## 4 Discussion  
In this exploratory cohort of 59 patients, the PET-based radiomic models achieved cross-validated AUCs up to 0.97 for immunotherapy response and 0.85 for tumor grade, with internal validation AUCs of 0.88 and 0.80, respectively. These results illustrate the potential of handcrafted radiomics to outperform conventional PET metrics in predicting both treatment benefit and histopathologic differentiation [18,19]. Advanced cSCC is a rare, challenging disease of predominantly elderly, frail patients with limited systemic options. While ceniplimab yields 40-50% response rates [8,9], there remains an urgent need for noninvasive, image-based biomarkers to guide therapy selection and avoid unnecessary toxicity in non-responders. By selecting a homogeneous elderly cohort, we aimed to assess the intrinsic value of baseline radiomic heterogeneity; adding clinical covariates in larger, more diverse populations will be an important future step to mirror real-world decision making. Our PET-based radiomic models achieved cross-validated AUCs up to 0.97 and internal validation AUCs up to 0.88, clearly outperforming CT-based models. This highlights how metrics of metabolic heterogeneity capture immune-relevant tumor biology beyond conventional volumetric measures such as SUVmax, MTV, and TLG. A key strength of our cohort is that 77.9% of patients received immunotherapy as first-line treatment, reflecting real-world practice in which ceniplimab is increasingly adopted upfront for advanced cSCC. In this context, imaging and laboratory biomarkers to stratify patients by likelihood of benefit are urgently needed. We also assessed a secondary endpoint: the ability of PET radiomics to infer tumor grade \"in vivo.\" Accurate noninvasive grading is invaluable because histological grade reflects underlying biology--proliferation, differentiation, stromal interactions--and correlates with aggressiveness and evolutionary potential [20,21]. Whereas conventional biopsy may undersample heterogeneous high-grade regions, radiomic texture features survey the entire lesion (and multifocal sites), capturing spatial heterogeneity and temporal evolution [22]. Our grade-prediction models yielded AUCs of 0.85 (cross-validated) and 0.80 (internal validation), indicating that baseline metabolic heterogeneity encodes histopathologic grade. Such noninvasive predictions could inform treatment intensity and surveillance, and--when combined with immunotherapy-response models--generate a multiparametric signature of tumor biology that guides both prognosis and therapeutic sensitivity [23]. However, in this regard, while an AUC \(\geq\) 0.8 is generally considered the minimum for a clinically useful screening biomarker, confirmatory testing and prospective calibration will be required before routine use of tumor-grade radiomics. Regarding laboratory biomarkers, elevated or rising IL-6 levels have been linked to poorer outcomes in ceniplimab-treated cSCC [24]. A recent translational study identified early immune and transcriptomic changes as predictive markers: responders showed increased intratumoral B and CD8+ T cells, plus early downregulation of IL1\(\beta\) and IL8 (also mirrored in peripheral blood). Moreover, PD1+ regulatory T cells declined in responders but rebounded in non-responders, highlighting IL8 and PD1+ Tregs as promising early indicators of response [25]. Radiomics complements laboratory data by providing a noninvasive, image-based means to enrich for likely responders--especially relevant in our predominantly elderly cohort, for whom toxicity and quality-of-life concerns are paramount [26]. By identifying complex texture patterns on baseline PET scans, our models could support clinical decision making at therapy initiation, potentially contributing to a composite predictive score alongside clinical and laboratory variables. Although PET/CT is not yet standard for assessing immunotherapy response in cSCC, [PERSON] et al.