## 4 Discussion
[38] supported that integrating radiomic features from the PPAT with tumor nodule features improved prognostic stratification. They introduced a radiomics nomogram combining intratumoral and periprostatic radiomic features from mpMRI for the prediction of biochemical recurrence-free survival, obtaining very high AUC values (0.85-0.92 at 1-5 years) [38]. These findings suggest that PPAT radiomics may serve as an additional biomarker of tumor aggressiveness and risk stratification, reflecting the interactions between the tumor and its local adipose environment. Emerging evidence highlights the role of PPAT in facilitating tumor progression [39] through mechanisms such as neovascularization [33], extracellular matrix remodeling, and epithelial-mesenchymal transition (EMT). PPAT secretes fatty acids and other soluble factors, enhancing tumor aggressiveness and regional spread [39]. For instance, [PERSON] et al. revealed diminished immune surveillance in PPAT associated with complement factor H (CFH) downregulation. These findings underscore the biological relevance of PPAT in prostate cancer pathophysiology [36]. Other studies support the evidence that radiomics analysis should be extended to the entire prostate gland and surrounding tissues to capture signals of locoregional disease spread. Some studies have shown that peritumoral radiomics features (i.e., features extracted from a defined ring of tissue surrounding the tumor) provide crucial information on extracapsular extension (ECE). In the study by [PERSON] et al. involving prostatectomy patients, a radiomic model incorporating peritumoral features from mpMRI more accurately predicted ECE compared with intratumoral radiomics alone [40]. We demonstrated an accuracy of about 89% and an AUC of 100% when all masks were combined, while the performances of both were lower if a single nodule, gland, or PPAt were analyzed. However, especially for single analysis, a disparity between accuracy and AUC in our results was noted. This underscores the complementary nature of these metrics: while accuracy reflects the overall proportion of correct predictions, it is sensitive to class imbalance. Conversely, AUC provides a threshold-independent measure of the model's discriminative power, particularly valuable in imbalanced datasets. The apparent discrepancy between accuracy and AUC values observed for DWI and ADC sequences can be attributed to the intrinsic limitations of using these modalities in isolation, as well as the class imbalance in our dataset. While accuracy is threshold-dependent and sensitive to the dominant class, AUC provides a threshold-independent assessment of the model's discriminative ability. This divergence highlights the importance of evaluating multiple performance metrics and supports our choice to integrate radiomic features from multiple anatomical regions to enhance predictive power. Currently, there is a growing interest in AI method development for predicting and detecting occult lymph node metastases preoperatively in many oncological fields [40]. However, only few studies about PCa are present in the literature, although with very encouraging results. [PERSON] et al. [6] achieved an AUC of 0.86 for predicting nodal or distant metastases, and [PERSON] et al. [23] reported an AUC of 0.83 in subgroups with small lymph nodes using radiomics features. [PERSON] et al. developed a machine learning model integrating radiomic features from a prostate tumor on mpMRI with clinical data that predicted lymph node invasion (LNI) with high accuracy (AUC \(\sim\)0.91 in the test set) [41]. Our study [41] demonstrates higher AUC values, reinforcing the potential of radiomics-based approaches in improving diagnostic accuracy. Our results demonstrated elevated specificity compared with sensitivity, indicating the model's robustness in identifying true negatives, i.e., patients without lymph node involvement. This is clinically significant as it enables the sparing of unnecessary lymphadenectomies and their associated complications.