Prediction of MGMT Methylation Status of Glioblastoma using Radiomics and Latent Space Shape Features
This work addresses a specific medical imaging problem for glioblastoma diagnosis, but it is incremental as it combines existing methods without major breakthroughs.
The paper tackled predicting MGMT promoter methylation status in high-grade gliomas by segmenting tumors from MR images using deep convolutional neural networks and extracting radiomic and shape features from a variational autoencoder, achieving results evaluated on the RSNA-ASNR-MICCAI BraTS 2021 challenge dataset.
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.