Applications of artificial intelligence in interventional oncology: An up-to-date review of the literature.
Ablation
Artificial intelligence
Embolization
Interventional radiology
Machine learning
Oncology
Journal
Japanese journal of radiology
ISSN: 1867-108X
Titre abrégé: Jpn J Radiol
Pays: Japan
ID NLM: 101490689
Informations de publication
Date de publication:
02 Oct 2024
02 Oct 2024
Historique:
received:
20
08
2024
accepted:
23
09
2024
medline:
2
10
2024
pubmed:
2
10
2024
entrez:
2
10
2024
Statut:
aheadofprint
Résumé
Interventional oncology provides image-guided therapies, including transarterial tumor embolization and percutaneous tumor ablation, for malignant tumors in a minimally invasive manner. As in other medical fields, the application of artificial intelligence (AI) in interventional oncology has garnered significant attention. This narrative review describes the current state of AI applications in interventional oncology based on recent literature. A literature search revealed a rapid increase in the number of studies relevant to this topic recently. Investigators have attempted to use AI for various tasks, including automatic segmentation of organs, tumors, and treatment areas; treatment simulation; improvement of intraprocedural image quality; prediction of treatment outcomes; and detection of post-treatment recurrence. Among these, the AI-based prediction of treatment outcomes has been the most studied. Various deep and conventional machine learning algorithms have been proposed for these tasks. Radiomics has often been incorporated into prediction and detection models. Current literature suggests that AI is potentially useful in various aspects of interventional oncology, from treatment planning to post-treatment follow-up. However, most AI-based methods discussed in this review are still at the research stage, and few have been implemented in clinical practice. To achieve widespread adoption of AI technologies in interventional oncology procedures, further research on their reliability and clinical utility is necessary. Nevertheless, considering the rapid research progress in this field, various AI technologies will be integrated into interventional oncology practices in the near future.
Identifiants
pubmed: 39356439
doi: 10.1007/s11604-024-01668-3
pii: 10.1007/s11604-024-01668-3
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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