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

Abstract

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. Methods We propose a two-stage deep learning pipeline for virtual immunohistochemistry. In Stage 1, a regression-based convolutional neural network predicts continuous ER/PR positivity from H&E image patches trained using a mean-squared-error objective; an Edge-Focused Loss was explored as an alternative but was not adopted. In Stage 2, the predicted biomarker percentage is incorporated as a spatial condition into a conditional generative adversarial network (cGAN), enabling the synthesis of virtual IHC images. The framework was evaluated on the IHC4BC dataset using 5-fold patient-level cross-validation. Results The predictive model achieved a mean absolute error (MAE) of 9.14% for ER and 6.10% for PR under 5-fold patient-level cross-validation; treated as a clinical positive/negative classifier it reached an area under the ROC curve of 0.92 for ER and 0.74 for PR. The generative stage produced visually plausible virtual IHC images with biologically meaningful nuclear staining patterns. Although pixel-based metrics (SSIM and PSNR) favored the unconditioned baseline, domain-specific analysis of the deconvolved DAB channel showed substantially improved biomarker-specific staining fidelity for the conditional framework. For PR, the correlation between the generated DAB signal and ground-truth positivity increased from 0.32 (unconditioned baseline) to 0.61 in the deployed pipeline, reaching 0.82 under oracle conditioning, illustrating both the benefit of conditioning and the residual error propagated from Stage 1. Conclusion This proof-of-concept study presents a dual-output computational framework that integrates quantitative biomarker prediction with interpretable virtual staining. The proposed approach may support preliminary biomarker assessment and workflow prioritization, particularly in resource-limited settings. However, external validation and domain-specific evaluation metrics are required before clinical application.