## 4 Discussion
[27] reported high discordance between metabolic complete response (CMR)on FDG-PET and anatomical response per RECIST1.1 on CT/MRI in cenpilimab-treated patients. In their retrospective Australian cohort (n = 15; median age 73 years, 93% male), 73% achieved CMR on PET, yet only one of these met the RECIST1.1 criteria for complete response, suggesting that PET may detect deeper, immune-mediated tumor eradication not captured by size-based measures. Our work moves beyond simple CMR/PMR categories by applying high-throughput quantification of handcrafted radiomic features. This approach uncovers subtle spatial and textural patterns--dimensions that may more accurately predict long-term benefit than binary response metrics. In our prior study of 25 cSCC patients treated with cenpilimab, baseline MTV and TLG both correlated significantly with event-free survival (\(p<0.05\)), and a lack of metabolic response at 12 weeks was linked to markedly shorter survival (7.2 \(\pm\) 1 vs. 20.3 \(\pm\) 2.3 months). On multivariate analysis, metabolic response at 12 weeks remained the sole independent predictor. While these conventional metrics are prognostically valuable, they miss intratumoral heterogeneity and spatial complexity. By integrating radiomic features with MTV and TLG, we aim to build a more comprehensive, multiparametric signature of tumor aggressiveness and immunogenicity [12]. In our cohort, PET-based radiomics outperformed CT-based radiomics for the prediction of immunotherapy response. This may stem from greater feature variability--and thus more critical robustness assessment--in CT radiomics when lesions span diverse anatomical regions due to CT's higher spatial resolution and different physical principles compared with PET. Indeed, in the BCR model, PET yielded 86 robust features versus only 7 from CT, most belonging to the low-frequency domain; these components are crucial for establishing a reliable training set in PET-based predictive modeling. Overall, PET imaging continues to demonstrate its suitability for radiomic analysis and predictive modeling, offering a more stable and informative feature set across heterogeneous anatomical contexts. This observation is consistent with findings by [PERSON] et al., where PET-based radiomics outperformed CT in predicting pathological complete response in breast cancer, further underscoring the potential of PET as a robust foundation for radiomics-driven clinical applications [16]. Nonetheless, the clinical translation of radiomics faces hurdles. Many radiomic parameters--including first- and second-order features and complex wavelet- or texture-based descriptors--lack intuitive clinical meaning, hindering routine adoption [28]. Bridging this gap will require advanced analytics and a better contextualization of high-dimensional radiomic data within known biological frameworks [29]. An important caveat of any predictive model is the balance between false positives (FP) and false negatives (FN). In our context, an FP, i.e., classifying a patient as likely to respond when they will not, could lead to the continuation of ineffective immunotherapy, unnecessary exposure to immune-related adverse events, and the delay of potentially life-saving alternative treatments. By contrast, an FN--misclassifying a true responder as a non-responder--could deprive a patient of a highly active therapy, potentially shortening their progression-free survival. In future clinical implementation, thresholds will need to be tuned to minimize the more clinically detrimental error type, and decision algorithms could incorporate FP/FN costs (e.g., via cost-sensitive learning) to reflect the asymmetric consequences in advanced cSCC management. Methodological standardization is also essential--covering lesion segmentation (absolute vs. relative SUV thresholds), voxel discretization (fixed-bin number vs. fixed-bin width), and feature aggregation in multifocal disease [30].