## 3 Results
### Correlation between Seven CT, Twenty-Four MRI Features and Ki-67 Score  
In the assessment of the correlation between the radiomics features and the Ki-67 values adjusted, R\({}^{2}\) of more than 0.8 was considered as indicative of the linear correlation. Seven CT and 24 MRI features exhibited a correlation with the post-treatment Ki-67 values according to this criterion (Figure 3). An example of five CT-derived radiomics features (Figure 3A top row) and five MRI-derived features (Figure 3A bottom row) as a function of post-treatment Ki-67 marker have been shown in Figure 3. Besides the imaging features, the figure displays the respective Ki-67 staining pictures with the denoted average Ki-67 values used in the assessment of post-treatment histopathology (Figure 3B). The calculated correlation coefficients as well as the significance levels for the CT-derived features are outlined in Table 1. Figure 1 depicts only five features while the Table 1 presents details for all CT radiomics with \(p\)-value of \(<\)0.05. The Pearson correlation coefficients and the significance levels were calculated for the imaging features derived from the whole tumor. In order to further validate the presented results we generated two additional subregions in the tumors for both CT and MRI imaging modalities and we calculated the Pearson correlation coefficients for the radiomics derived from those subregions. Thereby, we were able to calculate the variance of in the Pearson correlation coefficients on feature-by-feature basis and they are outlined in the third column of the table. Figure 3: Dependence of five CT (**A** top row) and five MRI (**A** bottom row) radiomics features on post-treatment histochemistry proliferation marker Ki-67. The CT features (**A** top row) are mean absolute deviation 3D, cube IMC1, matrix IMC1, matrix inverse difference moment, and cube sparsity. The MRI features (bottom row) are mean absolute deviation 3D, matrix mean, matrix entropy, cube IMC1, and cube IMC2. Also, (**B**) the column on the right contains the histology slides with Ki-67 values denoted in the top left corner of each slide. The Ki-67 increases from top to bottom, as can be observed from the increasing amount of red-brown stain on the slides. The correlation coefficients for the MRI-based radiomics are presented in Table 2. There are 24 MRI features with \(p\)-value of \(<\)0.05. Similarly to Table 1 above, the last column in Table 2 outlines the variance in the Pearson correlation coefficients where the two additional subregions in the tumor are used for radiomics extraction. The variances of Pearson correlation coefficients in both tables are very small, indicating that the observed relations are stable within the tumor volumes. For both CT and MRI radiomics, numerous imaging features demonstrate clear correlations with tumor cell proliferation markers derived from post-treatment histology as denoted in Tables 1 and 2. All of the correlation coefficients are at a significance level of \(<\)0.05, 11 out of 24 are at a level of \(<\)0.01. The correlation coefficients in turn, are greater than 0.9 or less than \(-\)0.9, indicating that imaging features can serve as a good method to predict Ki-67 proliferation marker in murine breast tumors.