Virtual immunohistochemistry for ER and PR prediction in breast cancer histopathology: a two-stage conditional generative framework

Background

Immunohistochemistry (IHC) remains the gold standard for assessing key breast cancer biomarkers such as estrogen receptor (ER) and progesterone receptor (PR). However, the staining process is resource-intensive, time-consuming, and dependent on specialized laboratory infrastructure. While recent advances in computational pathology have demonstrated the feasibility of predicting biomarker status directly from hematoxylin and eosin (H&E) stained tissue, most models lack visual interpretability, limiting clinical trust and adoption.