TY  - JOUR
AU  - Sachpekidis, Christos
AU  - Machiraju, Devayani
AU  - Strauss, Dimitrios Stefanos
AU  - Pan, Leyun
AU  - Kopp-Schneider, Annette
AU  - Edenbrandt, Lars
AU  - Dimitrakopoulou-Strauss, Antonia
AU  - Hassel, Jessica C
TI  - Artificial intelligence-assisted assessment of metabolic response to tebentafusp in metastatic uveal melanoma: a long axial field-of-view [18F]FDG PET/CT study.
JO  - European journal of nuclear medicine and molecular imaging
VL  - nn
SN  - 1619-7070
CY  - Heidelberg [u.a.]
PB  - Springer-Verl.
M1  - DKFZ-2025-01856
SP  - nn
PY  - 2025
N1  - #EA:E060#LA:E060# / epub
AB  - Tebentafusp has emerged as the first systemic therapy to significantly prolong survival in treatment-naïve HLA-A*02:01 + patients with unresectable or metastatic uveal melanoma (mUM). Notably, a survival benefit has been observed even in the absence of radiographic response. This study aims to investigate the feasibility and prognostic value of artificial intelligence (AI)-assisted quantification and metabolic response assessment of [18F]FDG long axial field-of-view (LAFOV) PET/CT in mUM patients undergoing tebentafusp therapy.Fifteen patients with mUM treated with tebentafusp underwent [18F]FDG LAFOV PET/CT at baseline and 3 months post-treatment. Total metabolic tumor volume (TMTV) and total lesion glycolysis (TLG) were quantified using a deep learning-based segmentation tool On the RECOMIA platform. Metabolic response was assessed according to AI-assisted PERCIST 1.0 criteria. Associations between PET-derived parameters and overall survival (OS) were evaluated using Kaplan-Meier survival analysis.The median follow up (95
KW  - Artificial intelligence (Other)
KW  - CtDNA (Other)
KW  - Deep learning (Other)
KW  - Metastatic uveal melanoma (Other)
KW  - PERCIST (Other)
KW  - Tebentafusp (Other)
KW  - Total lesion glycolysis (TLG) (Other)
KW  - Total metabolic tumor volume (TMTV) (Other)
KW  - Treatment response evaluation (Other)
KW  - [18F]FDG LAFOV PET/CT (Other)
LB  - PUB:(DE-HGF)16
C6  - pmid:40913640
DO  - DOI:10.1007/s00259-025-07504-8
UR  - https://inrepo02.dkfz.de/record/304463
ER  -