A systematic review of data sources for artificial intelligence applications in pediatric brain tumors in Europe: implications for bias and generalizability.
CNS tumors
artificial intelligence
childhood cancer
data sharing
pediatric oncology
Journal
Frontiers in oncology
ISSN: 2234-943X
Titre abrégé: Front Oncol
Pays: Switzerland
ID NLM: 101568867
Informations de publication
Date de publication:
2023
2023
Historique:
received:
30
08
2023
accepted:
16
10
2023
medline:
28
11
2023
pubmed:
28
11
2023
entrez:
28
11
2023
Statut:
epublish
Résumé
Europe works to improve cancer management through the use of artificialintelligence (AI), and there is a need to accelerate the development of AI applications for childhood cancer. However, the current strategies used for algorithm development in childhood cancer may have bias and limited generalizability. This study reviewed existing publications on AI tools for pediatric brain tumors, Europe's most common type of childhood solid tumor, to examine the data sources for developing AI tools. We performed a bibliometric analysis of the publications on AI tools for pediatric brain tumors, and we examined the type of data used, data sources, and geographic location of cohorts to evaluate the generalizability of the algorithms. We screened 10503 publications, and we selected 45. A total of 34/45 publications developing AI tools focused on glial tumors, while 35/45 used MRI as a source of information to predict the classification and prognosis. The median number of patients for algorithm development was 89 for single-center studies and 120 for multicenter studies. A total of 17/45 publications used pediatric datasets from the UK. Since the development of AI tools for pediatric brain tumors is still in its infancy, there is a need to support data exchange and collaboration between centers to increase the number of patients used for algorithm training and improve their generalizability. To this end, there is a need for increased data exchange and collaboration between centers and to explore the applicability of decentralized privacy-preserving technologies consistent with the General Data Protection Regulation (GDPR). This is particularly important in light of using the European Health Data Space and international collaborations.
Identifiants
pubmed: 38016063
doi: 10.3389/fonc.2023.1285775
pmc: PMC10646175
doi:
Types de publication
Systematic Review
Langues
eng
Pagination
1285775Informations de copyright
Copyright © 2023 Tozzi, Croci, Voicu, Dotta, Colafati, Carai, Fabozzi, Lacanna, Premuselli and Mastronuzzi.
Déclaration de conflit d'intérêts
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
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