| Abstrakt: |
Simple Summary: Glioblastoma is the most aggressive type of brain cancer, with patients typically surviving less than 15 months after diagnosis. Accurate prediction of survival time is crucial for tailoring treatment plans to individual patients. This study develops a new computer-based method that analyzes brain scans to forecast survival in glioblastoma patients. By examining detailed features from magnetic resonance imaging (MRI) scans, our approach aims to provide more precise and personalized survival estimates than current methods. We tested our model on a large group of patients from two different hospitals, demonstrating its ability to predict survival accurately at various time points. If validated in future studies, this tool could help doctors make more informed decisions about patient care, potentially improving outcomes for those diagnosed with this challenging disease. (1) Background: Glioblastoma (GBM) is the most common primary malignant brain tumor in adults, with an aggressive disease course that requires accurate prognosis for individualized treatment planning. This study aims to develop and evaluate a radiomics-based machine learning (ML) model to estimate overall survival (OS) for patients with GBM using pre-treatment multi-parametric magnetic resonance imaging (MRI). (2) Methods: The MRI data of 865 patients with GBM were assessed, comprising 499 patients from the UPENN-GBM dataset and 366 patients from the UCSF-PDGM dataset. A total of 14,598 radiomic features were extracted from T1, T1 with contrast, T2, and FLAIR MRI sequences using PyRadiomics. The UPENN-GBM dataset was used for model development (70%) and internal validation (30%), while the UCSF-PDGM dataset served as an external test set. The NGBoost Survival model was developed to generate continuous probability estimates as well as predictions for 6-, 12-, 18-, and 24-month OS. (3) Results: The NGBoost Survival model successfully predicted survival, achieving a C-index of 0.801 on internal validation and 0.725 on external validation. For 6-month OS, the model attained an AUROC of 0.791 (95% CI: 0.742–0.832) and 0.708 (95% CI: 0.654–0.748) for internal and external validation, respectively. (4) Conclusions: The radiomics-based ML model demonstrates potential to improve the prediction of OS for patients with GBM. [ABSTRACT FROM AUTHOR] |
