ArticleTechnology in cancer research & treatment
Fully Automated Stain Quantification Framework for IHC Whole Slide Images in Breast Cancer.
Article in Technology in cancer research & treatment. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- Neoadjuvant stereotactic body radiation therapy with durvalumab and oleclumab in ERNature medicine · 2026Trial
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9 authors.
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Abstract
IntroductionImmunohistochemistry (IHC) plays a crucial role in breast cancer diagnosis, treatment selection, and research. However, manual scoring of IHC whole slide images (WSIs) is time-consuming and suffers from inter- and intra-observer variability.MethodsTo help address these challenges, we present and publicly release a fully automated, compartment-specific (ie, tumor and stroma) H-scoring framework for IHC analysis. The framework consists of three deep learning modules: tumor-stroma segmentation, nuclei segmentation, and H-score estimation for tumor and stroma. It processes WSIs in minutes, delivering consistent and reproducible H-scores with accuracy comparable to expert pathologists. The modular design also allows flexibility for use in other IHC tasks such as cellularity quantification, and supports configuration options to balance accuracy and computational efficiency.ResultsFine-tuned on 87 expert-annotated patches, the framework achieved a Spearman's rank correlation (
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