Combination of Whole-Body Baseline CT Radiomics and Clinical Parameters to Predict Response and Survival in a Stage-IV Melanoma Cohort Undergoing Immunotherapy.

Date publication

juin 2022

Journal

Cancers

Auteurs

Membres identifiés du Cancéropôle Est :
Dr AMARAL Teresa Maria

Tous les auteurs :
Peisen F, Hänsch A, Hering A, Brendlin AS, Afat S, Nikolaou K, Gatidis S, Eigentler T, Amaral T, Moltz JH, Othman AE

Lien Pubmed

https://www.ncbi.nlm.nih.gov/pubmed/35740659

Résumé

This study investigated whether a machine-learning-based combination of radiomics and clinical parameters was superior to the use of clinical parameters alone in predicting therapy response after three months, and overall survival after six and twelve months, in stage-IV malignant melanoma patients undergoing immunotherapy with PD-1 checkpoint inhibitors and CTLA-4 checkpoint inhibitors.

Mots clés

artificial intelligence and machine-learning, biomarkers for immunotherapy, checkpoint blockade, imaging biomarkers, melanoma, prognostic biomarkers

Référence

Cancers (Basel). 2022 06 17;14(12):