Deep Learning for Automatic Subclassification of Gastric Carcinoma Using Whole-Slide Histopathology Images.

ABSTRACT: Histomorphologic types of gastric cancer (GC) have significant prognostic values that should be considered during treatment planning. Because the thorough quantitative review of a tissue slide is a laborious task for pathologists, deep learning (DL) can be a useful tool to support pathologic workflow. In the present study, a fully automated approach was applied to distinguish differentiated/undifferentiated and non-mucinous/mucinous tumor types in GC tissue whole-slide images from The Cancer Genome Atlas (TCGA) stomach adenocarcinoma dataset (TCGA-STAD). By classifying small patches of tissue images into differentiated/undifferentiated and non-mucinous/mucinous tumor tissues, the relative proportion of GC tissue subtypes can be easily quantified. Furthermore, the distribution of different

SUBMITTER: Jang HJ 

PROVIDER: S-EPMC8345042 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature