Application of convolutional neural networks to breast biopsies to delineate tissue correlates of mammographic breast density.

ABSTRACT: Breast density, a breast cancer risk factor, is a radiologic feature that reflects fibroglandular tissue content relative to breast area or volume. Its histology is incompletely characterized. Here we use deep learning approaches to identify histologic correlates in radiologically-guided biopsies that may underlie breast density and distinguish cancer among women with elevated and low density. We evaluated hematoxylin and eosin (H&E)-stained digitized images from image-guided breast biopsies (n = 852 patients). Breast density was assessed as global and localized fibroglandular volume (%). A convolutional neural network characterized H&E composition. In total 37 features were extracted from the network output, describing tissue quantities and morphological structure. A random forest

SUBMITTER: Mullooly M 

PROVIDER: S-EPMC6864056 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature