Radiomics and artificial intelligence in prostate cancer: new tools for molecular hybrid imaging and theragnostics.
Artificial intelligence
Positron emission tomography
Prostate cancer
Radiomics
Theragnostics
Journal
European radiology experimental
ISSN: 2509-9280
Titre abrégé: Eur Radiol Exp
Pays: England
ID NLM: 101721752
Informations de publication
Date de publication:
15 06 2022
15 06 2022
Historique:
received:
18
01
2022
accepted:
20
04
2022
entrez:
14
6
2022
pubmed:
15
6
2022
medline:
18
6
2022
Statut:
epublish
Résumé
In prostate cancer (PCa), the use of new radiopharmaceuticals has improved the accuracy of diagnosis and staging, refined surveillance strategies, and introduced specific and personalized radioreceptor therapies. Nuclear medicine, therefore, holds great promise for improving the quality of life of PCa patients, through managing and processing a vast amount of molecular imaging data and beyond, using a multi-omics approach and improving patients' risk-stratification for tailored medicine. Artificial intelligence (AI) and radiomics may allow clinicians to improve the overall efficiency and accuracy of using these "big data" in both the diagnostic and theragnostic field: from technical aspects (such as semi-automatization of tumor segmentation, image reconstruction, and interpretation) to clinical outcomes, improving a deeper understanding of the molecular environment of PCa, refining personalized treatment strategies, and increasing the ability to predict the outcome. This systematic review aims to describe the current literature on AI and radiomics applied to molecular imaging of prostate cancer.
Identifiants
pubmed: 35701671
doi: 10.1186/s41747-022-00282-0
pii: 10.1186/s41747-022-00282-0
pmc: PMC9198151
doi:
Types de publication
Journal Article
Review
Systematic Review
Langues
eng
Sous-ensembles de citation
IM
Pagination
27Informations de copyright
© 2022. The Author(s) under exclusive licence to European Society of Radiology.
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