Cross-scale Prediction of Glioblastoma MGMT Methylation Status Based on Deep Learning Combined with Magnetic Resonance Images and Pathology Images

Background The methylation status of the O6-methylguanine-DNA methyltransferase (MGMT) promoter is a key predictive biomarker for chemotherapy response in glioblastoma (GBM). Current reliance on complex molecular testing necessitates the development of alternative predictive methods for postoperative decision support during the waiting period. Although both preoperative MRI and histopathological images contain valuable biological information, their combined potential for predicting MGMT status remains unexplored. We aimed to develop and validate a deep learning radiopathomics model (DLRPM) that integrates MRI and pathological images for predicting MGMT promoter methylation. Methods A retrospective collection of pathologically confirmed isocitrate dehydrogenase (IDH) wild-type GBM patients (n=207) from three centers was performed, all of whom underwent MRI scanning within 2 weeks prior to surgery. The pre-trained ResNet50 was used as the feature extractor. Features of 1024 dimensions were extracted from MRI and pathological images, respectively, and the features were screened for modeling. Then feature fusion was performed by calculating the normalized multimode MRI fusion features and pathological features, and prediction models of MGMT based on deep learning radiomics, pathomics, and radiopathomics (DLRM, DLPM, DLRPM) were constructed and applied to internal and external validation cohorts. Results In the training, internal and external validation cohorts, the DLRPM further improved the predictive performance, with a significantly better predictive performance than the DLRM and DLPM, with AUCs of 0.920 (95% CI 0.870–0.968), 0.854 (95% CI 0.702–1.000), and 0.840 (95% CI 0.625–1.000) and the corresponding accuracy was 83.2%, 82.1% and 80.0%, respectively. The AUCs of DLRM were 0.786, 0.771, and 0.600 respectively, with accuracy rates of 67.3%, 67.9%, and 60.0% respectively. The AUCs of DLPM were 0.864, 0.844, and 0.780 respectively, with accuracy rates of 79.6%, 78.6%, and 66.7% respectively. Conclusion By integrating MRI and histopathological images, the DLRPM narrows the diagnostic gap created by the waiting period for molecular testing, offering a practical approach to predicting MGMT methylation status. The model’s robust performance across validation cohorts demonstrates its potential as a practical clinical tool to supplement or reduce reliance on invasive tissue sampling, thereby aiding in personalized treatment planning for GBM patients. Future studies will focus on prospective validation and explore its utility in predicting other molecular markers and treatment outcomes.