Is it Possible to Predict MGMT Promoter Methylation from Brain Tumor MRI Scans using Deep Learning Models?
This addresses the need for non-invasive methods to predict chemotherapy effectiveness in glioblastoma patients, but the findings are incremental as they confirm negative results suggested by prior work.
The study investigated whether deep learning models could predict MGMT promoter methylation status from brain tumor MRI scans, concluding that no significant connection was found despite using extensive datasets and various models.
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.