## 4 Discussion  
Lymph node involvement in prostate cancer [20] is currently assessed through regional lymphadenectomy, as recommended by the latest clinical guidelines. Specifically, the European Association of Urology (EAU), European Society for Radiotherapy and Oncology (ESTRO), EAU Section of Urological Research (ESUR), and International Society of Geriatric Oncology (SIOG) advocate for an extended pelvic lymph node dissection (e-PLND) in patients with a risk of nodal involvement exceeding 5% [21]. The National Comprehensive Cancer Network (NCCN), however, recommends e-PLND for patients with a risk greater than 2% [22]. Lymphadenectomy typically targets specific nodal stations, such as the inguinal and internal obturator lymph nodes. Despite its diagnostic and therapeutic significance, the procedure is associated with substantial complications, prompting the exploration of alternative, non-invasive methods for predicting lymph node involvement. Algorithms combining clinical, anatomo-pathological, and radiological data (e.g., Briganti or MSKCC nomograms [23]) have been developed to estimate the likelihood of lymph node involvement, sparing unnecessary surgical interventions. ChatGPT has been used as a tool for analysis, although in terms of radiological and imaging performance, it does not have sufficient performance to replace a radiologist [24]. Among imaging modalities, MRI has shown utility in evaluating lymph node status with varying success, often dependent on the radiologist's expertise. Diffusion-weighted imaging (DWI) [23; 25], a functional imaging technique that quantifies water molecule movement through apparent diffusion coefficient (ADC) maps, has demonstrated the ability to differentiate between benign and malignant lymph nodes. However, its role remains limited due to difficulties in detecting micrometastases, necessitating reliance on surgical staging for definitive evaluation. Advancements in artificial intelligence (AI) [26] and radiomics have significantly improved the capacity to extract and analyze quantitative data from imaging [7; 27]. Radiomics allows for the evaluation of diverse tumor characteristics, including shape, intensity, and texture features [28], offering a detailed representation of tumor microenvironments. In this study, we employed a machine learning model of random forest type [29; 30] to predict lymph node involvement by combining MRI-derived radiomics [31] features with clinical, anatomopathological, and radiological information. The use of a train-test split combined with internal cross-validation strengthened the reliability of the reported model performance, reducing the risk of overfitting and supporting the generalizability of our findings. Our approach extended beyond the analysis of prostate nodules to include the entire gland and periprostatic adipose tissue (PPAT), recognizing the paracrine influence of PPAT [32] in prostate cancer progression. We identified significant radiomics descriptors from prostate nodules, the gland, and PPAT using machine learning to integrate these features for improved lymph node involvement prediction. Notably, the inclusion of PPAT features alongside gland and nodule data yielded the highest AUC values, supporting the notion that lymph node and adjacent organ involvement is influenced not only by primary tumor characteristics, but also by the surrounding microenvironment [33; 34]. Consistent with us, recent studies have highlighted that radiomic features extracted from periprostatic adipose tissue (PPAT) can provide additional information on prostate cancer aggressiveness and local disease spread [35; 36]. [PERSON], [PERSON] et al. [37], in their radiomic analysis on T1-weighted images, demonstrated that different patterns of periprostatic fat significantly correlated with prostate cancer (Gleason \(\geq\) 7), distinguishing indent tumors with high predictive accuracy (AUC \(\sim\)0.82). Similarly, [PERSON], [PERSON] et al.