Towards Radiomics-Based Automated Disease Progression Assessment for Glioblastoma Patients

Glioblastoma is a highly infiltrative brain tumor with fast progression and poor prognosis for patients. Due to the rapid growth, close treatment response monitoring is key. In this study, we benchmark different machine learning approaches for automated progression classification with various radiomic feature sets extracted from longitudinal magnetic resonance imaging and classifiers. Our experiments show differences in robustness and performance and offer insights into common failure modes. The best ROC-AUC was achieved with a random forest classifier without feature selection (0.748), and the best F1 score was at 0.792 for an XGBoost classifier where features of the current time point and the change from the reference time point were provided. Analyzing misclassifications shows different behavior for statistical machine learning classifiers and Residual Neural Networks.