# Textural Analysis Supports Prostate MR Diagnosis in PIRADS Protocol
## 5 Conclusions  
In the current study, the feasibility of an automated approach to MR images of prostate was proposed. The results presented are promising, as the best F1-score of almost 77% was achieved for classification of patients into healthy (PIRADS \(<\) 4) and with PCa (PIRADS \(\geq\) 4). This good result shows that it is possible to represent the prostate glands with textural features and apply machine learning algorithms to build models that enable easy recognition of the cases considered.  
The results obtained allow the development of an objective prostate gland evaluation process and can be used in the future to prepare protocols for the automatic analysis of prostate images. This is especially welcomed by the medical community as prostate scan in MRI becomes a gold standard in the diagnostic process. However, there are many uncertainties due to interobserver differences in rating even among experienced radiologists. Therefore, a response supported by the automated approach may serve as a second opinion. Additionally, it could be a significant aid for less experienced physicians. In both cases, it should reduce possible diagnostic errors. There is a high demand for a unified system for determining the probability of prostate cancer. It can be developed in the future based on proposed approaches toward objectification of the diagnostic process with an increase in patient comfort and the legal safety of healthcare providers.  
**Supplementary Materials:** The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app13179871/s1](https://www.mdpi.com/article/10.3390/app13179871/s1)]([https://www.mdpi.com/article/10.3390/app13179871/s1](https://www.mdpi.com/article/10.3390/app13179871/s1)), Table S1: All results for models built from pair of features in all considered experiment cases.  
**Author Contributions:** Conceptualization, S.G., R.O., A.P. and K.N.,; methodology, S.G., R.O., A.P. and K.N.; software, K.N.; validation, K.N.; formal analysis, S.G., R.O. and K.N.; investigation, S.G., R.O. and K.N.; resources, S.G., R.O. and A.P.; data curation, S.G., R.O., J.L. and A.P.; writing--original draft preparation, S.G., R.O. and K.N.; writing--review and editing, S.G., R.O. and K.N.; visualization, S.G., R.O., A.P. and K.N.; supervision, S.G., R.O. and K.N.; project administration, S.G., R.O., A.P. and K.N. Funding acquisition S.G. and R.O. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** The study was conducted in accordance with the Declaration of Helsinki and was approved by the Institutional Review Board of Jagiellonian University (no 1072.6120.21.22, 23 February 2022).  
**Informed Consent Statement:** This study was retrospective in nature; anonymized images acquired during routine examinations were used with the permission of the Bioethics Committee.  
**Data Availability Statement:** One hundred and fourteen T2-weighted MRI 3D images of the prostate with corresponding segmentations of transition and peripheral zones are available on the Zenodo website, [[https://doi.org/10.5281/zenodo.7676958](https://doi.org/10.5281/zenodo.7676958)]([https://doi.org/10.5281/zenodo.7676958](https://doi.org/10.5281/zenodo.7676958)) (accessed on 30 July 2023).  
**Conflicts of Interest:** The authors declare no conflict of interest.