mlsanityData to 13 Oct 2026

Is it Possible to Predict MGMT Promoter Methylation from Brain Tumor MRI Scans using Deep Learning Models?

arXiv 2201.06086 ↗First posted 16 Jan 2022eess.IV cs.CVcited by 3

Not on the map. Why?

Abstract (arXiv)

Glioblastoma is a common brain malignancy that tends to occur in older adults and is almost always lethal. The effectiveness of chemotherapy, being the standard treatment for most cancer types, can be improved if a particular genetic sequence in the tumor known as MGMT promoter is methylated. However, to identify the state of the MGMT promoter, the conventional approach is to perform a biopsy for genetic analysis, which is time and effort consuming. A couple of recent publications proposed a connection between the MGMT promoter state and the MRI scans of the tumor and hence suggested the use of deep learning models for this purpose. Therefore, in this work, we use one of the most extensive datasets, BraTS 2021, to study the potency of employing deep learning solutions, including 2D and 3D CNN models and vision transformers. After conducting a thorough analysis of the models' performance, we concluded that there seems to be no connection between the MRI scans and the state of the MGMT promoter.

Key facts. Cited by 3 papers in the map (data through 13 Oct 2026). Builds on 6 of its 24 references that are in the map. First posted on arXiv 16 Jan 2022.

Builds on

6 of 24 references.

References · 6

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,950
  2. 2020A Simple Framework for Contrastive Learning of Visual Representationscited by 12,009
  3. 2016Densely Connected Convolutional Networkscited by 6,707
  4. 2019Optuna: A Next-generation Hyperparameter Optimization Frameworkcited by 2,472
  5. 2021The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classificationcited by 514
  6. 2021Big Self-Supervised Models Advance Medical Image Classificationcited by 320

Built on by

Cited by year

Citing papers per year: 2022: 1; 2023: 1; 2024: 1.

3 papers.