Object: To develop new imaging biomarkers of therapeutic efficacy through the quantification of intratumoral microvascular heterogeneity. Materials and Methods: The described method was a combination of non-supervised clustering and extraction of intratumoral complexity features (ICF): number of non-connected objects, volume fraction. It was applied to a set of 3D DCE-MRI K trans maps acquired previously on tumor bearing mice prior to and on day 4 of anti-angiogenic treatment. Evolutions of ICF were compared to conventional summary statistics (CSS) and to heterogeneity related whole tumor texture features (TF) on treated (n=9) and control (n=6) mice. Results: Computed optimal number of clusters per tumor was 4. Several intratumoral features extracted from the clusters were able to monitor a therapy effect. Whereas no feature significantly changed for the control group, 6 features significantly changed for the treated group (4 ICF, 2 CSS). Among these, 5 also significantly differentiated the two groups. (3 ICF, 2 CSS). TF failed in demonstrating differences within and between the two groups. Discussion: ICF are potential imaging biomarkers for anti-angiogenic therapy assessment. The presented method is expected to have advantages with respect to texture analysis based methods regarding interpretability of results and setup of standardized image analysis protocols.