Development and validation of survival prediction model for gastric adenocarcinoma patients using deep learning: A SEER-based study.

ABSTRACT:

Background

The currently available prediction models, such as the Cox model, were too simplistic to correctly predict the outcome of gastric adenocarcinoma patients. This study aimed to develop and validate survival prediction models for gastric adenocarcinoma patients using the deep learning survival neural network.

Methods

A total of 14,177 patients with gastric adenocarcinoma from the Surveillance, Epidemiology, and End Results (SEER) database were included in the study and randomly divided into the training and testing group with a 7:3 ratio. Two algorithms were chosen to build the prediction models, and both algorithms include random survival forest (RSF) and a deep learning based-survival prediction algorithm (DeepSurv). Also, a traditional Cox proportional hazard (Cox

SUBMITTER: Zeng J 

PROVIDER: S-EPMC10029996 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature