Abstract
Glioblastoma is a stage IV highly invasive astrocytoma tumor. Its heterogeneous appearance in MRI poses critical challenge in diagnosis, prognosis and survival prediction. This work extracts a total of 1207 different types of texture and other features, tests their significance and prognostic values, and then utilizes the most significant features with Random Forest regression model to perform survival prediction. We use 163 cases from BraTS17 training dataset for evaluation of the proposed model. A 10-fold cross validation offers normalized root mean square error of 30% for the training dataset and the cross validated accuracy of 63%, respectively.
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