## 4 Discussion
proposed multi-class CNN (FocalNet) to jointly detect prostate cancer lesions and predict their aggressiveness using GS and evaluated the GS classification by AUROC for clinically significant lesions (GS 7) (AUROC = 0.81 \(\pm\) 0.01) in comparison to U-Net-Mult and Deeplab (AUROC = 0.72 \(\pm\) 0.01 and 0.71 \(\pm\) 0.02, respectively) [25]. Since they used a publicly unavailable dataset, it is difficult to compare their study directly with ours. In the previously reported grand challenges by [PERSON] et al. using a publicly available dataset, the AUROC for the PROSTATEx Challenge task for differentiating between lesions that are and are not clinically significant ranged from 0.45 to 0.87 [32]. Although the AUROC in our results was not state-of-the-art, the AUROC in segmentation volume was higher than that in normal volume (Table 2). As shown in our result in Figure 4, all datasets outperformed random guessing (AUROC = 0.5). Moreover, the PROSTATEx-2 Challenges demonstrated the five-point Gleason Grade Group classification task. For further reduction of unnecessary biopsies, we will focus on the discrimination of the five-point Gleason Grade Group classification. This study has several limitations. First, the prostate volume on the MRI was small in the overall image. The same image matrix size was used when performing 3D-CNN, regardless of prostate semantic segmentation use. This suggests that the amount of relevant data available for AI to analyze was limited, potentially affecting learning accuracy. This implies that the AI system may require more extensive data to improve its performance, which could involve utilizing larger MRI images or incorporating additional imaging modalities. Secondly, the training data used in this study were scarce, with only 134 and 70 patients with GS \(<\) 7 and \(\geq\)7, respectively, available in the public database. This limitation emphasizes the need for larger, more diverse datasets to train the AI system efficiently. The NCCN prostate cancer guidelines currently select the initial therapy based on initial risk stratification and staging workup for clinically localized diseases. The risk group is categorized according to clinical or pathological features, which are characterized by the Gleason pattern or Gleason Grade Group. Based on the GS division, these are very low, favorable, unfavorable intermediate, high, and very high-risk groups. The Gleason Grade Group was categorized into five groups based on combinations of GS and Gleason patterns, including \(\leq\)6 and \(\leq\)3 + 3; 7 and 3 + 4; 7 and 4 + 3; 8 and 4 + 4, 3 + 5, 5 + 3; 9 or 10 and 4 + 5, 5 + 4, or 5 + 5. However, the SPIE-AAPM-NCI PROSTATEx Challenge dataset training cohort was labeled as GS \(\geq\) 7 for clinically significant prostate cancer. Therefore, the proposed method represents a two-class classification of a GS \(\geq\) 7 or GS \(<\) 7 using semantic segmentation and 3D-CNN. This highlights the need for more nuanced classification systems, which could potentially be achieved through the use of larger and more diverse datasets. Moreover, further research is necessary to utilize more detailed GS and Gleason pattern data for a five-class classification, which could offer a more precise and personalized approach to prostate cancer diagnosis and treatment. Third, there is a shortage of publicly available, high-quality prostate MRI datasets. The SPIE-AAPM-NCI PROSTATEx Challenge is a popular and publicly available prostate MRI dataset that was used in this study [29]. However, despite its focus on quantitative image analysis methods for diagnostic, clinically significant prostate cancer classification, and prostate AI, radiology, and urology expert involvement, this study contained a small, single-center, and multivendor dataset. The need for well-curated, larger datasets with diverse, multi-center, and multivendor data is clear. These datasets are essential for training the AI-based CAD system effectively and ensuring meaningful validation, robust performance, and a generalized model [33].