Artificial intelligence for response prediction and personalisation in radiation oncology.
Artificial intelligence
Normal tissue complication probability
Radiotherapy
Treatment response
Tumour control probability
Journal
Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al]
ISSN: 1439-099X
Titre abrégé: Strahlenther Onkol
Pays: Germany
ID NLM: 8603469
Informations de publication
Date de publication:
30 Aug 2024
30 Aug 2024
Historique:
received:
10
05
2024
accepted:
14
07
2024
medline:
31
8
2024
pubmed:
31
8
2024
entrez:
30
8
2024
Statut:
aheadofprint
Résumé
Artificial intelligence (AI) systems may personalise radiotherapy by assessing complex and multifaceted patient data and predicting tumour and normal tissue responses to radiotherapy. Here we describe three distinct generations of AI systems, namely personalised radiotherapy based on pretreatment data, response-driven radiotherapy and dynamically optimised radiotherapy. Finally, we discuss the main challenges in clinical translation of AI systems for radiotherapy personalisation.
Identifiants
pubmed: 39212687
doi: 10.1007/s00066-024-02281-z
pii: 10.1007/s00066-024-02281-z
doi:
Types de publication
Journal Article
Review
Langues
eng
Sous-ensembles de citation
IM
Informations de copyright
© 2024. The Author(s).
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