convolutional neural
network (CNN) was trained and validated using 122 glioma patients. The trained
model was applied to the remaining 163 GBM patients to generate tumor contours.
The handcrafted and DL-based radiomic features were extracted from
auto-contours using explicitly designed algorithms and a pre-trained CNN
respectively. 163 GBM patients were randomly split into training (n=122) and
testing (n=41) sets for survival analysis. Cox regression models with
regularization techniques were trained to construct the handcrafted and
DL-based signatures. The prognostic power of the two signatures was evaluated
and compared. The 3D CNN achieved an average Dice coefficient of 0.85 across
163 GBM patients for tumor segmentation. The handcrafted signature achieved a
C-index of 0.64 (95% CI: 0.55-0.73), while the DL-based signature achieved a
C-index of 0.67 (95% CI: 0.57-0.77). Unlike the handcrafted signature, the
DL-based signature successfully stratified testing patients into two
prognostically distinct groups (p-value<0.01, HR=2.80, 95% CI: 1.26-6.24). The
proposed 3D CNN generated accurate GBM tumor contours from four MR images. The
DL-based signature resulted in better GBM survival prediction, in terms of
higher C-index and significant patient stratification, than the handcrafted
signature. The proposed automatic radiomic workflow demonstrated the potential
of improving patient stratification and survival prediction in GBM patients.
