Development and validation of a deep learning model for predicting gastric cancer recurrence based on CT Imaging: a multicenter study.

ABSTRACT:

Introduction

The postoperative recurrence of gastric cancer has a significant impact on the overall prognosis of patients. Therefore, accurately predicting the postoperative recurrence of gastric cancer is crucial.

Methods

This retrospective study gathered data from 2,813 gastric cancer patients who underwent radical surgery between 2011 and 2017 at two medical centers. Follow-up was extended until May 2023, and cases were categorized as recurrent or non-recurrent based on postoperative outcomes. Clinical pathological information and imaging data were collected for all patients. A new deep learning signature (DLS) was generated using pretreatment CT images, based on a pre-trained baseline (a customized Resnet50), for predicting postoperative recurrence. The deep learning fus

SUBMITTER: Cao M 

PROVIDER: S-EPMC11634148 | biostudies-literature | 2024 Jun

REPOSITORIES: biostudies-literature