# Textural Analysis Supports Prostate MR Diagnosis in PIRADS Protocol
## 4 Discussion
This scatter plots present nicely that there is a large number of textural feature pairs that allow one to create models working well. It also shows that, in some weaker cases, several models obtain comparable outcomes. However, when all those features are used together, the results do not improve. Finally, from this graphical presentation, we also see clearly that the F1-score grows proportionally to the accuracy, which was not so clear when analysing smaller sets of data in the tables. See all results in Supplementary Materials. Previous studies indicate that computer assistance results are not worse than the methods used so far; however, it is too early to assess the full advantage of artificial intelligence in analysing MR imaging [51]. Computer-assisted prostate interpretation showed that artificial intelligence (AI) systems are capable of increasing the accuracy of MRI interpretation [51; 52]. The results presented in [53] agree with previous conclusions showing a precision of 85% for PIRADS = 4. [PERSON] et al. [53] presented the ability of the system to increase not only the accuracy of the interpretation but also the speed of the interpretation. Next, in the systematic review prepared by [PERSON] et al. [54], the authors present a comparable performance of fully automated and AI-assisted techniques. However, in the review by [PERSON] et al. [55], the automated system meets the performance of expert radiologists with lower sensitivity. Implementing AI models markedly increased the accuracy of prostate image interpretation. There are expectations for the interpretation of PIRADS = 3 [56]. However, in a study by [PERSON] et al. [57], which conducted an evaluation method based on textural classification, a sensitivity of 0.85 and a specificity of 0.73 were found in demanding PIRADS = 3. On the other hand, [PERSON] et al. [58] reported no clear benefit from the use of deep learning software in studies carried out at different levels of experienced radiologists. Compared to similar works undertaken by [PERSON] [59; 60], obtaining a correct classification F1-score greater than 76% is a good result for texture features attributed to different lesions assigned to PIRADS. Especially when considering the much more homogenous group of confirmed tumours in the second work. Finally, an excellent review of the application of AI techniques in classification of prostate lesions among different MRI modalities is given in [61]. The authors reported a mean sensitivity  
Figure 3: The figure presents distribution of the best 500 classification models in the results space. of 0.80, which is close to our results. That puts our approach in the main stream of results currently obtained with machine learning approaches. In this research, we decided to use the approach to prostate MRI analysis as a binary classification problem, where only healthy (PIRADS \(<\) 4) and PCa (PIRADS \(\geq\) 4) were distinguished. That allowed for task simplification and diminished the influence of class imbalance in the gathered dataset. Since it was a trial study evaluating whether texture analysis is applicable, we found this simplification justifiable. However, we are aware that in further research, all five grades from PIRADS should be addressed. Our work was conducted using 1.5 T scanner, which might be perceived as a limitation of the study, where the PIRADS Steering Committee prefers 3 T scanners over 1.5 T. However, 1.5 T scanners are accepted because the application of new technologies allows them to provide adequate images and reliable diagnostic results [62]. Moreover, the 1.5 T scanners number is prevalent in the healthcare market, which naturally forces their use. Finally, deriving information from 1.5 T scanner data with texture analysis overcomes any possible data inaccuracy. This study's strengths can be concluded as follows: We prepared a dataset of MRI prostate scans that were supported with PIRADS grading.