Annotation-free deep learning algorithm trained on hematoxylin & eosin images predicts epithelial-to-mesenchymal transition phenotype and endocrine response in estrogen receptor-positive breast cancer

Annotation-free deep learning algorithm trained on hematoxylin & eosin images predicts epithelial-to-mesenchymal transition phenotype and endocrine response in estrogen receptor-positive breast cancer

Abstract Recent evidence indicates that endocrine resistance in estrogen receptor-positive (ER+) breast cancer is closely correlated with phenotypic characteristics of epithelial-to-mesenchymal transition (EMT). Nonetheless, identifying tumor tissues with a mesenchymal phenotype remains challenging...

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Bibliographic Details
Main Authors: Kaimin Hu, Yinan Wu, Yajing Huang, Meiqi Zhou, Yanyan Wang, Xingru Huang
Format: Article
Language:English
Published: BMC 2025-01-01
Series:Breast Cancer Research
Subjects:
Deep learning
Estrogen receptor-positive breast cancer
Epithelial-to-mesenchymal transition
Digital pathology
Phenotypic classifier
Endocrine therapy response
Online Access:https://doi.org/10.1186/s13058-025-01959-1
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https://doi.org/10.1186/s13058-025-01959-1

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