Predicting Overall Survival in Glioblastoma Patients Using Machine Learning: An Analysis of Treatment Efficacy and Patient Prognosis

Razvan Onciul ^1,2 Felix Mircea Brehar ^1,3* Adrian Vasile Dumitru ^1,2* Carla Crivoi ⁴ Razvan-Adrian Covache-Busuioc ^1,5 Matei Serban ^1,5 Petrinel Mugurel Radoi ^1,5 Corneliu Toader ^1,5

• ¹ Faculty of Medicine, Carol Davila University of Medicine and Pharmacy, Bucharest, Romania
• ² Bucharest University Emergency Hospital, Bucharest, Romania
• ³ Department of Neurosurgery, Emergency Clinical Hospital Bagdasar Arseni, Bucharest, Romania
• ⁴ Faculty of Mathematics and Computer Science, University of Bucharest, Bucharest, Romania
• ⁵ National Institute of Neurology and Neurovascular Diseases Bucharest, Bucharest, Romania

Glioblastoma (GBM), the most aggressive primary brain tumor, poses a significant challenge in predicting patient survival due to its heterogeneity and resistance to treatment. Accurate survival prediction is essential for optimizing treatment strategies and improving clinical outcomes. Methods: This study utilized metadata from 135 GBM patients, including demographic, clinical, and molecular variables such as age, Karnofsky Performance Status (KPS), MGMT promoter methylation, and EGFR amplification. Six machine learning models-XGBoost, Random Forests, Support Vector Machines, Artificial Neural Networks, Extra Trees Regressor, and K-Nearest Neighbors-were employed to classify patients into predefined survival categories. Data preprocessing included label encoding for categorical variables and MinMax scaling for numerical features. Model performance was assessed using ROC-AUC and accuracy metrics, with hyperparameters optimized through grid search. Results: XGBoost demonstrated the highest predictive accuracy, achieving a mean ROC-AUC of 0.90 and an accuracy of 0.78. Ensemble models outperformed simpler classifiers, emphasizing the predictive value of metadata. The models identified key prognostic markers, including MGMT promoter methylation and KPS, as significant contributors to survival prediction. Conclusions: The application of machine learning to GBM metadata offers a robust approach to predicting patient survival. The study highlights the potential of ML models to enhance clinical decision-making and contribute to personalized treatment strategies, with a focus on accuracy, reliability, and interpretability.