  Surgery planning in patients diagnosed with brain tumor is dependent on their
survival prognosis. A poor prognosis might demand for a more aggressive
treatment and therapy plan, while a favorable prognosis might enable a less
risky surgery plan. Thus, accurate survival prognosis is an important step in
treatment planning. Recently, deep learning approaches have been used
extensively for brain tumor segmentation followed by the use of deep features
for prognosis. However, radiomics-based studies have shown more promise using
engineered/hand-crafted features. In this paper, we propose a three-step
approach for multi-class survival prognosis. In the first stage, we extract
image slices corresponding to tumor regions from multiple magnetic resonance
image modalities. We then extract radiomic features from these 2D slices.
Finally, we train machine learning classifiers to perform the classification.
We evaluate our proposed approach on the publicly available BraTS 2019 data and
achieve an accuracy of 76.5% and precision of 74.3% using the random forest
classifier, which to the best of our knowledge are the highest reported results
yet. Further, we identify the most important features that contribute in
improving the prediction.
