Keywords: Radiogenomics, Glioblastoma, MGMT promoter, Deep learning
TL;DR: A validation study on the possibility of using deep learning methods on MRI scans to predict the status of MGMT promoter.
Abstract: 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 model's performance, we concluded that there seems to be no connection between the MRI scans and the state of the MGMT promoter.
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Paper Type: validation/application paper
Primary Subject Area: Validation Study
Secondary Subject Area: Interpretability and Explainable AI
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Code And Data: Link for the data: https://www.kaggle.com/c/rsna-miccai-brain-tumor-radiogenomic-classification/data The code will be uploaded soon.