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

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 m

SUBMITTER: Vellal AD 

PROVIDER: S-EPMC7898083 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature