Brain Tumor Radiogenomic Classification

TL;DR

This study evaluates various deep learning models, including Vision Transformer and Xception, for predicting MGMT biomarker status in glioblastoma using multi-parametric MRI scans, achieving moderate accuracy.

Contribution

It compares multiple architectures on a radiogenomic classification challenge, highlighting the effectiveness of ViT3D and Xception models for this task.

Findings

ViT3D achieved an AUC of 0.6015

Xception achieved an AUC of 0.61745

Results validate the complexity of brain tumor radiogenomic classification

Abstract

The RSNA-MICCAI brain tumor radiogenomic classification challenge aimed to predict MGMT biomarker status in glioblastoma through binary classification on Multi parameter mpMRI scans: T1w, T1wCE, T2w and FLAIR. The dataset is splitted into three main cohorts: training set, validation set which were used during training, and the testing were only used during final evaluation. Images were either in a DICOM format or in Png format. different architectures were used to investigate the problem including the 3D version of Vision Transformer (ViT3D), ResNet50, Xception and EfficientNet-B3. AUC was used as the main evaluation metric and the results showed an advantage for both the ViT3D and the Xception models achieving 0.6015 and 0.61745 respectively on the testing set. compared to other results, our results proved to be valid given the complexity of the task. further improvements can be made through exploring different strategies, different architectures and more diverse datasets.