biostudies-literatureDuman AMEB3351https://www.ebi.ac.uk/biostudies/studies/S-EPMC11476262biostudies-literatureUnknown16(19)<h4>Purpose</h4>To develop and validate an MRI-based radiomic model for predicting overall survival (OS) in patients diagnosed with glioblastoma multiforme (GBM), utilizing a retrospective dataset from multiple institutions.<h4>Materials and methods</h4>Pre-treatment MRI images of 289 GBM patients were collected. From each patient's tumor volume, 660 radiomic features (RFs) were extracted and subjected to robustness analysis. The initial prognostic model with minimum RFs was subsequently enhanced by including clinical variables. The final clinical-radiomic model was derived through repeated three-fold cross-validation on the training dataset. Performance evaluation included assessment of concordance index (C-Index), integrated area under curve (iAUC) alongside patient stratification into lCancersReproducible and Interpretable Machine Learning-Based Radiomic Analysis for Overall Survival Prediction in Glioblastoma Multiforme.PMC11476262MEB-2023-16Spezi EThomas SDuman ASun XPowell JRfalseReproducible and Interpretable Machine Learning-Based Radiomic Analysis for Overall Survival Prediction in Glioblastoma Multiforme.<h4>Purpose</h4>To develop and validate an MRI-based radiomic model for predicting overall survival (OS) in patients diagnosed with glioblastoma multiforme (GBM), utilizing a retrospective dataset from multiple institutions.<h4>Materials and methods</h4>Pre-treatment MRI images of 289 GBM patients were collected. From each patient's tumor volume, 660 radiomic features (RFs) were extracted and subjected to robustness analysis. The initial prognostic model with minimum RFs was subsequently enhanced by including clinical variables. The final clinical-radiomic model was derived through repeated three-fold cross-validation on the training dataset. Performance evaluation included assessment of concordance index (C-Index), integrated area under curve (iAUC) alongside patient stratification into l2024-01-01T00:00:00Z2024 Sep2025-04-04T01:17:14.197Z2025-04-04T01:17:14.197ZS-EPMC114762623940997010.3390/cancers16193351