Development of model for identifying homologous recombination deficiency (HRD) status of ovarian cancer with deep learning on whole slide images.

ABSTRACT:

Background

Homologous recombination deficiency (HRD) refers to the dysfunction of homologous recombination repair (HRR) at the cellular level. The assessment of HRD status has the important significance for the formulation of treatment plans, efficacy evaluation, and prognosis prediction of patients with ovarian cancer.

Objectives

This study aimed to construct a deep learning-based classifier for identifying tumor regions from whole slide images (WSIs) and stratify the HRD status of patients with ovarian cancer (OC).

Methods

The deep learning models were trained on 205 H&E-stained sections which contained 205 ovarian cancer patients, 64 were found to have HRD status while 141 had homologous recombination proficiency (HRP) status from two institutions Memorial Sloan Ke

SUBMITTER: Zhang K 

PROVIDER: S-EPMC11877705 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature