Fig. 4

Capability of mULM parameters to distinguish tumors with different vascular phenotypes. a Results of the inter-group comparison of all parameters using the one-way ANOVA and Bonferroni post-hoc test. Differences between parameters with p < 0.01 are highlighted in dark green. Differences with p < 0.05 are indicated in light green. Only the parameters which could discriminate all three tumor models were used to generate confusion matrices. b Confusion matrices were generated to assess the capability of the parameters to correctly assign individual tumors to their according group. The numbers in the diagonal elements of the matrix represent correct classifications (highlighted in green), the remaining numbers indicate false assignments (highlighted in pink; see explanatory example in the upper row). Confusion matrices of the maximum of distances to the closest vessel and of the maximum of distances to vessel with low velocities reveal a correct classification in all cases (100%). For the variance of distances to the closest vessel, 83% correct classification is achieved. c Although several parameters alone already allowed a correct assignment of all tumors, parameter combinations may be required when investigating more heterogeneous tumor populations. Therefore, a correlation matrix (Pearson’s correlation coefficient (r)) of all mULM parameters was generated to indicate the parameters providing complementary information. The highly discriminating distance parameters strongly correlated and thus, their combination may not be advantageous. However, the parameter local flow direction entropy showed a low correlation with the distance parameters and could be selected as a potential candidate for a multi-parameter readout