Predicting overall survival in glioblastoma patients using machine learning: an analysis of treatment efficacy and patient prognosis
IntroductionGlioblastoma (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.Me...
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| Language: | English |
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Frontiers Media S.A.
2025-04-01
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| Series: | Frontiers in Oncology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fonc.2025.1539845/full |
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| author | Razvan Onciul Razvan Onciul Felix-Mircea Brehar Felix-Mircea Brehar Adrian Vasile Dumitru Adrian Vasile Dumitru Carla Crivoi Razvan-Adrian Covache-Busuioc Razvan-Adrian Covache-Busuioc Matei Serban Matei Serban Petrinel Mugurel Radoi Petrinel Mugurel Radoi Corneliu Toader Corneliu Toader |
| author_facet | Razvan Onciul Razvan Onciul Felix-Mircea Brehar Felix-Mircea Brehar Adrian Vasile Dumitru Adrian Vasile Dumitru Carla Crivoi Razvan-Adrian Covache-Busuioc Razvan-Adrian Covache-Busuioc Matei Serban Matei Serban Petrinel Mugurel Radoi Petrinel Mugurel Radoi Corneliu Toader Corneliu Toader |
| author_sort | Razvan Onciul |
| collection | DOAJ |
| description | IntroductionGlioblastoma (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.MethodsThis 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.ResultsXGBoost 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.ConclusionsThe 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. |
| format | Article |
| id | doaj-art-b1932c2fa26f4b5b8f0fade376d99ed4 |
| institution | DOAJ |
| issn | 2234-943X |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Oncology |
| spelling | doaj-art-b1932c2fa26f4b5b8f0fade376d99ed42025-08-20T03:17:57ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2025-04-011510.3389/fonc.2025.15398451539845Predicting overall survival in glioblastoma patients using machine learning: an analysis of treatment efficacy and patient prognosisRazvan Onciul0Razvan Onciul1Felix-Mircea Brehar2Felix-Mircea Brehar3Adrian Vasile Dumitru4Adrian Vasile Dumitru5Carla Crivoi6Razvan-Adrian Covache-Busuioc7Razvan-Adrian Covache-Busuioc8Matei Serban9Matei Serban10Petrinel Mugurel Radoi11Petrinel Mugurel Radoi12Corneliu Toader13Corneliu Toader14Department of Neurosurgery, “Carol Davila” University of Medicine and Pharmacy, Bucharest, RomaniaNeurosurgery Department, Emergency University Hospital, Bucharest, RomaniaDepartment of Neurosurgery, “Carol Davila” University of Medicine and Pharmacy, Bucharest, RomaniaDepartment of Neurosurgery, Clinical Emergency Hospital “Bagdasar-Arseni”, Bucharest, RomaniaDepartment of Neurosurgery, “Carol Davila” University of Medicine and Pharmacy, Bucharest, RomaniaDepartment of Pathology, University Emergency Hospital Bucharest, Carol Davila University of Medicine and Pharmacy, Bucharest, RomaniaDepartment of Computer Science, Faculty of Mathematics and Computer Science, University of Bucharest, Bucharest, RomaniaDepartment of Neurosurgery, “Carol Davila” University of Medicine and Pharmacy, Bucharest, RomaniaDepartment of Vascular Neurosurgery, National Institute of Neurology and Neurovascular Diseases, Bucharest, RomaniaDepartment of Neurosurgery, “Carol Davila” University of Medicine and Pharmacy, Bucharest, RomaniaDepartment of Vascular Neurosurgery, National Institute of Neurology and Neurovascular Diseases, Bucharest, RomaniaDepartment of Neurosurgery, “Carol Davila” University of Medicine and Pharmacy, Bucharest, RomaniaDepartment of Vascular Neurosurgery, National Institute of Neurology and Neurovascular Diseases, Bucharest, RomaniaDepartment of Neurosurgery, “Carol Davila” University of Medicine and Pharmacy, Bucharest, RomaniaDepartment of Vascular Neurosurgery, National Institute of Neurology and Neurovascular Diseases, Bucharest, RomaniaIntroductionGlioblastoma (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.MethodsThis 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.ResultsXGBoost 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.ConclusionsThe 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.https://www.frontiersin.org/articles/10.3389/fonc.2025.1539845/fullmachine learningprognostic biomarkersexplainable AIsurvival predictionclinical decision supportpersonalized medicine |
| spellingShingle | Razvan Onciul Razvan Onciul Felix-Mircea Brehar Felix-Mircea Brehar Adrian Vasile Dumitru Adrian Vasile Dumitru Carla Crivoi Razvan-Adrian Covache-Busuioc Razvan-Adrian Covache-Busuioc Matei Serban Matei Serban Petrinel Mugurel Radoi Petrinel Mugurel Radoi Corneliu Toader Corneliu Toader Predicting overall survival in glioblastoma patients using machine learning: an analysis of treatment efficacy and patient prognosis Frontiers in Oncology machine learning prognostic biomarkers explainable AI survival prediction clinical decision support personalized medicine |
| title | Predicting overall survival in glioblastoma patients using machine learning: an analysis of treatment efficacy and patient prognosis |
| title_full | Predicting overall survival in glioblastoma patients using machine learning: an analysis of treatment efficacy and patient prognosis |
| title_fullStr | Predicting overall survival in glioblastoma patients using machine learning: an analysis of treatment efficacy and patient prognosis |
| title_full_unstemmed | Predicting overall survival in glioblastoma patients using machine learning: an analysis of treatment efficacy and patient prognosis |
| title_short | Predicting overall survival in glioblastoma patients using machine learning: an analysis of treatment efficacy and patient prognosis |
| title_sort | predicting overall survival in glioblastoma patients using machine learning an analysis of treatment efficacy and patient prognosis |
| topic | machine learning prognostic biomarkers explainable AI survival prediction clinical decision support personalized medicine |
| url | https://www.frontiersin.org/articles/10.3389/fonc.2025.1539845/full |
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