Multi-View MRI Approach for Classification of MGMT Methylation in Glioblastoma Patients
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arXiv
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| Autores principales: | , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866911322445185024 |
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| author | Alyahya, Rawan Alruwayqi, Asrar Alqarni, Atheer Alkhaldi, Asma Alkubeyyer, Metab Gao, Xin Alshahrani, Mona |
| author_facet | Alyahya, Rawan Alruwayqi, Asrar Alqarni, Atheer Alkhaldi, Asma Alkubeyyer, Metab Gao, Xin Alshahrani, Mona |
| contents | The presence of MGMT promoter methylation significantly affects how well chemotherapy works for patients with Glioblastoma Multiforme (GBM). Currently, confirmation of MGMT promoter methylation relies on invasive brain tumor tissue biopsies. In this study, we explore radiogenomics techniques, a promising approach in precision medicine, to identify genetic markers from medical images. Using MRI scans and deep learning models, we propose a new multi-view approach that considers spatial relationships between MRI views to detect MGMT methylation status. Importantly, our method extracts information from all three views without using a complicated 3D deep learning model, avoiding issues associated with high parameter count, slow convergence, and substantial memory demands. We also introduce a new technique for tumor slice extraction and show its superiority over existing methods based on multiple evaluation metrics. By comparing our approach to state-of-the-art models, we demonstrate the efficacy of our method. Furthermore, we share a reproducible pipeline of published models, encouraging transparency and the development of robust diagnostic tools. Our study highlights the potential of non-invasive methods for identifying MGMT promoter methylation and contributes to advancing precision medicine in GBM treatment. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_14232 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Multi-View MRI Approach for Classification of MGMT Methylation in Glioblastoma Patients Alyahya, Rawan Alruwayqi, Asrar Alqarni, Atheer Alkhaldi, Asma Alkubeyyer, Metab Gao, Xin Alshahrani, Mona Computer Vision and Pattern Recognition The presence of MGMT promoter methylation significantly affects how well chemotherapy works for patients with Glioblastoma Multiforme (GBM). Currently, confirmation of MGMT promoter methylation relies on invasive brain tumor tissue biopsies. In this study, we explore radiogenomics techniques, a promising approach in precision medicine, to identify genetic markers from medical images. Using MRI scans and deep learning models, we propose a new multi-view approach that considers spatial relationships between MRI views to detect MGMT methylation status. Importantly, our method extracts information from all three views without using a complicated 3D deep learning model, avoiding issues associated with high parameter count, slow convergence, and substantial memory demands. We also introduce a new technique for tumor slice extraction and show its superiority over existing methods based on multiple evaluation metrics. By comparing our approach to state-of-the-art models, we demonstrate the efficacy of our method. Furthermore, we share a reproducible pipeline of published models, encouraging transparency and the development of robust diagnostic tools. Our study highlights the potential of non-invasive methods for identifying MGMT promoter methylation and contributes to advancing precision medicine in GBM treatment. |
| title | Multi-View MRI Approach for Classification of MGMT Methylation in Glioblastoma Patients |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2512.14232 |