Oops, you're using an old version of your browser so some of the features on this page may not be displaying properly.

MINIMAL Requirements: Google Chrome 24+Mozilla Firefox 20+Internet Explorer 11Opera 15–18Apple Safari 7SeaMonkey 2.15-2.23

Poster viewing and lunch

178P - Deep learning reveals spatial disorganisation of histological features in the normal breast of breast cancer patients

Date

12 May 2023

Session

Poster viewing and lunch

Presenters

Siyuan Chen

Citation

Annals of Oncology (2023) 8 (1suppl_4): 101222-101222. 10.1016/esmoop/esmoop101222

Authors

S. Chen1, M. Parreno-Centeno1, G. Verghese1, G. Booker2, I. Wall3, F. Mohamed1, S. Arslan4, P. Raharja-Liu4, A. Oozeer1, M. D'angelo1, R. Barrow5, R. Nelan5, M. Sobral-Leite6, E. Lips6, F. de Martino7, C. Brisken7, C. Gillett1, L. Jones5, S.E. Pinder8, A. Grigoriadis3

Author affiliations

  • 1 KCL - King's College London, London/GB
  • 2 King's College London Guy's Hospital - NHS Foundation Trust, WC2R 2LS - London/GB
  • 3 King's College London Guy's Hospital - NHS Foundation Trust, London/GB
  • 4 Panakeia Technologies, London/GB
  • 5 Queen Mary University of London, London/GB
  • 6 Netherlands Cancer Institute, Amsterdam/NL
  • 7 EPFL - Ecole Polytechnique Federale de Lausanne - Interfaculty (EPFL-SV-IBI), Lausanne/CH
  • 8 King's College London, Guy's Hospital, London/GB

Resources

Login to get immediate access to this content.

If you do not have an ESMO account, please create one for free.

Abstract 178P

Background

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.

Methods

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 BRCA1/2 alterations derived from risk reduction mastectomy, and the contralateral/ipsilateral tissue from breast cancer patients with known germline pathogenic BRCA1/2 alterations, totalling 282 patients. To characterise the epithelial patterns, 70 WSI were manually annotated. A convolution deep learning (DL)-network was implemented to predict the chronological age group (<30y or >50y) of tiles of epithelium. K-means clustering identified specific histological age-associated features. Class activation map and cellular segmentation were used for interpretation.

Results

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.

Conclusions

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.

Legal entity responsible for the study

The authors.

Funding

KCL-China Scholarship Council (CSC).

Disclosure

All authors have declared no conflicts of interest.

This site uses cookies. Some of these cookies are essential, while others help us improve your experience by providing insights into how the site is being used.

For more detailed information on the cookies we use, please check our Privacy Policy.

Customise settings
  • Necessary cookies enable core functionality. The website cannot function properly without these cookies, and you can only disable them by changing your browser preferences.