Noninvasive MGMT-promotor methylation prediction in high grade gliomas using conventional MRI and deep learning-based segmentations.

Background/objectives: High grade gliomas (HGG) are aggressive brain tumors, most frequently glioblastoma and astrocytoma grade 4. Methylation of O6-methylguanine-DNA methyltransferase (MGMT) promoter in HGG is crucial for temozolomide efficacy. As MGMT promoter methylation (MGMTpm) assessment requires tumor tissue, magnetic resonance imaging (MRI) is of interest for non-invasive prediction. We aimed to analyze volumetric data from edema, contrast-enhancing tumor, necrosis, total-tumor and total-tumor/edema ratio for MGMTpm prediction in HGG. Further we assessed overall survival (OS) and progression free survival (PFS) between groups and volumes. Methods: Segmentation was performed using deep learning models (DL-models), DeepBraTumIA and Raidionics, on 70 HGG patients (45 males, 32 MGMTpm). Manual segmentation was conducted in 37 for validation of DL-models. Group differences were evaluated using Man-Whitney U tests and receiver operation characteristic (ROC) curves. Multivariate analysis was conducted using logistic regression and bootstrapping. Dice coefficient, intraclass correlation coefficient (ICC) and Kruskal-Wallis test evaluated DL-model performance. Results: MGMTpm tumors displayed significantly larger edema, segmented by DeepBraTumIA ( Conclusion: This study suggests that significant radiological differences in MGMTpm can be found using deep learning models, primarily in tumor edema volume. MGMTpm status and region of interest volumes impact OS and PFS. Future studies should incorporate other molecular imaging sequences for methylation prediction.