Evidence map›Paper›PMID 40913640›Full record

ArticleEuropean journal of nuclear medicine and molecular imaging2026

Artificial intelligence-assisted assessment of metabolic response to tebentafusp in metastatic uveal melanoma: a long axial field-of-view [

Christos Sachpekidis, Devayani Machiraju, Dimitrios Stefanos Strauss, Leyun Pan, Annette Kopp-Schneider, Lars Edenbrandt, Antonia Dimitrakopoulou-Strauss, Jessica C Hassel

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Article in European journal of nuclear medicine and molecular imaging, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Christos SachpekidisClinical Cooperation Unit Nuclear Medicine, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69210, Heidelberg, Germany. c.sachpekidis@dkfz-heidelberg.de.ORCID 0000-0001-8739-8741
Devayani MachirajuDepartment of Dermatology and National Center for Tumor Diseases (NCT), University Hospital Heidelberg, Heidelberg, Germany.
Dimitrios Stefanos StraussDepartment of Diagnostic and Interventional Radiology, University Hospital Heidelberg, Heidelberg, Germany.
Leyun PanClinical Cooperation Unit Nuclear Medicine, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69210, Heidelberg, Germany.
Annette Kopp-SchneiderDivision of Biostatistics, German Cancer Research Center (DKFZ), Heidelberg, Germany.
Lars EdenbrandtDepartment of Clinical Physiology, Region Västra Götaland, Sahlgrenska University Hospital, Gothenburg, Sweden.
Antonia Dimitrakopoulou-StraussClinical Cooperation Unit Nuclear Medicine, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69210, Heidelberg, Germany.
Jessica C HasselDepartment of Dermatology and National Center for Tumor Diseases (NCT), University Hospital Heidelberg, Heidelberg, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeTebentafusp 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 [ MATERIALS AND

methodsFifteen patients with mUM treated with tebentafusp underwent [

resultsThe 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.

conclusionAI-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.

Indexed as

Artificial IntelligenceFluorodeoxyglucose F18MelanomaPositron Emission Tomography Computed TomographyUveal NeoplasmsAdultAgedFemaleHumansMaleMiddle AgedNeoplasm MetastasisTreatment OutcomeUveal MelanomaFluorodeoxyglucose F18[18F]FDG LAFOV PET/CTArtificial intelligenceCtDNADeep learningMetastatic uveal melanomaPERCISTTebentafuspTotal lesion glycolysis (TLG)Total metabolic tumor volume (TMTV)Treatment response evaluation

Identifiers

PMID40913640
PMCPMC12860866

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.