Abstract: Glioblastoma Multiforme is a very aggressive type of brain tumor. Due to
spatial and temporal intra-tissue inhomogeneity, location and the extent of the
cancer tissue, it is difficult to detect and dissect the tumor regions. In this
paper, we propose survival prognosis models using four regressors operating on
handcrafted image-based and radiomics features. We hypothesize that the
radiomics shape features have the highest correlation with survival prediction.
The proposed approaches were assessed on the Brain Tumor Segmentation
(BraTS-2020) challenge dataset. The highest accuracy of image features with
random forest regressor approach was 51.5\% for the training and 51.7\% for the
validation dataset. The gradient boosting regressor with shape features gave an
accuracy of 91.5\% and 62.1\% on training and validation datasets respectively.
It is better than the BraTS 2020 survival prediction challenge winners on the
training and validation datasets. Our work shows that handcrafted features
exhibit a strong correlation with survival prediction. The consensus based
regressor with gradient boosting and radiomics shape features is the best
combination for survival prediction.