## 4 Discussion
The principal limitation of our study is the lack of external validation. This might limit the assessment of model generalizability. The observed drop in performance from AUC 0.97 (training) to 0.88 (internal validation) in the BCR_PET model reflects a reduction in the predictive performance in internal evaluation. However, we would like to note that the reduced performance on the independent test set compared to the training set suggests that the test data were truly unseen to the model. This supports the methodological rigor of our internal validation approach [36]. Unfortunately, we did not have access to an external dataset and there is no public database of [18]FJFDG PET scans for advanced cSCC, reflecting the rarity of this clinical entity. Nonetheless, external validation--preferably using independent, multicenter datasets--remains crucial to confirm the robustness and reproducibility of our predictive radiomic signatures. In our cohort, lesion segmentation was performed using a semi-automated thresholding approach based on 40% of SUVmax within the lesion bounding box. This method was selected because it reflects current clinical practice and is widely adopted in oncologic PET imaging due to its operational simplicity and reproducibility [37]. However, it does not assess inter-observer variability, nor does it formally assess robustness to different reconstruction kernels, as recommended by IBSI guidelines. Looking forward, deep learning-based segmentation and feature extraction could streamline radiomic workflows, reducing manual VOI delineation and enabling near-real-time application [38]. In this regard, [PERSON] et al. introduced a transformer-based attention framework that fuses CNN-extracted features with handcrafted radiomics via a channel-spatial attention module, demonstrating significantly improved feature relevance and classification accuracy in breast cancer imaging [39]. This work highlights how integrating attention mechanisms can further enhance the discriminative power of radiomic-deep learning hybrids. Prospective trials should embed radiomic biomarker collection to validate predictive algorithms and assess real-world utility. Beyond guiding individual patient care, our predictive radiomic signatures could be prospectively incorporated as stratification factors or exploratory endpoints in future clinical trials of first-line PD-1 blockade or combination regimens in advanced cSCC. By identifying likely responders at baseline, trial designers could enrich for sensitive subpopulations--boosting statistical power and reducing sample size--while also exploring adaptive designs where non-responder signatures trigger early protocol amendments. Embedding our radiomic models alongside translational assays (e.g., IL-8, PD-1, and T cells' dynamics) would create a multiparametric biomarker platform, accelerating the shift toward precision immuno-oncology in rare skin cancers. Concurrently, expanding public repositories to encompass less-common pathologies--such as cSCC--and leveraging open data platforms like The Cancer Imaging Archive will facilitate multi-institutional external validation and accelerate the adoption of radiomics in PET imaging [40].