Biobanking in the digital pathology era.
Biobank
Biomarkers
Digital pathology
Pathology
Translational research
Journal
Oncology research
ISSN: 1555-3906
Titre abrégé: Oncol Res
Pays: United States
ID NLM: 9208097
Informations de publication
Date de publication:
2021
2021
Historique:
received:
12
06
2022
accepted:
29
07
2022
medline:
13
6
2023
pubmed:
12
6
2023
entrez:
12
6
2023
Statut:
epublish
Résumé
Digital Pathology is becoming more and more important to achieve the goal of precision medicine. Advances in whole-slide imaging, software integration, and the accessibility of storage solutions have changed the pathologists' clinical practice, not only in terms of laboratory workflow but also for diagnosis and biomarkers analysis. In parallel with the pathology setting advancement, translational medicine is approaching the unprecedented opportunities unrevealed by artificial intelligence (AI). Indeed, the increased usage of biobanks' datasets in research provided new challenges for AI applications, such as advanced algorithms, and computer-aided techniques. In this scenario, machine learning-based approaches are being propose in order to improve biobanks from biospecimens collection repositories to computational datasets. To date, evidence on how to implement digital biobanks in translational medicine is still lacking. This viewpoint article summarizes the currently available literature that supports the biobanks' role in the digital pathology era, and to provide possible practical applications of digital biobanks.
Identifiants
pubmed: 37303941
doi: 10.32604/or.2022.024892
pii: 24892
pmc: PMC10207967
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
229-233Informations de copyright
© 2021 Bonizzi.
Déclaration de conflit d'intérêts
The authors declare that they have no conflicts of interest to report regarding the present study.
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