DCE-MRI and OE-MRI scans were performed on 8 preclinical U87 tumour xenografts. Heuristic features (area-under-curve and rate-of-enhancement) were calculated from tumour voxel enhancement curves for each imaging modality. Clustering algorithms (k-means clustering and Gaussian mixture modelling) were applied to these features and native tissue T1 to investigate their utility in characterising physiological heterogeneity in tumours. Efficacy in identifying large regions where there is agreement between features is shown. Further optimisation is needed to optimise the approach to characterise smaller, and potentially important, regions where there is a lack of concordance between features.