PET Radiomics Signatures and Artificial Intelligence for Decoding Immunotherapy Response in Advanced Cutaneous Squamous Cell Carcinoma: A Retrospective Single-Center Study

The aim of this study was to develop a baseline [18F]FDG PET/CT model to predict immunotherapy response in advanced cutaneous squamous cell carcinoma (cSCC) and noninvasively determine tumor grade, thereby enhancing early patient stratification. We retrospectively analyzed 59 patients with histologically confirmed advanced cSCC submitted to immunotherapy with cemiplimab. All underwent [18F]FDG PET/CT at baseline and after approximately 12 weeks. Clinical response was assessed through PET findings integrated with clinical and dermatological evaluation, and patients were classified as responders (complete/partial metabolic response or stable disease) or non-responders (progression or toxicity-related discontinuation). Tumors were also classified as low to intermediate (G1–G2) or poorly differentiated (G3). Machine learning models (Random Forest and Extreme Gradient Boosting) were trained to predict treatment response and tumor grade. Clinical benefit was observed in 46/59 patients (77.9%), while 13 (22.1%) were non-responders. Histology showed 64.4% (n = 38) G1–G2 and 35.6% (n = 21) G3 tumors. The PET-based model best predicted clinical benefit (AUC = 0.96, accuracy = 91% cross-validation; AUC = 0.88, accuracy = 82% internal validation). For tumor grade prediction, the CT-based model achieved a higher AUC of 0.80 (accuracy 73%), whereas the PET-based model reached an AUC of 0.78 but demonstrated a slightly higher accuracy of 77%. Radiomic analysis of baseline [18F]FDG PET enables the discriminative prediction of immunotherapy response and tumor grade in advanced cSCC, with PET-based models outperforming CT-based ones.