%0 Journal Article
%A Sachpekidis, Christos
%A Machiraju, Devayani
%A Strauss, Dimitrios Stefanos
%A Pan, Leyun
%A Kopp-Schneider, Annette
%A Edenbrandt, Lars
%A Dimitrakopoulou-Strauss, Antonia
%A Hassel, Jessica C
%T Artificial intelligence-assisted assessment of metabolic response to tebentafusp in metastatic uveal melanoma: a long axial field-of-view [18F]FDG PET/CT study.
%J European journal of nuclear medicine and molecular imaging
%V nn
%@ 1619-7070
%C Heidelberg [u.a.]
%I Springer-Verl.
%M DKFZ-2025-01856
%P nn
%D 2025
%Z #EA:E060#LA:E060# / epub
%X 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
%K Artificial intelligence (Other)
%K CtDNA (Other)
%K Deep learning (Other)
%K Metastatic uveal melanoma (Other)
%K PERCIST (Other)
%K Tebentafusp (Other)
%K Total lesion glycolysis (TLG) (Other)
%K Total metabolic tumor volume (TMTV) (Other)
%K Treatment response evaluation (Other)
%K [18F]FDG LAFOV PET/CT (Other)
%F PUB:(DE-HGF)16
%9 Journal Article
%$ pmid:40913640
%R 10.1007/s00259-025-07504-8
%U https://inrepo02.dkfz.de/record/304463