Preoperative prediction value of 2.5D deep learning model based on contrast-enhanced CT for lymphovascular invasion of gastric cancer

Preoperative prediction value of 2.5D deep learning model based on contrast-enhanced CT for lymphovascular invasion of gastric cancer

Abstract To develop and validate artificial intelligence models based on contrast-enhanced CT(CECT) images of venous phase using deep learning (DL) and Radiomics approaches to predict lymphovascular invasion in gastric cancer prior to surgery. We retrospectively analyzed data from 351 gastric cancer...

Full description

Saved in:
Bibliographic Details
Main Authors: Xiao Sun, Pei Wang, Rui Ding, Li Ma, Huairong Zhang, Li Zhu
Format: Article
Language:English
Published: Nature Portfolio 2025-07-01
Series:Scientific Reports
Subjects:
Lymphovascular invasion
Gastric cancer
Deep learning
Radiomics
Tomography (X-ray computed)
Online Access:https://doi.org/10.1038/s41598-025-11427-7
Tags: Add Tag
No Tags, Be the first to tag this record!

Internet

https://doi.org/10.1038/s41598-025-11427-7

Similar Items