18F-FDG PET radiomic analysis to predict outcomes in metastatic melanoma treated with immune checkpoint inhibitors
Karim Amrane, Coline Le Meur, David Bourhis, Christian Berthou, Olivier Pradier, Laurent Misery, Delphine Legoupil, Maxime Etienne, Georges-Philippe Fontaine, Cyril Leleu, Romain Floch, Pierre-Yves Salaun, Ronan Abgral, Vincent Bourbonne

TL;DR
This study uses FDG-PET scans to develop a radiomic model that predicts which metastatic melanoma patients will benefit from immunotherapy.
Contribution
A novel radiomic model (MEL-RAD) based on FDG-PET/CT is developed to predict 1-year progression-free survival in melanoma patients treated with immunotherapy.
Findings
The MEL-RAD model achieved an AUC of 0.74 in predicting 1-year progression-free survival.
Patients with high MEL-RAD scores had significantly worse progression-free and overall survival.
The model was validated across two independent patient cohorts.
Abstract
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. 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…
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Taxonomy
TopicsRadiomics and Machine Learning in Medical Imaging · Cancer Immunotherapy and Biomarkers · Cutaneous Melanoma Detection and Management
