Automated quantification of levels of breast terminal duct lobular (TDLU) involution using deep learning.

ABSTRACT: Convolutional neural networks (CNNs) offer the potential to generate comprehensive quantitative analysis of histologic features. Diagnostic reporting of benign breast disease (BBD) biopsies is usually limited to subjective assessment of the most severe lesion in a sample, while ignoring the vast majority of tissue features, including involution of background terminal duct lobular units (TDLUs), the structures from which breast cancers arise. Studies indicate that increased levels of age-related TDLU involution in BBD biopsies predict lower breast cancer risk, and therefore its assessment may have potential value in risk assessment and management. However, assessment of TDLU involution is time-consuming and difficult to standardize and quantitate. Accordingly, we developed a CNN to enable a

SUBMITTER: de Bel T 

PROVIDER: S-EPMC8770616 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature