Computational pathology to improve biomarker testing in breast cancer: how close are we?
Journal
European journal of cancer prevention : the official journal of the European Cancer Prevention Organisation (ECP)
ISSN: 1473-5709
Titre abrégé: Eur J Cancer Prev
Pays: England
ID NLM: 9300837
Informations de publication
Date de publication:
01 09 2023
01 09 2023
Historique:
medline:
23
10
2023
pubmed:
12
4
2023
entrez:
11
4
2023
Statut:
ppublish
Résumé
The recent advancements in breast cancer precision medicine have highlighted the urgency for the precise and reproducible characterization of clinically actionable biomarkers. Despite numerous standardization efforts, biomarker testing by conventional methodologies is challenged by several issues such as high inter-observer variabilities, the spatial heterogeneity of biomarkers expression, and technological heterogeneity. In this respect, artificial intelligence-based digital pathology approaches are being increasingly recognized as promising methods for biomarker testing and subsequently improved clinical management. Here, we provide an overview on the most recent advances for artificial intelligence-assisted biomarkers testing in breast cancer, with a particular focus on tumor-infiltrating lymphocytes, programmed death-ligand 1, phosphatidylinositol-3 kinase catalytic alpha, and estrogen receptor 1. Challenges and solutions for this integrative analysis in pathology laboratories are also provided.
Identifiants
pubmed: 37038997
doi: 10.1097/CEJ.0000000000000804
pii: 00008469-990000000-00060
doi:
Substances chimiques
Biomarkers
0
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
460-467Informations de copyright
Copyright © 2023 Wolters Kluwer Health, Inc. All rights reserved.
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