Deep learning-based subtyping of gastric cancer histology predicts clinical outcome: a multi-institutional retrospective study.

ABSTRACT:

Introduction

The Laurén classification is widely used for Gastric Cancer (GC) histology subtyping. However, this classification is prone to interobserver variability and its prognostic value remains controversial. Deep Learning (DL)-based assessment of hematoxylin and eosin (H&E) stained slides is a potentially useful tool to provide an additional layer of clinically relevant information, but has not been systematically assessed in GC.

Objective

We aimed to train, test and externally validate a deep learning-based classifier for GC histology subtyping using routine H&E stained tissue sections from gastric adenocarcinomas and to assess its potential prognostic utility.

Methods

We trained a binary classifier on intestinal and diffuse type GC whole slide images for a sub

SUBMITTER: Veldhuizen GP 

PROVIDER: S-EPMC10361890 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature