Brain Tumor Radiogenomic Classification
This addresses the challenge of non-invasive brain tumor biomarker prediction for medical imaging applications, but is incremental as it applies existing methods to a specific dataset.
The paper tackled the problem of predicting MGMT biomarker status in glioblastoma from multi-parameter MRI scans using binary classification, achieving AUC scores of 0.6015 with ViT3D and 0.61745 with Xception on the test set.
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.