Keywords: Molecular Imaging, RadiomicsMolecular MRI allows for immunotherapy treatment monitoring of glioblastoma, but analysis of multi-parametric data is complex. Machine learning algorithms can be applied to quantitative maps, to identify correlations between radiomic features and treatment outcomes. Feature selection is key when dealing with longitudinal preclinical data with multiple contrasts and small group numbers. We evaluated three feature selection methods in terms of their ability to produce predictive models of survival. The best performance was seen using recursive feature elimination applied to features from iron concentration maps of the tumor, which yielded an ROC AUC of 0.78 and an accuracy of 0.72.
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