Enhancing Glioblastoma Survival Forecasting via Ensemble Learning of Clinical and Genomic Features

Abstract

Brain cancer is the uncontrolled growth of malignant cells, and the most malignant primary brain tumor is the gliomas. Glioblastoma grade IV gliomas represents the most malignant histologic grade and accounts for approximately <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{5 4 \%}$</tex> of all gliomas. GBM diagnosis requires a multidisciplinary approach involving clinical evaluation and neuroimaging with histopathology and molecular profiling. Due to the aggressiveness and heterogeneity of GBM, accurate survival prediction continues to be challenging. We constructed a stacked ensemble learning model comprising Ridge Regression, Lasso Regression, XGBoost, and RandomForestRegressor in this work and used it on survival outcome classification in glioblastoma. The model achieved a Concordance Index (<tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{C}$</tex>-index) of 0.73 was good for ranking patients according to survival time. By integrating linear models that can handle well with multicollinearity and selection of features and tree models that capture non-linear relation and interaction, the ensemble uses the complementary strengths of the models to generalize well and over-fit less. This performance is on par with the usual single model methods even with highly dimensional and highly complex clinical-genomic interaction involved in glioblastoma data. Clinically, an over 0.70 C-index implies the potential value of the model in stratifying the patient and predicting prognosis and supports individualized treatment planning and informed clinical decisions. Future work will include the inclusion of additional omics data, model hyperparameter optimization through cross-validation and validating the model on independent sets to confirm its robustness and translatability. Future work will focus on leveraging larger and more comprehensive datasets to identify genomic factors that significantly influence patient survivability, with the aim of enhancing the precision and clinical applicability of overall survival prediction.