Germline BRCA1/2 mutation carriers have an increased risk of developing breast cancer, however the characterisation of histological features of the normal breast for these high-risk patients has not so far been performed on large-scale image collections. Hence, deep learning-based framework is proposed to identify subvisual morphometric phenotypes in normal breast tissue of women with different risk of developing cancer.
Digitised 1190 whole slide images of H&E-stained breast normal tissue images from women across different age groups were collected from 5 different biobanks, i.e., healthy donors, healthy patients derived from reduction mammoplasty, healthy patients with known germline
Our DL-based framework revealed inter and intra-variability of epithelial histological patterns in the normal breast of healthy women, underlying the physiological ageing process. The proportion of lobule types differed in normal breast tissue of <30y or >50y healthy women. In normal breast of younger women, densely packed acinar structures with a higher glandular-to-stromal ratio are predominant and display higher neighbourhood enrichment. Normal breast tissue of young breast cancer patients revealed histological tissue-ageing patterns deviating from their chronological age.
Our DL-framework exposed histological patterns associated with the variation of the underlying ageing process of normal breast tissue, which may indicate early signs of cancerous in high-risk germline BRCA1/2 carriers.
The authors.
KCL-China Scholarship Council (CSC).
All authors have declared no conflicts of interest.