Ncog-31. Development and Evaluation of Prognostic Models to Predict Survival of Patients With Glioblastoma Multiforme

Abstract

Glioblastoma multiforme (GBM), the most common form of glioma, is a malignant tumor with a high risk of mortality. By providing accurate survival estimates, prognostic models have been identified as promising tools in clinical decision support. In this study, we produced and validated two machine learning-based models to predict survival time for GBM patients. Publicly available clinical and genomic data from The Cancer Genome Atlas (TCGA) and Broad Institute GDAC Firehouse were obtained through cBioPortal. Random forest and multivariate regression models were created to predict survival. Predictive accuracy was assessed and compared through mean absolute error (MAE) and root mean square error (RMSE) calculations. 619 GBM patients were included in the dataset. There were 381 (62.9%) cases of recurrence/progression and 53 (8.7%) cases of disease-free survival. The MAE and RMSE values were 0.553 and 0.887 years respectively for the random forest regression model, and they were 1.756 and 2.451 years respectively for the multivariate regression model. Both models accurately predicted overall survival. Comparison of models through MAE, RMSE, and visual analysis produced higher accuracy values for random forest than multivariate linear regression. Further investigation on feature selection and model optimization may improve predictive power. These findings suggest that using machine learning in GBM prognostic modeling will improve clinical decision support. *Co-first authors.