Evidence map›Paper›PMID 41822493›Full record

ArticleFrontiers in immunology2026

Karim Amrane, Coline Le Meur, David Bourhis, Christian Berthou, Olivier Pradier, Laurent Misery, Delphine Legoupil, Maxime Etienne, Georges-Philippe Fontaine, Cyril Leleu and 4 more

Abstract read
In one paragraph

Article in Frontiers in immunology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

14 authors.

Karim Amrane *Department of Oncology, Regional Hospital of Morlaix, Morlaix, France.
Coline Le Meur *Department of Radiotherapy, University Hospital of Brest, Brest, France.
David BourhisDepartment of Nuclear Medicine, University Hospital of Brest, Brest, France.
Christian BerthouLBAI, UMR1227, Univ Brest, Inserm, Brest, France.
Olivier PradierDepartment of Radiotherapy, University Hospital of Brest, Brest, France.
Laurent MiseryDepartment of Dermatology, University Hospital of Brest, Brest, France.
Delphine LegoupilDepartment of Dermatology, University Hospital of Brest, Brest, France.
Maxime EtienneDepartment of Dermatology Centre Hospitalier de Cornouaille, Quimper, France.
Georges-Philippe FontaineNuclear Medicine Centre Georges Charpak, Quimper, France.
Cyril LeleuDepartment of Radiotherapy, Centre Hospitalier de Cornouaille, Quimper, France.
Romain FlochDepartment of Nuclear Medicine, University Hospital of Brest, Brest, France.
Pierre-Yves SalaunDepartment of Nuclear Medicine, University Hospital of Brest, Brest, France.
Ronan AbgralDepartment of Nuclear Medicine, University Hospital of Brest, Brest, France.
Vincent BourbonneDepartment of Radiotherapy, University Hospital of Brest, Brest, France.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: Cutaneous melanoma (CM) incidence is rising, and despite advances in immune checkpoint inhibitors (ICI), many metastatic patients do not respond or develop resistance. This study aimed to evaluate the prognostic value of a pre-treatment FDG-PET/CT-based radiomic model (MEL-RAD) for predicting 1-year progression-free survival (1y-PFS) in metastatic CM patients treated with first-line ICI. Methods: We retrospectively included 154 metastatic CM patients from two centers who underwent pre-treatment FDG-PET/CT before ICI initiation. Patients were split into a development cohort (n=95) and an independent testing cohort (n=59). Radiomic features were extracted and harmonized to reduce inter-cohort variability. A two-step feature selection identified three key wavelet-transformed texture features used to build the MEL-RAD predictive model. The model's performance was assessed by receiver operating characteristic (ROC) analysis, sensitivity, specificity, and predictive values. Survival analyses (progression-free (PFS) and overall survival (OS)) were performed with Cox regression and Kaplan-Meier methods. Results: In the development cohort, MEL-RAD achieved an AUC of 0.74 (p<0.0001) for predicting 1y-PFS. Using a 55% probability threshold, sensitivity was 93.8%, specificity 31.9%, with positive and negative predictive values of 58.4% and 83.4%, respectively. Patients with MEL-RAD >55% had significantly worse PFS (HR = 2.73, p=0.0009) and OS (HR = 3.20, p=0.0003). These results were externally validated: in the testing cohort, MEL-RAD positivity remained significantly associated with poorer PFS (HR = 2.73, p=0.047), and showed non-significant for OS. Conclusion: The MEL-RAD radiomic model based on pre-treatment FDG-PET/CT offers a non-invasive biomarker to stratify metastatic CM patients treated with immunotherapy.

Indexed as

Fluorodeoxyglucose F18Immune Checkpoint InhibitorsMelanomaPositron Emission Tomography Computed TomographySkin NeoplasmsAgedFemaleHumansMaleMiddle AgedNeoplasm MetastasisPrognosisRadiomicsRadiopharmaceuticalsRetrospective StudiesTreatment OutcomeFluorodeoxyglucose F18Immune Checkpoint InhibitorsRadiopharmaceuticalsBRAFFDG-PETimmune checkpoint inhibitionmelanomaNRASradiomic

Identifiers

PMID41822493
PMCPMC12975435

What OpenQuestion holds

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Registered trials

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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.