18FDG PET/CT and Machine Learning for the prediction of lung cancer response to immunotherapy

In patients with non-small cell lung cancer (NSCLC) treated with immunotherapy, individual biological and PET imaging prognostic biomarkers have been recently identified. However, combination of biomarkers has not been studied yet. The purpose of this study is to combine clinical, biological and 18FDG PET/CT parameters and use machine-learning algorithms to build more accurate prognostic models of NSCLC response to immunotherapy

Domaines

Informatique [cs] Sciences du Vivant [q-bio]

Hugo Schmutz : Connectez-vous pour contacter le contributeur

https://hal.science/hal-03876074

Soumis le : lundi 28 novembre 2022-16:37:29

Dernière modification le : jeudi 14 novembre 2024-16:30:02

Dates et versions

hal-03876074 , version 1 (28-11-2022)

Identifiants

HAL Id : hal-03876074 , version 1
DOI : 10.1007/s00259-022-05924-4

Citer

Hugo Schmutz, Pierre-Alexandre Mattei, Sara Contu, David Chardin, Olivier Humbert. 18FDG PET/CT and Machine Learning for the prediction of lung cancer response to immunotherapy. EANM 2022 - 35th Annual Congres - Annual Congress of the European Association of Nuclear Medicine, Oct 2022, Barcelona, Spain. pp.245, ⟨10.1007/s00259-022-05924-4⟩. ⟨hal-03876074⟩

Exporter

BibTeX XML-TEI Dublin Core DC Terms EndNote DataCite

Collections

CEA CNRS INRIA I3S INSMI DIEUDONNE INRIA2 CEA-UPSAY UNIV-PARIS-SACLAY UNIV-COTEDAZUR JOLIOT CEA-DRF SHFJ

107 Consultations

0 Téléchargements