## 4 Discussion
Unfortunately, most public datasets are too small, and the quality of annotations provided per dataset varies significantly. [PERSON] et al. reviewed 17 public prostate MRIs [34]. The SPIE-AAPM-NCI PROSTATEx Challenge, including the SPIE-AAPM-NCI PROSTATEx-2 Challenges running from 15 May 2017, to 23 June 2017, presents additional difficulties, such as the focus on quantitative multiparametric MRI biomarker development to determine the Gleason Grade Group in prostate cancer [27; 28; 29]. As of 5 May 2022, the PI-CAI challenge has publicly released 1500 anonymized multiparametric prostate MRIs from 1476 patients at multi-centers in The Netherlands, acquired with a multivendor between 2012 and 2021 [35]. This challenge aimed to validate the diagnostic performance of AI and radiologists at clinically significant prostate cancer detection or diagnosis in MRI with histopathology and over three years of follow-up as the reference standard. Therefore, datasets should be added cautiously, ensuring that they provide unique and high-quality data. Another primary limitation of our study is that we have not delved into the investigation of hyperparameters in deep learning model training. In machine learning, and more specifically in deep learning, hyperparameters are crucial elements that can significantly influence the accuracy, efficiency, and computational cost of models. These variables, set before the model training process begins, control the overall behavior of a learning algorithm and can substantially affect the performance of the model. Although many public prostate MRI data may be available, it is difficult to consolidate multiple public datasets using the definition and annotation quality provided per dataset with missing information across images and cohort distributions. Also, data overlap in public datasets should be avoided, as multiple public datasets may contain identical cases [34]. [PERSON] et al. reviewed data overlap as an example of how The National Cancer Institute's Cancer Imaging Program, in collaboration with the International Society for Biomedical Imaging (NCI-ISBI 2013) dataset [36], combined the Prostate-3T [37] and PROSTATE-DIAGNOSIS dataset [38], and the PROSTATEx [27] and Prostate-3T datasets were included in the PI-CAI dataset [34; 35]. Therefore, datasets should be added cautiously. Although this study was ensured to use well-established, robust, and commonly accepted values for hyperparameters in our models, an exhaustive hyperparameter tuning process was not implemented. There could potentially exist a different set of hyperparameters that could yield better performance or more accurate results for the same models applied to the same dataset. In addition, further study of the segmentation model for prostate extraction is needed to determine changes in extraction accuracy and classification results. It is important to consider common factors between segmentation models and classification models, as well as the extent of obtaining large-scale data, to ensure accuracy. To address this, we believe it is crucial to thoroughly investigate previous research papers and refer to them in order to understand how much data is needed to guarantee accuracy. It is also important to train models using not only publicly available databases but also real-world datasets that can be obtained through ethical approval. This approach will contribute to refining and enhancing the performance of the models. Despite the limitations of the current study, its findings underline the potential of AI-based systems for advancing prostate cancer diagnosis. There is a clear need for more detailed classification systems in the clinical guidelines for initial therapy, and further research is required to non-invasively estimate the GS using multiparametric MRI. Public prostate MRI datasets are often small and feature different cohorts and annotation qualities, underscoring the need for larger and more diverse datasets.