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Applied Sciences
Volume 15
Issue 12
10.3390/app15126453
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

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

by
Luigi Manco
1,†,
Ilaria Proietti
2,†,
Giovanni Scribano
1,
Riccardo Pirisino
3,
Oreste Bagni
3,
Concetta Potenza
2,
Giovanni Pellacani
4 and
Luca Filippi
5,*
1
Medical Physics Unit, University Hospital of Ferrara, 44124 Ferrara, Italy
2
Dermatology Unit “Daniele Innocenzi”, “A. Fiorini” Hospital, Via Firenze, 1, 04019 Terracina, Italy
3
Department of Nuclear Medicine, Santa Maria Goretti Hospital, AUSL Latina, 04100 Latina, Italy
4
Department of Clinical Internal, Anesthesiological and Cardiovascular Sciences, Dermatology Clinic, Sapienza University, 00184 Rome, Italy
5
Department of Biomedicine and Prevention, University of Rome “Tor Vergata”, Via Montpellier 1, 00133 Rome, Italy
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Appl. Sci. 2025, 15(12), 6453; https://doi.org/10.3390/app15126453 (registering DOI)
Submission received: 7 May 2025 / Revised: 31 May 2025 / Accepted: 5 June 2025 / Published: 8 June 2025
(This article belongs to the Special Issue Emerging Topics in Precision Medicine: Non-invasive Innovations Shaping Cancer and Immunotherapy Progress)
Versions Notes

Featured Application

Radiomic analysis of baseline [¹⁸F]FDG PET/CT scans may offer a non-invasive tool to predict immunotherapy response and tumor grade in patients with advanced cutaneous squamous cell carcinoma. This approach could support clinical decision making by identifying likely responders prior to treatment initiation and tailoring management strategies based on tumor differentiation.

Abstract

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.
Keywords: immunotherapy; skin tumors; cutaneous squamous cell carcinoma; PET/CT; radiomics; artificial intelligence immunotherapy; skin tumors; cutaneous squamous cell carcinoma; PET/CT; radiomics; artificial intelligence

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MDPI and ACS Style

Manco, L.; Proietti, I.; Scribano, G.; Pirisino, R.; Bagni, O.; Potenza, C.; Pellacani, G.; Filippi, L. PET Radiomics Signatures and Artificial Intelligence for Decoding Immunotherapy Response in Advanced Cutaneous Squamous Cell Carcinoma: A Retrospective Single-Center Study. Appl. Sci. 2025, 15, 6453. https://doi.org/10.3390/app15126453

AMA Style

Manco L, Proietti I, Scribano G, Pirisino R, Bagni O, Potenza C, Pellacani G, Filippi L. PET Radiomics Signatures and Artificial Intelligence for Decoding Immunotherapy Response in Advanced Cutaneous Squamous Cell Carcinoma: A Retrospective Single-Center Study. Applied Sciences. 2025; 15(12):6453. https://doi.org/10.3390/app15126453

Chicago/Turabian Style

Manco, Luigi, Ilaria Proietti, Giovanni Scribano, Riccardo Pirisino, Oreste Bagni, Concetta Potenza, Giovanni Pellacani, and Luca Filippi. 2025. "PET Radiomics Signatures and Artificial Intelligence for Decoding Immunotherapy Response in Advanced Cutaneous Squamous Cell Carcinoma: A Retrospective Single-Center Study" Applied Sciences 15, no. 12: 6453. https://doi.org/10.3390/app15126453

APA Style

Manco, L., Proietti, I., Scribano, G., Pirisino, R., Bagni, O., Potenza, C., Pellacani, G., & Filippi, L. (2025). PET Radiomics Signatures and Artificial Intelligence for Decoding Immunotherapy Response in Advanced Cutaneous Squamous Cell Carcinoma: A Retrospective Single-Center Study. Applied Sciences, 15(12), 6453. https://doi.org/10.3390/app15126453

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