[Artificial intelligence, radiomics and pathomics to predict response and survival of patients treated with radiations].
Intelligence artificielle en radiothérapie : radiomique, pathomique, et prédiction de la survie et de la réponse aux traitements.
Artificial intelligence
Dosiomics
Dosiomique
Intelligence artificielle
Pathomics
Pathomique
Radiomics
Radiomique
Radiotherapy
Radiothérapie
Journal
Cancer radiotherapie : journal de la Societe francaise de radiotherapie oncologique
ISSN: 1769-6658
Titre abrégé: Cancer Radiother
Pays: France
ID NLM: 9711272
Informations de publication
Date de publication:
Oct 2021
Oct 2021
Historique:
received:
08
06
2021
accepted:
19
06
2021
pubmed:
22
7
2021
medline:
1
10
2021
entrez:
21
7
2021
Statut:
ppublish
Résumé
Artificial intelligence approaches in medicine are more and more used and are extremely promising due to the growing number of data produced and the variety of data they allow to exploit. Thus, the computational analysis of medical images in particular, radiological (radiomics), or anatomopathological (pathomics), has shown many very interesting results for the prediction of the prognosis and the response of cancer patients. Radiotherapy is a discipline that particularly benefits from these new approaches based on computer science and imaging. This review will present the main principles of an artificial intelligence approach and in particular machine learning, the principles of a radiomic and pathomic approach and the potential of their use for the prediction of the prognosis of patients treated with radiotherapy.
Identifiants
pubmed: 34284970
pii: S1278-3218(21)00126-8
doi: 10.1016/j.canrad.2021.06.027
pii:
doi:
Substances chimiques
Biomarkers, Tumor
0
Types de publication
Journal Article
Review
Langues
fre
Sous-ensembles de citation
IM
Pagination
630-637Informations de copyright
Copyright © 2021 Société française de radiothérapie oncologique (SFRO). Published by Elsevier Masson SAS. All rights reserved.