## 2 Materials and Methods
### Segmentation, Feature Extraction and Selection  
All mp-MRI examinations were transferred to a dedicated workstation. A three-year radiology resident (R.R.) manually segmented the prostate nodule previously (identified as a key image on T2-weighted sequences, DWI, and ADC maps), the entire prostate gland (on T2-weighted sequences), and the periprostatic adipose tissue (PPAT) (on T2-weighted sequences) using the open-source software 3D Slicer (v. 5.0.1). A volume of interest (VOI) was generated for each segmentation (Figures 1 and 2).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Category** & **Number** \\ \hline Patients with positive LNS & 35 \\ Patients with negative LNS & 50 \\ PSA (ng/mL) (Median, range) & 4.5 \\ Gleason Grade (Median) & 7 \\ Tumor target Zone Peripheral & 33 \\ Tumor target Zone Transition & 12 \\  & T3 (16) \\  & T2 (10) \\  & T3.5 (19) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Patients’ characteristics. LNS: lymph node status.  
Figure 1: Segmentation of the index prostatic nodule (green zone) in TSW (**a**), in ADC (violet zone in (**d**)) and in DWI (red zone in (**e**)), the PPTA (yellow zone) in T2W (**b**), prostate gland (orange zone) in T2W (**c**), in a 63-year-old patient with a PSA value of 10.3 ng/mL. The histological analysis showed an adenocarcinoma with a Gleason score of 7 (4 + 3), with involvement of neuromuscular bundle and negative lymph node involvement.  
Two radiologists, with 12 years (E.F.) and 5 years (D.S.) of experience, independently reviewed all prostate MRI scans and validated segmentation. Target lesions were identified in all sequences, measured, and recorded as key images.  
A total of 131 radiomic features were extracted from each VOI using the Pyradiomics application (v. 3) integrated into the software. Features were automatically selected based on relevance. Both semantic features and radiomics features were included in the analysis, by capturing intratumoral histopathological characteristics, such as tumor shape and intensity.  
Radiomics Features:  
Shape Features: Describing geometric properties of the region of interest (ROI), including surface area, total volume, maximum diameter, elongation, sphericity, and surface-to-volume ratio;  
First-Order Statistics: Histogram-based features representing the distribution of voxel intensities in the ROI, including metrics such as energy, entropy, mean, interquartile range, skewness, kurtosis, and uniformity;  
Figure 2: Segmentation of the index prostatic nodule (green zone) in TSW (**a**), in ADC (violet zone in (**d**)), and in DWI (red zone in (**e**)), the PPTA (yellow zone) in T2W (**b**), prostate gland (orange zone) in T2W (**c**), in a 55-year-old patient with a PSA value of 7 ng/mL. The histological analysis showed an adenocarcinoma with a Gleason score of 7 (4 + 3), with a locoregional involvement of neurovascular bundle and positive lymph node involvement.  
Second-Order Textural Features: Capturing the statistical interrelationships between neighboring voxels. These include:  
* Gray-Level Co-Occurrence Matrix (GLCM): Analyzes spatial gray-level intensity distributions within a 3D image;
* Gray-Level Run-Length Matrix (GLRLM): Quantifies contiguous voxels with the same gray-level value in multiple directions;
* Gray-Level Size Zone Matrix (GLSZM): Measures zones of connected voxels with identical gray-level intensity in a 3D space;
* Gray-Tone Difference Matrix (NGTDM): Evaluates differences between voxel intensity and the average intensity of neighboring voxels within a set distance;
* Gray-Level Dependence Matrix (GLDM): Assesses the degree of dependence between neighboring voxels at varying distances.