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

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.

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Publication Details

Journal
npj Breast Cancer
Published
2026-02-09
DOI
https://doi.org/10.1038/s41523-026-00896-2
Primary Topic
AI in cancer detection
Type
article
Field-Weighted Citation Impact
0.00

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article

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

Rachel Nelan, Marcelo Sobral-Leite, Rachel Barrow, Gregory Verghese et al.
npj Breast Cancer
AI in cancer detection
article

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

Rachel Nelan, Marcelo Sobral-Leite, Rachel Barrow, Gregory Verghese, Christopher R. S. Banerji, Pahini Pandya, Cathrin Brisken, Salim Arslan, Matthew John Smalley, Cheryl Gillett, E. Lips, Mario Parreno-Centeno, Aasiyah Oozeer, Sarah E Pinder, Fabio De Martino, Siyuan Chen, Louise J. Jones, Marcello D’Angelo, Graham Booker, Fathima Sumayya Mohamed
article en

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.

npj Breast CancerVol. 12(1)
University College London Hospitals NHS Foundation Trust (GB), Institute of Cancer Research (GB), Pharos University in Alexandria (EG), Queen Mary University of London (GB), King's College London (GB), Breast Cancer Now (GB), The Netherlands Cancer Institute (NL), London Cancer (GB), Oncode Institute (NL), Kings Health Partners (GB), The Alan Turing Institute (GB), University College London (GB), École Polytechnique Fédérale de Lausanne (CH), Diabetes UK (GB), Cardiff University (GB)
Government of the United Kingdom, Cancer Research UK, Breast Cancer Research Trust, King's College London, King's Health Partners, China Scholarship Council, Medical Research Council
Openalex Percentile: Top 63%
AI in cancer detection
DOIOpen Access PDFOpenAlex
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