ESPWA: a deep learning-enabled tool for precision-based use of endocrine therapy in resource-limited settings.

ABSTRACT:

Background

Cancer morbidity disproportionately affects patients in low- and middle-income countries (LMICs), where timely and accurate tumor profiling is often nonexistent. Immunohistochemistry-based assessment of estrogen receptor (ER) status, a critical step to guide use of endocrine therapy (ET) in breast cancer, is often delayed or unavailable. As a result, ET is often prescribed empirically, leading to ineffective and toxic treatment for ER-negative patients. To address this unmet need, we developed ESPWA (Estrogen Receptor Status Prediction for Haitian patients using deep learning-enabled histopathology Whole Slide Imaging Analysis), a deep-learning (DL) model that predicts ER status directly from hematoxylin-and-eosin (H&E)-stained w

SUBMITTER: Pulido-Arias D 

PROVIDER: S-EPMC12407906 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature