Toward radiomics for assessment of response to systemic therapies in lung cancer.
computed tomography
immunotherapy
lung cancer
positron emission tomography
prognosis
Journal
Oncotarget
ISSN: 1949-2553
Titre abrégé: Oncotarget
Pays: United States
ID NLM: 101532965
Informations de publication
Date de publication:
22 Dec 2020
22 Dec 2020
Historique:
received:
28
11
2020
accepted:
30
11
2020
entrez:
21
1
2021
pubmed:
22
1
2021
medline:
22
1
2021
Statut:
epublish
Résumé
This editorial comment explains recent developments in radiomics regarding the use of quantitative imaging biomarkers to predict lung cancer sensitivity to a variety of cancer therapies. Tumor response assessment has been a crucial component guiding cancer treatment. Evaluation of treatment response was standardized and classically based on measuring changes in tumor lesion size. Recent breakthroughs in artificial intelligence pave the way for the use of radiomics in tumor response assessment. Such objective techniques would bring a remarkable transformation to conventional methods, which can be inherently subjective. Successful implementation of these technologies would allow for faster and more accurate predictions of treatment efficacy, which will be critical to the advancement of personalized medicine.
Identifiants
pubmed: 33473253
doi: 10.18632/oncotarget.27847
pii: 27847
pmc: PMC7771714
doi:
Types de publication
Journal Article
Langues
eng
Pagination
4677-4680Subventions
Organisme : NCI NIH HHS
ID : U01 CA225431
Pays : United States
Informations de copyright
Copyright: © 2020 Sun et al.
Déclaration de conflit d'intérêts
CONFLICTS OF INTEREST Authors have no conflicts of interest to declare.
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