## 5 Conclusions  
This study showed that the first-order statistics extracted from radiomics, DCE and standard MRI (T2 WI-DWI), could predict PCA results; pre-clinical experiments with tissue-mimicking phantoms validated the study findings. The contribution of radiomic features showed better results compared with DCE; therefore, it could replace it in the future and avoid the need for DCE being performed for the patient. This study showed that 15% of significant cancers which were missed by mpMRI were reported by radiomics, and 8% of lesions missed by radiomics were reported by mpMRI, indicating the importance of the combination of radiomics and mpMRI methods. The radiomic parameters showed an excellent inter-rater reliability, with a correlation coefficient of 0.98 in a single measure (\(p<0.001\)) in five radiomic parameters. In this study, four radiomic features were significantly correlated with the histopathology.  
**Supplementary Materials:** The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app13010576/s1](https://www.mdpi.com/article/10.3390/app13010576/s1)]([https://www.mdpi.com/article/10.3390/app13010576/s1](https://www.mdpi.com/article/10.3390/app13010576/s1)), Figure S1: Extraction of the Radiomic feature by Olea software and this example shows 4 regions were extracted for the mid of the prostate and the drawing match with the TRUS protocol to compare the findings; Figure S2: Phantom which cancerous tissue simulation appeared with low signal intensity inside the red circle, and it is the similar appearance of cancer in the PZ in the T2 WI of the prostate as it leads to reduce the signal intensity; Figure S3: The extracted DCE values from different areas inside the prostate of one patient using Tissue 4d software Table S1: MRI acquisition parameters; Figure S4: The ROC curve for the radiation feature and the combined parameters between the 11 features has the highest AUC. 4 parameters reported a higher sensitivity and specificity compared with other parameters and these parameters were mean, median, mode, and minimum as their AUC were higher than 0.96; Figure S5: DAPC and how it separates both groups with high accuracy 100 % and no overlap between the cancerous and healthy which validates the use of the radiomic features in the clinical data; Figure S6: The correlation matrix between variables and the higher correlated variable can be excluded to give higher separation in PCA. The positive correlation between the variable was coloured with blue, while the negative correlation was coloured with red; Table S1: Shows the radiomic parameters used in this study; Table S2: Univariate and multivariate logistic regression analysis.  
**Author Contributions:** Conceptualization, G.N. and Z.H.; methodology, F.A., B.S.O.A., C.W., S.G., M.S.-B., J.W., Z.H. and G.N.; investigation, F.A., B.S.O.A., C.W., S.G., J.W., M.S.-B., Z.H. and G.N.; writing--original draft, F.A. and G.N.; reviewing and editing, F.A., Z.H. and G.N.; funding acquisition, G.N.; supervision, G.N. and Z.H. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** This was a prospective, protocol-driven study with ethical approval obtained through the East of Scotland Ethical committee and Caldicott permission (IGTCAL6358)) to access healthcare follow-up data.  
**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.  
**Data Availability Statement:** The data are available for scrutiny from external requests.  
**Conflicts of Interest:** The authors declare no conflict of interest.