## 4 Discussion  
There is a clinical challenge in the reproducibility of PIRADS amongst radiologists and between different centres [21]. The quantification of features from images could be used as a marker of disease diagnosis, prognosis, and long-term outcomes. The texture analysis values in the detection of prostate cancer showed promising results [22]. The phantoms used in this study were a validation cohort to assess the combination between the radiomic features, providing an illustration of how the combination between the parameters differentiates between cancerous and non-cancerous images in clinical and pre-clinical practice. Combining multiparametric MRI findings with radiomics could potentially improve our understanding of disease and provided the necessary information needed for decision-making. There are reports that combining DCE features and T2 WI increase AUC from 0.773 to 0.756 [23]. Excellent inter-rater reliability was shown with a correlation coefficient of 0.98 in a single measure (\(p<0.001\)) in five radiomic parameters which were mean, median, mode minimum, and maximum and the same finding was found in other study as they have 0.9 correlation coefficients in the phantom [24]. A higher  
Figure 8: The AIC and Pseudo R2 (R2 McFadden) for multiple logistic regression equations. As the number of parameters increased, AIC started to decrease, and R2 McFadden started to increase. reproducibility was suggested in a systematic review analysing the reproducibility of the radiomic parameters in different modalities (MRI, PET, and CT) and the analysis was performed in 481 studies supporting our findings [25]. We have shown that the combined approach improves the diagnostic accuracy of MRI and validates our findings through ex vivo phantom image analysis. The PCA showed a higher separation between non-cancerous and cancerous-mimicking materials in our pre-clinical validation study. This was confirmed in the clinical cohort where the combined approach performed well, albeit at lower rates. The homogenous phantom model in the preclinical setting presented higher diagnostic separation in contrast to clinical patient data as the signal intensity changed in the different prostate zones. Our results complement the findings of a previous study reporting a higher separation between the healthy and cancerous tissue. That study had a limited number of patients (n = 25) and only peripheral zones were analysed, in contrast to our study where the prostate glands were analysed for all 12 regions. The combined approach of DCE and texture analysis with standard MRI (T2 WI + DWI) improved the diagnostic accuracy; the AUC increased from 0.79 to 0.82. The discriminate analysis shows a high percentage of model accuracy in the separation group ex vivo and in vivo (100% and 81%, respectively), which is considered an acceptable proportion of separation of the healthy and cancerous tissue. Previously, reports have suggested that combining DCE and radiomics with standard MRI improves the detection rate of cancers in peripheral zones [26]; however, our model combining DCE, radiomics, and standard MRI increased the prediction of cancers in all zones. This study showed that some radiomic features were significantly correlated with the histopathology, using the maximum, minimum, range, and mean; the same findings have been reported in previous studies [27], which support the addition of radiomic features into prediction models of prostate cancer. The DCE MRI appears to be controversial, because some studies suggest and highlight the importance of this parameter, whereas other studies have demonstrated limitations of this technique, especially in the temporal resolution of the sequence. The findings in this study were not significantly correlated with the histopathology reports [23].