Deep Learning Image Analysis of Benign Breast Disease to Identify Subsequent Risk of Breast Cancer

Author:

Vellal Adithya D1,Sirinukunwattan Korsuk1234,Kensler Kevin H5ORCID,Baker Gabrielle M1,Stancu Andreea L1ORCID,Pyle Michael E1,Collins Laura C1,Schnitt Stuart J6,Connolly James L1,Veta Mitko7,Eliassen A Heather89ORCID,Tamimi Rulla M8910,Heng Yujing J1

Affiliation:

1. Department of Pathology, Harvard Medical School, Beth Israel Deaconess Medical Center, Boston, MA, USA

2. Department of Engineering Science, Institute of Biomedical Engineering (IBME), University of Oxford, Oxford, UK

3. Big Data Institute, University of Oxford, Li Ka Shing Centre for Health Information and Discovery, Oxford, UK

4. NIHR Oxford Biomedical Research Centre, Oxford University NHS Foundation Trust, Oxford, UK

5. Division of Population Sciences, Dana Farber Cancer Institute, Boston, MA, USA

6. Dana-Farber/Brigham and Women's Cancer Center, Harvard Medical School, Dana-Farber Cancer Institute-Brigham and Women's Hospital, Boston, MA, USA

7. Medical Image Analysis Group, Eindhoven University of Technology, Eindhoven, the Netherlands

8. Channing Division of Network Medicine, Department of Medicine, Harvard Medical School, Brigham and Women's Hospital, Boston, MA, USA

9. Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA

10. Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA

Abstract

Abstract Background New biomarkers of risk may improve breast cancer (BC) risk prediction. We developed a computational pathology method to segment benign breast disease (BBD) whole slide images into epithelium, fibrous stroma, and fat. We applied our method to the BBD BC nested case-control study within the Nurses’ Health Studies to assess whether computer-derived tissue composition or a morphometric signature was associated with subsequent risk of BC. Methods Tissue segmentation and nuclei detection deep-learning networks were established and applied to 3795 whole slide images from 293 cases who developed BC and 1132 controls who did not. Percentages of each tissue region were calculated, and 615 morphometric features were extracted. Elastic net regression was used to create a BC morphometric signature. Associations between BC risk factors and age-adjusted tissue composition among controls were assessed using analysis of covariance. Unconditional logistic regression, adjusting for the matching factors, BBD histological subtypes, parity, menopausal status, and body mass index evaluated the relationship between tissue composition and BC risk. All statistical tests were 2-sided. Results Among controls, direction of associations between BBD subtypes, parity, and number of births with breast composition varied by tissue region; select regions were associated with childhood body size, body mass index, age of menarche, and menopausal status (all P < .05). A higher proportion of epithelial tissue was associated with increased BC risk (odds ratio = 1.39, 95% confidence interval = 0.91 to 2.14, for highest vs lowest quartiles, Ptrend = .047). No morphometric signature was associated with BC. Conclusions The amount of epithelial tissue may be incorporated into risk assessment models to improve BC risk prediction.

Funder

National Cancer Institute of the National Institutes of Health

Breast Cancer Research Foundation

Klarman Family Foundation

Beth Israel Deaconess Medical Center High School Summer Research Program

Publisher

Oxford University Press (OUP)

Subject

Cancer Research,Oncology

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