  The number of studies on deep learning for medical diagnosis is expanding,
and these systems are often claimed to outperform clinicians. However, only a
few systems have shown medical efficacy. From this perspective, we examine a
wide range of deep learning algorithms for the assessment of glioblastoma - a
common brain tumor in older adults that is lethal. Surgery, chemotherapy, and
radiation are the standard treatments for glioblastoma patients. The
methylation status of the MGMT promoter, a specific genetic sequence found in
the tumor, affects chemotherapy's effectiveness. MGMT promoter methylation
improves chemotherapy response and survival in several cancers. MGMT promoter
methylation is determined by a tumor tissue biopsy, which is then genetically
tested. This lengthy and invasive procedure increases the risk of infection and
other complications. Thus, researchers have used deep learning models to
examine the tumor from brain MRI scans to determine the MGMT promoter's
methylation state. We employ deep learning models and one of the largest public
MRI datasets of 585 participants to predict the methylation status of the MGMT
promoter in glioblastoma tumors using MRI scans. We test these models using
Grad-CAM, occlusion sensitivity, feature visualizations, and training loss
landscapes. Our results show no correlation between these two, indicating that
external cohort data should be used to verify these models' performance to
assure the accuracy and reliability of deep learning systems in cancer
diagnosis.

Image and Video Processing;Computer Vision and Pattern Recognition;Machine Learning