Deciphering the response and resistance to immune-checkpoint inhibitors in lung cancer with artificial intelligence-based analysis: when PIONeeR meets QUANTIC

Joseph Ciccolini; Sébastien Benzekry; Fabrice Barlesi

doi:10.1038/s41416-020-0918-3

Deciphering the response and resistance to immune-checkpoint inhibitors in lung cancer with artificial intelligence-based analysis: when PIONeeR meets QUANTIC

Joseph Ciccolini, Sébastien Benzekry, Fabrice Barlesi

Résultats de recherche: Contribution à un journal › !!Comment/debate

11 Citations (Scopus)

Résumé

This project aims to generate dense longitudinal data in lung cancer patients undergoing anti-PD1/PDL1 therapy. Mathematical modelling with mechanistic learning algorithms will help decipher the mechanisms underlying the response or resistance to immunotherapy. A better understanding of these mechanisms should help identifying actionable items to increase the efficacy of immune-checkpoint inhibitors.

langue originale	Anglais
Pages (de - à)	337-338
Nombre de pages	2
journal	British Journal of Cancer
Volume	123
Numéro de publication	3
Les DOIs	https://doi.org/10.1038/s41416-020-0918-3
état	Publié - 4 août 2020

Accès au document

10.1038/s41416-020-0918-3

Autres fichiers et liens

Lien vers la publication dans Scopus

Contient cette citation

@article{921182f967b749b298f01f1fac9b919c,

title = "Deciphering the response and resistance to immune-checkpoint inhibitors in lung cancer with artificial intelligence-based analysis: when PIONeeR meets QUANTIC",

abstract = "This project aims to generate dense longitudinal data in lung cancer patients undergoing anti-PD1/PDL1 therapy. Mathematical modelling with mechanistic learning algorithms will help decipher the mechanisms underlying the response or resistance to immunotherapy. A better understanding of these mechanisms should help identifying actionable items to increase the efficacy of immune-checkpoint inhibitors.",

author = "Joseph Ciccolini and S{\'e}bastien Benzekry and Fabrice Barlesi",

note = "Publisher Copyright: {\textcopyright} 2020, Cancer Research UK.",

year = "2020",

month = aug,

day = "4",

doi = "10.1038/s41416-020-0918-3",

language = "English",

volume = "123",

pages = "337--338",

journal = "British Journal of Cancer",

issn = "0007-0920",

number = "3",

}

TY - JOUR

T1 - Deciphering the response and resistance to immune-checkpoint inhibitors in lung cancer with artificial intelligence-based analysis

T2 - when PIONeeR meets QUANTIC

AU - Ciccolini, Joseph

AU - Benzekry, Sébastien

AU - Barlesi, Fabrice

PY - 2020/8/4

Y1 - 2020/8/4

N2 - This project aims to generate dense longitudinal data in lung cancer patients undergoing anti-PD1/PDL1 therapy. Mathematical modelling with mechanistic learning algorithms will help decipher the mechanisms underlying the response or resistance to immunotherapy. A better understanding of these mechanisms should help identifying actionable items to increase the efficacy of immune-checkpoint inhibitors.

AB - This project aims to generate dense longitudinal data in lung cancer patients undergoing anti-PD1/PDL1 therapy. Mathematical modelling with mechanistic learning algorithms will help decipher the mechanisms underlying the response or resistance to immunotherapy. A better understanding of these mechanisms should help identifying actionable items to increase the efficacy of immune-checkpoint inhibitors.

UR - http://www.scopus.com/inward/record.url?scp=85088837879&partnerID=8YFLogxK

U2 - 10.1038/s41416-020-0918-3

DO - 10.1038/s41416-020-0918-3

M3 - Comment/debate

C2 - 32541872

AN - SCOPUS:85088837879

SN - 0007-0920

VL - 123

SP - 337

EP - 338

JO - British Journal of Cancer

JF - British Journal of Cancer

IS - 3

ER -