Radiomics and Artificial Intelligence in Uterine Sarcomas: A Systematic Review.
artificial intelligence
deep learning
fibroids
machine learning
radiomics
uterine sarcoma
uterine tumors
Journal
Journal of personalized medicine
ISSN: 2075-4426
Titre abrégé: J Pers Med
Pays: Switzerland
ID NLM: 101602269
Informations de publication
Date de publication:
11 Nov 2021
11 Nov 2021
Historique:
received:
20
08
2021
revised:
28
10
2021
accepted:
09
11
2021
entrez:
27
11
2021
pubmed:
28
11
2021
medline:
28
11
2021
Statut:
epublish
Résumé
Recently, artificial intelligence (AI) with computerized imaging analysis is attracting the attention of clinicians, in particular for its potential applications in improving cancer diagnosis. This review aims to investigate the contribution of radiomics and AI on the radiological preoperative assessment of patients with uterine sarcomas (USs). Our literature review involved a systematic search conducted in the last ten years about diagnosis, staging and treatments with radiomics and AI in USs. The protocol was drafted according to the systematic review and meta-analysis preferred reporting project (PRISMA-P) and was registered in the PROSPERO database (CRD42021253535). The initial search identified 754 articles; of these, six papers responded to the characteristics required for the revision and were included in the final analysis. The predominant technique tested was magnetic resonance imaging. The analyzed studies revealed that even though sometimes complex models included AI-related algorithms, they are still too complex for translation into clinical practice. Furthermore, since these results are extracted by retrospective series and do not include external validations, currently it is hard to predict the chances of their application in different study groups. To date, insufficient evidence supports the benefit of radiomics in USs. Nevertheless, this field is promising but the quality of studies should be a priority in these new technologies.
Sections du résumé
BACKGROUND
BACKGROUND
Recently, artificial intelligence (AI) with computerized imaging analysis is attracting the attention of clinicians, in particular for its potential applications in improving cancer diagnosis. This review aims to investigate the contribution of radiomics and AI on the radiological preoperative assessment of patients with uterine sarcomas (USs).
METHODS
METHODS
Our literature review involved a systematic search conducted in the last ten years about diagnosis, staging and treatments with radiomics and AI in USs. The protocol was drafted according to the systematic review and meta-analysis preferred reporting project (PRISMA-P) and was registered in the PROSPERO database (CRD42021253535).
RESULTS
RESULTS
The initial search identified 754 articles; of these, six papers responded to the characteristics required for the revision and were included in the final analysis. The predominant technique tested was magnetic resonance imaging. The analyzed studies revealed that even though sometimes complex models included AI-related algorithms, they are still too complex for translation into clinical practice. Furthermore, since these results are extracted by retrospective series and do not include external validations, currently it is hard to predict the chances of their application in different study groups.
CONCLUSION
CONCLUSIONS
To date, insufficient evidence supports the benefit of radiomics in USs. Nevertheless, this field is promising but the quality of studies should be a priority in these new technologies.
Identifiants
pubmed: 34834531
pii: jpm11111179
doi: 10.3390/jpm11111179
pmc: PMC8624692
pii:
doi:
Types de publication
Journal Article
Review
Langues
eng
Subventions
Organisme : Fondazione Cassa di Risparmio in Bologna
ID : to AMP
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