Can Whole-Body Baseline CT Radiomics Add Information to the Prediction of Best Response, Progression-Free Survival, and Overall Survival of Stage IV Melanoma Patients Receiving First-Line Targeted Therapy: A Retrospective Register Study.

Date publication

octobre 2023

Journal

Diagnostics (Basel, Switzerland)

Auteurs

Membres identifiés du Cancéropôle Est :
Dr AMARAL Teresa Maria

Tous les auteurs :
Peisen F, Gerken A, Hering A, Dahm I, Nikolaou K, Gatidis S, Eigentler TK, Amaral T, Moltz JH, Othman AE

Lien Pubmed

https://www.ncbi.nlm.nih.gov/pubmed/37892030

Résumé

The aim of this study was to investigate whether the combination of radiomics and clinical parameters in a machine-learning model offers additive information compared with the use of only clinical parameters in predicting the best response, progression-free survival after six months, as well as overall survival after six and twelve months in patients with stage IV malignant melanoma undergoing first-line targeted therapy.

Mots clés

artificial intelligence and machine learning, biomarkers for targeted therapy, imaging biomarkers, melanoma, prognostic biomarkers, targeted therapy

Référence

Diagnostics (Basel). 2023 10 14;13(20):