Predicting early breast cancer recurrence from histopathological images in the Carolina Breast Cancer Study.
Journal
NPJ breast cancer
ISSN: 2374-4677
Titre abrégé: NPJ Breast Cancer
Pays: United States
ID NLM: 101674891
Informations de publication
Date de publication:
11 Nov 2023
11 Nov 2023
Historique:
received:
22
11
2022
accepted:
20
10
2023
medline:
12
11
2023
pubmed:
12
11
2023
entrez:
11
11
2023
Statut:
epublish
Résumé
Approaches for rapidly identifying patients at high risk of early breast cancer recurrence are needed. Image-based methods for prescreening hematoxylin and eosin (H&E) stained tumor slides could offer temporal and financial efficiency. We evaluated a data set of 704 1-mm tumor core H&E images (2-4 cores per case), corresponding to 202 participants (101 who recurred; 101 non-recurrent matched on age and follow-up time) from breast cancers diagnosed between 2008-2012 in the Carolina Breast Cancer Study. We leveraged deep learning to extract image information and trained a model to identify recurrence. Cross-validation accuracy for predicting recurrence was 62.4% [95% CI: 55.7, 69.1], similar to grade (65.8% [95% CI: 59.3, 72.3]) and ER status (66.3% [95% CI: 59.8, 72.8]). Interestingly, 70% (19/27) of early-recurrent low-intermediate grade tumors were identified by our image model. Relative to existing markers, image-based analyses provide complementary information for predicting early recurrence.
Identifiants
pubmed: 37952058
doi: 10.1038/s41523-023-00597-0
pii: 10.1038/s41523-023-00597-0
pmc: PMC10640636
doi:
Types de publication
Journal Article
Langues
eng
Pagination
92Subventions
Organisme : NCI NIH HHS
ID : U54 CA156733
Pays : United States
Organisme : NCI NIH HHS
ID : P01 CA151135
Pays : United States
Organisme : NCI NIH HHS
ID : P30 CA016086
Pays : United States
Organisme : NCI NIH HHS
ID : P50 CA058223
Pays : United States
Informations de copyright
© 2023. The Author(s).
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