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arXiv · 2109.12339

Prediction of MGMT Methylation Status of Glioblastoma using Radiomics and Latent Space Shape Features

Sveinn Pálsson·Stefano Cerri·Koen Van Leemput
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Abstract

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.

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Sveinn Pálsson, Stefano Cerri, Koen Van Leemput. 2021-09-25. Prediction of MGMT Methylation Status of Glioblastoma using Radiomics and Latent Space Shape Features. https://arxiv.org/abs/2109.12339

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