  In this paper we propose a method for predicting the status of MGMT promoter
methylation in high-grade gliomas. From the available MR images, we segment the
tumor using deep convolutional neural networks and extract both radiomic
features and shape features learned by a variational autoencoder. We
implemented a standard machine learning workflow to obtain predictions,
consisting of feature selection followed by training of a random forest
classification model. We trained and evaluated our method on the
RSNA-ASNR-MICCAI BraTS 2021 challenge dataset and submitted our predictions to
the challenge.
