  Purpose: To develop a novel deep-learning model that integrates radiomics
analysis in a multi-dimensional feature fusion workflow for glioblastoma (GBM)
post-resection survival prediction. Methods: A cohort of 235 GBM patients with
complete surgical resection was divided into short-term/long-term survival
groups with 1-yr survival time threshold. Each patient received a pre-surgery
multi-parametric MRI exam, and three tumor subregions were segmented by
neuroradiologists. The developed model comprises three data source branches: in
the 1st radiomics branch, 456 radiomics features (RF) were from each patient;
in the 2nd deep learning branch, an encoding neural network architecture was
trained for survival group prediction using each single MR modality, and
high-dimensional parameters of the last two network layers were extracted as
deep features (DF). The extracted radiomics features and deep features were
processed by a feature selection procedure to reduce dimension size of each
feature space. In the 3rd branch, non-image-based patient-specific clinical
features (PSCF) were collected. Finally, data sources from all three branches
were fused as an integrated input for a supporting vector machine (SVM)
execution for survival group prediction. Different strategies of model design,
including 1) 2D/3D-based image analysis, and 2) different data source
combinations in SVM input design, were investigated in comparison studies.
Results: The model achieved 0.638 prediction accuracy when using PSCF only,
which was higher than the results using RF or DF only in both 2D and 3D
analysis. The joint use of RF/PSCF improved accuracy results to 0.681 in 3D
analysis. The most accurate models in 2D/3D analysis reached the highest
accuracy 0.745 with different combinations of RF/DF/ PSCF, and the
corresponding ROC AUC results were 0.69(2D) and 0.71(3D), respectively.
