We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Computational pathology-based weakly supervised prediction model for MGMT promoter methylation status in glioblastoma.
- Authors
Yongqi He; Ling Duan; Gehong Dong; Feng Chen; Wenbin Li
- Abstract
Introduction: The methylation status of oxygen 6-methylguanine-DNA methyltransferase (MGMT) is closely related to the treatment and prognosis of glioblastoma. However, there are currently some challenges in detecting the methylation status of MGMT promoters. The hematoxylin and eosin (H&E)- stained histopathological slides have always been the gold standard for tumor diagnosis. Methods: In this study, based on the TCGA database and H&E-stained Whole slide images (WSI) of Beijing Tiantan Hospital, we constructed a weakly supervised prediction model of MGMT promoter methylation status in glioblastoma by using two Transformer structure models. Results: The accuracy scores of this model in the TCGA dataset and our independent dataset were 0.79 (AUC= 0.86) and 0.76 (AUC= 0.83), respectively. Conclusion: The model demonstrates effective prediction of MGMT promoter methylation status in glioblastoma and exhibits some degree of generalization capability. At the same time, our study also shows that adding Patches automatic screening module to the computational pathology research framework of glioma can significantly improve the model effect.
- Subjects
O6-Methylguanine-DNA Methyltransferase; GLIOBLASTOMA multiforme; PREDICTION models; METHYLATION; HEMATOXYLIN &; eosin staining
- Publication
Frontiers in Neurology, 2024, p1
- ISSN
1664-2295
- Publication type
Article
- DOI
10.3389/fneur.2024.1345687