Determining breast cancer biomarker status and associated morphological features using deep learning.

ABSTRACT:

Background

Breast cancer management depends on biomarkers including estrogen receptor, progesterone receptor, and human epidermal growth factor receptor 2 (ER/PR/HER2). Though existing scoring systems are widely used and well-validated, they can involve costly preparation and variable interpretation. Additionally, discordances between histology and expected biomarker findings can prompt repeat testing to address biological, interpretative, or technical reasons for unexpected results.

Methods

We developed three independent deep learning systems (DLS) to directly predict ER/PR/HER2 status for both focal tissue regions (patches) and slides using hematoxylin-and-eosin-stained (H&E) images as input. Models were trained and evaluated using pathologist annotated slides from three d

SUBMITTER: Gamble P 

PROVIDER: S-EPMC9037318 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature