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

## TL;DR

This paper introduces NBT-Classifiers, AI tools that accurately classify normal breast tissue compartments in digital slides, aiding early cancer detection.

## Contribution

The novel contribution is the development of robust CNN-based models for precise classification of normal breast tissue compartments in whole slide images.

## Key findings

- NBT-Classifiers achieved AUCs of 0.98–1.00 across three external cohorts for classifying epithelium, stroma, and adipocytes.
- The model learned distinct features of normal tissue that differ from precancerous and cancerous epithelium.
- Integration into a preprocessing pipeline enables efficient analysis of peri-lobular regions for downstream applications.

## 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.