The EU-funded I
Artificial intelligence
Machine learning
Non-small cell lung cancer
Personalized medicine
Predictive biomarkers
Journal
Clinical lung cancer
ISSN: 1938-0690
Titre abrégé: Clin Lung Cancer
Pays: United States
ID NLM: 100893225
Informations de publication
Date de publication:
06 2023
06 2023
Historique:
received:
29
09
2022
revised:
15
02
2023
accepted:
15
02
2023
medline:
30
5
2023
pubmed:
24
3
2023
entrez:
23
3
2023
Statut:
ppublish
Résumé
Although immunotherapy (IO) has changed the paradigm for the treatment of patients with advanced non-small cell lung cancers (aNSCLC), only around 30% to 50% of treated patients experience a long-term benefit from IO. Furthermore, the identification of the 30 to 50% of patients who respond remains a major challenge, as programmed Death-Ligand 1 (PD-L1) is currently the only biomarker used to predict the outcome of IO in NSCLC patients despite its limited efficacy. Considering the dynamic complexity of the immune system-tumor microenvironment (TME) and its interaction with the host's and patient's behavior, it is unlikely that a single biomarker will accurately predict a patient's outcomes. In this scenario, Artificial Intelligence (AI) and Machine Learning (ML) are becoming essential to the development of powerful decision-making tools that are able to deal with this high-complexity and provide individualized predictions to better match treatments to individual patients and thus improve patient outcomes and reduce the economic burden of aNSCLC on healthcare systems. I
Identifiants
pubmed: 36959048
pii: S1525-7304(23)00039-6
doi: 10.1016/j.cllc.2023.02.005
pii:
doi:
Substances chimiques
Biomarkers
0
B7-H1 Antigen
0
Types de publication
Observational Study
Multicenter Study
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
381-387Informations de copyright
Copyright © 2023. Published by Elsevier Inc.