Prediction of clinicopathological features, multi-omics events and prognosis based on digital pathology and deep learning in HR+/HER2− breast cancer

ABSTRACT:

Background

Breast cancer has the highest incidence and mortality rates among women worldwide. Hormone receptor (HR)+/human epidermal growth factor receptor 2 (HER2)− breast cancer is the most common molecular subtype, accounting for 50–79% of breast cancers. Deep learning has been widely used in cancer image analysis, especially for predicting targets related to precise treatment and patient prognosis. However, studies focusing on therapeutic target and prognosis predicting in HR+/HER2− breast cancer are lacking.

Methods

This study retrospectively collected hematoxylin and eosin (H&E)-stained slides of HR+/HER2− breast cancer patients between January 2013 and December 2014 at Fudan University Shanghai Cancer Center (FUSCC) and scanned to generate whole-slide images (WSIs)

SUBMITTER: Hu J 

PROVIDER: S-EPMC10267923 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature