  Prediction of Overall Survival (OS) of brain cancer patients from multi-modal
MRI is a challenging field of research. Most of the existing literature on
survival prediction is based on Radiomic features, which does not consider
either non-biological factors or the functional neurological status of the
patient(s). Besides, the selection of an appropriate cut-off for survival and
the presence of censored data create further problems. Application of deep
learning models for OS prediction is also limited due to the lack of large
annotated publicly available datasets. In this scenario we analyse the
potential of two novel neuroimaging feature families, extracted from brain
parcellation atlases and spatial habitats, along with classical radiomic and
geometric features; to study their combined predictive power for analysing
overall survival. A cross validation strategy with grid search is proposed to
simultaneously select and evaluate the most predictive feature subset based on
its predictive power. A Cox Proportional Hazard (CoxPH) model is employed for
univariate feature selection, followed by the prediction of patient-specific
survival functions by three multivariate parsimonious models viz. Coxnet,
Random survival forests (RSF) and Survival SVM (SSVM). The brain cancer MRI
data used for this research was taken from two open-access collections TCGA-GBM
and TCGA-LGG available from The Cancer Imaging Archive (TCIA). Corresponding
survival data for each patient was downloaded from The Cancer Genome Atlas
(TCGA). A high cross validation $C-index$ score of $0.82\pm.10$ was achieved
using RSF with the best $24$ selected features. Age was found to be the most
important biological predictor. There were $9$, $6$, $6$ and $2$ features
selected from the parcellation, habitat, radiomic and region-based feature
groups respectively.
