  Imaging biomarkers offer a non-invasive way to predict the response of
immunotherapy prior to treatment. In this work, we propose a novel type of deep
radiomic features (DRFs) computed from a convolutional neural network (CNN),
which capture tumor characteristics related to immune cell markers and overall
survival. Our study uses four MRI sequences (T1-weighted, T1-weighted
post-contrast, T2-weighted and FLAIR) with corresponding immune cell markers of
151 patients with brain tumor. The proposed method extracts a total of 180 DRFs
by aggregating the activation maps of a pre-trained 3D-CNN within labeled tumor
regions of MRI scans. These features offer a compact, yet powerful
representation of regional texture encoding tissue heterogeneity. A
comprehensive set of experiments is performed to assess the relationship
between the proposed DRFs and immune cell markers, and measure their
association with overall survival. Results show a high correlation between DRFs
and various markers, as well as significant differences between patients
grouped based on these markers. Moreover, combining DRFs, clinical features and
immune cell markers as input to a random forest classifier helps discriminate
between short and long survival outcomes, with AUC of 72\% and
p=2.36$\times$10$^{-5}$. These results demonstrate the usefulness of proposed
DRFs as non-invasive biomarker for predicting treatment response in patients
with brain tumors.
