## 4 Discussion
For instance, a 40% SUV-Vmax threshold balances reproducibility and accuracy, while fixed-bin-width discretization enhances consistency across reconstruction settings [31; 32]. Dimensionality reduction techniques such as principal component analysis can streamline feature sets and reduce redundancy, as shown in melanoma radiomics [15]. Aggregation strategies, e.g., volume-weighted averaging across the three largest lesions, can translate lesion-level features into patient-level predictors. In addition, harmonization techniques, such as ComBat, are essential in multicenter or multi-scanner studies, where variability in acquisition protocols or reconstruction parameters can introduce batch effects that compromise feature comparability [33]. However, we would like to clarify that our study was conducted using a single PET/CT scanner, with the same acquisition and reconstruction protocol across all patients. No differences in CT kernel, PET reconstruction settings or SUV normalization approaches were present in the dataset. Therefore, the implementation of a harmonization step was not necessary in this context, as no batch effects related to equipment or protocol variability were expected. All radiomic features in our study were extracted using the open-source platform PyRadiomics, which follows the guidelines of the Image Biomarker Standardization Initiative (IBSI) to ensure reproducibility and standardized nomenclature. In the set of features extracted, both from the CT and PET images, the robust and nonredundant features were identified by application of the Mann-Whitney U-test and Spearman correlation analysis. While recognizing that some texture-based features may seem mathematically abstract, previous studies have demonstrated their importance in characterizing tumor heterogeneity, metabolism, and aggressiveness in oncological imaging [34]. In this regard, although it would have been interesting to compare original and wavelet-based texture features separately, in our cohort, the limited number of robust features retained after reduction precluded a meaningful subgroup analysis. Such stratification would have compromised statistical power and model stability. Future studies with larger cohorts may enable more detailed comparisons. Our study has some limitations, including its retrospective, single-center design and the limited number of patients, which may restrict generalizability; prospective validation in independent cohorts is needed before clinical implementation. Additionally, in our study, lesions were identified based on [18F]FDG uptake, and only the primary cutaneous lesion was considered for each patient. In cases with multiple lesions, as reported in Section 2.3, the one with the highest SUVmax was defined as the index lesion and used for radiomic analysis. Thus, each patient contributed a single lesion to the dataset. Importantly, no spatial normalization or scaling procedures were applied to the features dataset during preprocessing. This choice preserves regional and morphological heterogeneity at the feature level, allowing the machine learning algorithm to incorporate this variability into the predictive modeling process. In this context, anatomical heterogeneity is not considered as noise but as a potentially informative component of the feature space. Nonetheless, we agree that anatomical location could represent a limitation to the external generalizability of our ML models. Although SMOTE is a useful technique for mitigating class imbalance, it can potentially introduce synthetic artifacts and reduce model generalizability. In particular, oversampling may lead to overfitting or create unrealistic feature patterns, especially in small datasets. To minimize such risks, we performed SMOTE with a minimum number of three nearest neighbors, thus limiting the generation of overly similar synthetic samples. This approach helps preserve the intrinsic structure of the original dataset and reduces the likelihood of introducing artificial bias during the model construction [35].