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@ARTICLE{Sachpekidis:304463,
      author       = {C. Sachpekidis$^*$ and D. Machiraju and D. S. Strauss and
                      L. Pan$^*$ and A. Kopp-Schneider$^*$ and L. Edenbrandt and
                      A. Dimitrakopoulou-Strauss$^*$ and J. C. Hassel},
      title        = {{A}rtificial intelligence-assisted assessment of metabolic
                      response to tebentafusp in metastatic uveal melanoma: a long
                      axial field-of-view [18{F}]{FDG} {PET}/{CT} study.},
      journal      = {European journal of nuclear medicine and molecular imaging},
      volume       = {nn},
      issn         = {1619-7070},
      address      = {Heidelberg [u.a.]},
      publisher    = {Springer-Verl.},
      reportid     = {DKFZ-2025-01856},
      pages        = {nn},
      year         = {2025},
      note         = {#EA:E060#LA:E060# / epub},
      abstract     = {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 $(95\%$ CI) was 14.1 months (12.9 months -
                      not available). Automated TMTV and TLG measurements were
                      successfully obtained in all patients. Elevated baseline
                      TMTV and TLG were significantly associated with shorter OS
                      (TMTV: 16.9 vs. 27.2 months; TLG: 16.9 vs. 27.2 months; p <
                      0.05). Similarly, higher TMTV and TLG at 3 months
                      post-treatment predicted poorer survival outcomes (TMTV:
                      14.3 vs. 24.5 months; TLG: 14.3 vs. 24.5 months; p < 0.05).
                      AI-assisted PERCIST response evaluation identified six
                      patients with disease control (complete metabolic response,
                      partial metabolic response, stable metabolic disease) and
                      nine with progressive metabolic disease. A trend toward
                      improved OS was observed in patients with disease control
                      (24.5 vs. 14.6 months, p = 0.08). Circulating tumor DNA
                      (ctDNA) levels based on GNAQ and GNA11 mutations were
                      available in 8 patients; after 3 months Of tebentafusp
                      treatment, 5 showed reduced Or stable ctDNA levels, and 3
                      showed an increase (median OS: 24.5 vs. 3.3 months; p =
                      0.13). Patients with increasing ctDNA levels exhibited
                      significantly higher TMTV and TLG on follow-up
                      imaging.AI-assisted whole-body quantification of [1⁸F]FDG
                      PET/CT and PERCIST-based response assessment are feasible
                      and hold prognostic significance in tebentafusp-treated mUM.
                      TMTV and TLG may serve as non-invasive imaging biomarkers
                      for risk stratification and treatment monitoring in this
                      malignancy.},
      keywords     = {Artificial intelligence (Other) / CtDNA (Other) / Deep
                      learning (Other) / Metastatic uveal melanoma (Other) /
                      PERCIST (Other) / Tebentafusp (Other) / Total lesion
                      glycolysis (TLG) (Other) / Total metabolic tumor volume
                      (TMTV) (Other) / Treatment response evaluation (Other) /
                      [18F]FDG LAFOV PET/CT (Other)},
      cin          = {E060 / C060},
      ddc          = {610},
      cid          = {I:(DE-He78)E060-20160331 / I:(DE-He78)C060-20160331},
      pnm          = {315 - Bildgebung und Radioonkologie (POF4-315)},
      pid          = {G:(DE-HGF)POF4-315},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:40913640},
      doi          = {10.1007/s00259-025-07504-8},
      url          = {https://inrepo02.dkfz.de/record/304463},
}