Predicting early breast cancer recurrence from histopathological images in the Carolina Breast Cancer Study.
NPJ Breast Cancer
; 9(1): 92, 2023 Nov 11.
Article
in En
| MEDLINE
| ID: mdl-37952058
ABSTRACT
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.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Language:
En
Journal:
NPJ Breast Cancer
Year:
2023
Document type:
Article
Affiliation country: