Normal breast tissue (NBT)-classifiers: advancing compartment classification in normal breast histology

Chen, Siyuan, Parreno-Centeno, Mario, Booker, Graham, Verghese, Gregory, Mohamed, Fathima Sumayya, Arslan, Salim, Pandya, Pahini, Oozeer, Aasiyah, D'Angelo, Marcello, Barrow, Rachel, Nelan, Rachel, Sobral-Leite, Marcelo, De Martino, Fabio, Briskin, Cathrin and Smalley, Matthew ORCID: https://orcid.org/0000-0001-9540-1146 2026. Normal breast tissue (NBT)-classifiers: advancing compartment classification in normal breast histology. npj Breast Cancer 10.1038/s41523-026-00896-2

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Abstract

Cancer research emphasises early detection, yet quantitative methods for normal tissue analysis remain limited. Digitised haematoxylin and eosin (H&E)-stained slides enable computational histopathology, but artificial intelligence (AI)-based analysis of normal breast tissue (NBT) in whole slide images (WSIs) remains scarce. We curated 70 WSIs of NBTs from multiple sources and cohorts with pathologist-guided manual annotations of epithelium, stroma, and adipocytes (https://github.com/cancerbioinformatics/OASIS). We developed robust convolutional neural network (CNN)-based, patch-level classification models, named NBT-Classifiers, to tessellate and classify NBTs at different scales. Across three external cohorts, NBT-Classifiers trained on 128 × 128 µm and 256 × 256 µm patches achieved AUCs of 0.98-1.00. The model learned independent normal features different from those of precancerous and cancerous epithelium, which were further visualised using two explainable AI techniques. When integrated into an end-to-end preprocessing pipeline, NBT-Classifiers facilitate efficient downstream analysis within peri-lobular regions. NBT-Classifiers provide robust compartment-specific analytical tools and enhance our understanding of NBT appearances, which serve as valuable reference points for identifying premalignant changes and guiding early breast cancer prevention strategies. [Abstract copyright: © 2026. The Author(s).]

Item Type:	Article
Date Type:	Published Online
Status:	In Press
Schools:	Schools > Biosciences
Additional Information:	Additional authors: Esther H. Lips, Cheryl Gillett, Louise J. Jones, Christopher R. S. Banerji, Sarah E. Pinder & Anita Grigoriadis
Publisher:	Nature Research
ISSN:	2374-4677
Date of First Compliant Deposit:	26 February 2026
Date of Acceptance:	14 January 2026
Last Modified:	26 Feb 2026 12:45
URI:	https://orca.cardiff.ac.uk/id/eprint/185336

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