Predicting MGMT Methylation in Glioblastoma for Informed Clinical Decisions: An AI-Driven Approach in Resource-Limited Settings

Abstract

Background Glioblastoma is an aggressive brain cancer with a poor prognosis. MGMT (O6-methylguanine-DNA methyltransferase) gene methylation status is crucial for treatment stratification, yet economic constraints often limit access. This study aims to develop an artificial intelligence (AI) framework for predicting MGMT methylation status. Methods Machine learning (ML) and deep learning (DL) techniques were applied to diagnostic MR images from the NIH and a private institution. The images were segmented according to ESTRO-ACROP 2016 guidelines for radiotherapy treatment volumes and combined, with clinical evaluations from neuroradiology experts. Radiomic features (quantitative) and clinical impressions (qualitative) were extracted for ML models. Feature selection methods were used to identify relevant phenotypes for training and validation with ML classifiers. Results We evaluated 100 patients from the NIH and 46 patients from a local institution. A total of 343 features were extracted. Eight feature selection methods produced seven independent predictive frameworks. The top-performing ML models included Recursive Feature Elimination (RFE) combined with Linear Discriminant Analysis (LDA) (accuracy of 0.75). DL performance achieved an accuracy of 0.74 using convolutional networks. Conclusion This study demonstrates that integrating clinical and radiotherapy-derived AI-driven phenotypes can accurately predict MGMT methylation. The framework also addresses constraints that limit molecular diagnosis access.