Radiogenomics for Glioblastoma Survival Prediction: Integrating Radiomics, Clinical, and Genomic Features Using Artificial Intelligence.
Authors
Affiliations (3)
Affiliations (3)
- Department of Computer Engineering and Mathematics, Universitat Rovira I Virgili, Tarragona, Spain. [email protected].
- Hawkes Institute, Department of Computer Science, University College London, London, UK.
- Department of Computer Engineering and Mathematics, Universitat Rovira I Virgili, Tarragona, Spain.
Abstract
Glioblastoma (GBM) remains one of the most formidable brain malignancies, characterized by a heterogeneous genetic profile that significantly influences patient prognosis. Per the 2021 WHO central nervous system classification, GBM is defined as an isocitrate dehydrogenase (IDH) wild-type diffuse astrocytic tumor. We analyzed two multi-institutional cohorts, UPENN-GBM (644 patients) and UCSF-PDGM (420 patients); after excluding the 116 and 42 IDH-mutant records, 528 and 378 wild-type cases remained for modelling. MGMT promoter methylation, present in 43% of GBM cases, correlates with enhanced survival outcomes, demonstrating a median survival of 504 days versus 329 days in unmethylated cases. In this study, we present a novel integration of imaging phenotypes, clinical characteristics, and molecular markers through the application of advanced machine learning methodologies, including Random Forest, XGBoost, LightGBM, and an optimized dense neural network (Dense NN). This integrative approach aims to refine survival prediction in GBM patients. MRI data were meticulously processed using the MRIPreprocessor tool and the radiomics Python library, facilitating the extraction of high-dimensional radiomic features. Our findings reveal that the proposed custom Dense NN model outperformed traditional tree-based algorithms, with the Dense NN achieving a concordance index (CI) of 0.86 on the UPENN-GBM dataset and 0.83 on the UCSF-PDGM dataset. The optimized Dense NN architecture features three hidden layers with 256, 128, and 64 units respectively, employing ReLU activation, L1/L2 regularization to mitigate overfitting, batch normalization to stabilize training, and dropout for improved generalization. This specific configuration was determined through hyperparameter tuning using techniques like RandomizedSearchCV. This integrative, non-invasive methodology provides a more nuanced assessment of tumor biology, thereby advancing the development of personalized therapeutic strategies. Our results underscore the transformative potential of artificial intelligence in delineating disease trajectories and optimizing treatment paradigms. Moreover, this research establishes a robust framework for future investigations in glioblastoma survival prediction, illustrating the efficacy of combining clinical, genetic, and imaging data to enhance prognostic accuracy within precision medicine paradigms for GBM patients.