Radiomics and Deep Learning Interplay for Predicting MGMT Methylation in Glioblastoma: The Crucial Role of Segmentation Quality

Background/Objectives: Glioblastoma (GBM) is the most malignant subtype of glioma and shows the poorest prognosis with a median survival time of 15 months. The methylation status of the Methylguanine-DNA Methyltransferase (MGMT) was proven to be a crucial factor in selecting the most appropriate therapy. Currently, it is assessed through brain biopsy, which is a highly invasive and very expensive technique. For these reasons, in recent years, the possibility of inferring this information from multi-parametric Magnetic Resonance Imaging (mpMRI) has been widely explored. However, substantial differences in performance are reported in the literature. Methods: In this study, we developed several models based on either radiomic or deep learning approaches and a mixture of them using mpMRI for the MGMT status assessment using the public dataset UPENN-GBM, available on The Cancer Imaging Archive. Despite the tests performed using all MRI acquisitions and different methodological approaches, we did not obtain sufficiently reliable performance to direct the therapeutic path of patients. We thus investigated the impact of segmentation quality on MGMT status prediction since the UPENN-GBM dataset contains both automatic and manual refined segmentation masks. Results: We found that performance obtained through radiomic features computed on manually segmented tumors was significantly higher compared to that obtained using automatic segmentation, even when the differences between segmentation masks, measured in terms of Dice Similarity Coefficient (DSC), is not significantly different. Conclusion: This could be the reason why very different MGMT classification performance is typically reported and suggests the creation of a benchmark dataset, with high-quality segmentation masks.