Development and Validation of a Computed Tomography-Based Model for Noninvasive Prediction of the T Stage in Gastric Cancer: Multicenter Retrospective Study.

ABSTRACT:

Background

As part of the TNM (tumor-node-metastasis) staging system, T staging based on tumor depth is crucial for developing treatment plans. Previous studies have constructed a deep learning model based on computed tomographic (CT) radiomic signatures to predict the number of lymph node metastases and survival in patients with resected gastric cancer (GC). However, few studies have reported the combination of deep learning and radiomics in predicting T staging in GC.

Objective

This study aimed to develop a CT-based model for automatic prediction of the T stage of GC via radiomics and deep learning.

Methods

A total of 771 GC patients from 3 centers were retrospectively enrolled and divided into training, validation, and testing cohorts. Patients with GC were classif

SUBMITTER: Tao J 

PROVIDER: S-EPMC11499715 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature