Modeling of Textures to Predict Immune Cell Status and Survival of Brain Tumour Patients

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 Ik!per), 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, KaplanMeier 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 x10(-7) compared to p = 0.03 for Immune cell markers, p = 0.07 for clinical variables, and p =1.45 x10(-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.