PET Radiomics Signatures and Artificial Intelligence for Decoding Immunotherapy Response in Advanced Cutaneous Squamous Cell Carcinoma: A Retrospective Single-Center Study
Abstract
Featured Application
Abstract
1. Introduction
2. Materials and Methods
2.1. Study Design and Patient Population
2.2. PET/CT Imaging
2.3. Imaging Post-Processing and Feature Extraction
2.4. Assessment of Immunotherapy Response
2.5. Follow-Up
2.6. Model Building
2.7. Statistical Analysis
3. Results
3.1. Patient Population
3.2. Response to Immunotherapy
3.3. Radiomic Analysis
3.4. Performance of the Machine Learning Models
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Characteristics | N (Percentage) |
---|---|
Patients | 59 |
Male sex | 51 (86.4%) |
Median age y (interquartile range) | 86 (84–89) |
ECOG | |
0–1 | 47 |
≥2 | 12 |
Tumor Location | |
Head/neck | 52 (88.1%) |
Trunk | 4 (6.7%) |
Lower limbs | 3 (5%) |
Tumor grade | |
G1–G2 | 38 (64.4%) |
G3 | 21 (35.6%) |
Metastatic patients | 8 (13.5%) |
Nodal metastases | 6 (10.1%) |
Distant metastases | 2 (3.3%) |
Immunotherapy with respect to other therapies | |
As 1st line | 46 (77.9%) |
After upfront surgery | 12 (20.3%) |
After upfront RT | 1 (1.7%) |
PET-based response after 12 weeks (PERCIST) | |
CMR | 5 (8.4%) |
PMR | 39 (66.1%) |
SMD | 2 (3.3%) |
PMD | 13 (22%) |
Best clinical response (BCR) to immunotherapy | |
CB | 46 (77.9%) |
NCB | 13 (22.1%) |
Immunotherapy-related toxicity | |
Cardiovascular toxicity | 1 (1.7%) |
Therapeutic-switch after immunotherapy failure | |
Chemotherapy | 2 (3.3%) |
Radiotherapy | 3 (5%) |
Palliative treatments | 8 (13.5%) |
Progression-free survival (median, months) | 14.7 |
Training 10FoldCV | AUC | CA | PRE | SEN | SPE | TP | TN | |
---|---|---|---|---|---|---|---|---|
BCR_CT Model | RF | 0.91 | 0.87 | 0.87 | 0.87 | 0.87 | 82.90% | 90.20% |
XGB | 0.89 | 0.88 | 0.88 | 0.88 | 0.88 | 85.00% | 90.40% | |
BCR_PET Model | RF | 0.96 | 0.91 | 0.91 | 0.91 | 0.92 | 93.60% | 88.90% |
XGB | 0.97 | 0.91 | 0.91 | 0.91 | 0.91 | 91.80% | 90.70% |
Training 10-Fold CV | AUC | CA | PRE | SEN | SPE | TP | TN | |
---|---|---|---|---|---|---|---|---|
GRADE_CT Model | RF | 0.78 | 0.76 | 0.77 | 0.76 | 0.74 | 80.00% | 73.50% |
XGB | 0.83 | 0.76 | 0.76 | 0.76 | 0.75 | 77.30% | 75.00% | |
GRADE_PET Model | RF | 0.77 | 0.70 | 0.71 | 0.70 | 0.69 | 71.40% | 69.70% |
XGB | 0.79 | 0.78 | 0.78 | 0.78 | 0.77 | 78.30% | 77.40% |
Internal Validation | AUC | CA | PRE | SEN | SPE | TP | TN | |
---|---|---|---|---|---|---|---|---|
BCR_CT Model | RF | 0.75 | 0.67 | 0.79 | 0.58 | 0.79 | 78.60% | 57.90% |
XGB | 0.71 | 0.73 | 0.78 | 0.74 | 0.71 | 77.80% | 66.70% | |
BCR_PET Model | RF | 0.88 | 0.82 | 0.85 | 0.82 | 0.86 | 68.80% | 94.10% |
XGB | 0.87 | 0.76 | 0.82 | 0.76 | 0.83 | 61.10% | 93.30% | |
GRADE_CT Model | RF | 0.70 | 0.55 | 0.67 | 0.46 | 0.67 | 66.70% | 46.20% |
XGB | 0.80 | 0.73 | 0.89 | 0.62 | 0.89 | 88.90% | 61.50% | |
GRADE_PET Model | RF | 0.75 | 0.77 | 0.78 | 0.77 | 0.77 | 83.30% | 70.00% |
XGB | 0.78 | 0.77 | 0.81 | 0.77 | 0.81 | 90.00% | 66.70% |
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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
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 StyleManco, 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 StyleManco, 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