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

arXiv: https://arxiv.org/abs/2109.12339 · HTML: https://mlsanity.com/paper/2109.12339/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 31 references that are in the map.
- First posted on arXiv 25 Sep 2021.

## Builds on (most cited first, 7 of 7)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classification](https://mlsanity.com/paper/2107.02314/) (2021)
- [Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge](https://mlsanity.com/paper/1811.02629/) (2018)
- [nnU-Net for Brain Tumor Segmentation](https://mlsanity.com/paper/2011.00848/) (2020)
- [Learning to Generate Chairs, Tables and Cars with Convolutional Networks](https://mlsanity.com/paper/1411.5928/) (2014)
- [Machine learning and glioma imaging biomarkers](https://mlsanity.com/paper/1910.07440/) (2019)

## Built on by (most cited first, 3 of 3)

- [Optimizing Prediction of MGMT Promoter Methylation from MRI Scans using Adversarial Learning](https://mlsanity.com/paper/2201.04416/) (2022)
- [Unmasking unlearnable models: a classification challenge for biomedical images without visible cues](https://mlsanity.com/paper/2407.19773/) (2024)
- [Multi-View MRI Approach for Classification of MGMT Methylation in Glioblastoma Patients](https://mlsanity.com/paper/2512.14232/) (2025)

## Abstract (arXiv)

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.