  Radiomics has shown a capability for different types of cancers such as
glioma to predict the clinical outcome. It can have a non-invasive means of
evaluating the immunotherapy response prior to treatment. However, the use of
deep convolutional neural networks (CNNs)-based radiomics requires large
training image sets. To avoid this problem, we investigate a new imaging
features that model distribution with a Gaussian mixture model (GMM) of learned
3D CNN features. Using these deep radiomic features (DRFs), we aim to predict
the immune marker status (low versus high) and overall survival for glioma
patients. We extract the DRFs by aggregating the activation maps of a
pre-trained 3D-CNN within labeled tumor regions of MRI scans that corresponded
immune markers of 151 patients. Our experiments are performed to assess the
relationship between the proposed DRFs, three immune cell markers (Macrophage
M1, Neutrophils and T Cells Follicular Helper), and measure their association
with overall survival. Using the random forest (RF) model, DRFs was able to
predict the immune marker status with area under the ROC curve (AUC) of 78.67,
83.93 and 75.67\% for Macrophage M1, Neutrophils and T Cells Follicular Helper,
respectively. Combined the immune markers with DRFs and clinical variables,
Kaplan-Meier estimator and Log-rank test achieved the most significant
difference between predicted groups of patients (short-term versus long-term
survival) with p\,=\,4.31$\times$10$^{-7}$ compared to p\,=\,0.03 for Immune
cell markers, p\,=\,0.07 for clinical variables , and
p\,=\,1.45$\times$10$^{-5}$ for DRFs. Our findings indicate that the proposed
features (DRFs) used in RF models may significantly consider prognosticating
patients with brain tumour prior to surgery through regularly acquired imaging
data.

Image and Video Processing;Artificial Intelligence;Computer Vision and Pattern Recognition;Genomics;Quantitative Methods;Methodology