  The accurate prognosis of Glioblastoma Multiforme (GBM) plays an essential
role in planning correlated surgeries and treatments. The conventional models
of survival prediction rely on radiomic features using magnetic resonance
imaging (MRI). In this paper, we propose a radiogenomic overall survival (OS)
prediction approach by incorporating gene expression data with radiomic
features such as shape, geometry, and clinical information. We exploit TCGA
(The Cancer Genomic Atlas) dataset and synthesize the missing MRI modalities
using a fully convolutional network (FCN) in a conditional Generative
Adversarial Network (cGAN). Meanwhile, the same FCN architecture enables the
tumor segmentation from the available and the synthesized MRI modalities. The
proposed FCN architecture comprises octave convolution (OctConv) and a novel
decoder, with skip connections in spatial and channel squeeze & excitation
(skip-scSE) block. The OctConv can process low and high-frequency features
individually and improve model efficiency by reducing channel-wise redundancy.
Skip-scSE applies spatial and channel-wise excitation to signify the essential
features and reduces the sparsity in deeper layers learning parameters using
skip connections. The proposed approaches are evaluated by comparative
experiments with state-of-the-art models in synthesis, segmentation, and
overall survival (OS) prediction. We observe that adding missing MRI modality
improves the segmentation prediction, and expression levels of gene markers
have a high contribution in the GBM prognosis prediction, and fused
radiogenomic features boost the OS estimation.
