| Title: |
Normal breast tissue (NBT)-classifiers: advancing compartment classification in normal breast histology. |
| Authors: |
Chen, S; Parreno-Centeno, M; Booker, G; Verghese, G; Mohamed, FS; Arslan, S; Pandya, P; Oozeer, A; D'Angelo, M; Barrow, R; Nelan, R; Sobral-Leite, M; de Martino, F; Brisken, C; Smalley, MJ; Lips, EH; Gillett, C; Jones, LJ; Banerji, CRS; Pinder, SE; Grigoriadis, A |
| Contributors: |
Brisken, Cathrin; Grigoriadis, Anita |
| Publisher Information: |
NATURE PORTFOLIO |
| Publication Year: |
2026 |
| Collection: |
The Institute of Cancer Research (ICR): Publications Repository |
| Subject Geographic: |
United States |
| Description: |
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. |
| Document Type: |
article in journal/newspaper |
| File Description: |
Print-Electronic; application/pdf |
| Language: |
English |
| ISSN: |
2374-4677 |
| Relation: |
npj Breast Cancer, 2026; https://repository.icr.ac.uk/handle/internal/7578 |
| Availability: |
https://repository.icr.ac.uk/handle/internal/7578 |
| Rights: |
http://creativecommons.org/licenses/by/4.0/ |
| Accession Number: |
edsbas.929EC325 |
| Database: |
BASE |