Sign in

NCT07190040Completed

Integrating Multi-Omics Data for Enhanced Prognosis Prediction in Gastric Cancer Post-Neoadjuvant Therapy

Source: ClinicalTrials.gov · Last updated 24 Sept 2025

Brief summary (registry, in English)

Study Protocol: Integrating Multi-Omics Data for Prognosis Prediction in Gastric Cancer Post-Neoadjuvant Therapy Objective: To develop and validate an integrative prognostic nomogram for patients with locally advanced gastric cancer (LAGC) undergoing neoadjuvant therapy, combining deep learning-derived radiomic features (DeepScore), transcriptome-based immune scores (ImmuneScore), and ypTNM staging. Study Design: A retrospective, single-center cohort study. Participants: A total of 179 LAGC patients who received neoadjuvant therapy followed by radical gastrectomy at Fujian Medical University Union Hospital between January 2019 and December 2022. Patients were divided into a training cohort (n = 125) and an independent validation cohort (n = 54). Data Collection: Baseline contrast-enhanced CT scans prior to neoadjuvant therapy were used for radiomic analysis. Postoperative tumor RNA sequencing data were used for immune profiling. Clinical and pathological data, including ypTNM stage, were collected from medical records. Methods: DeepScore: Extracted from CT images using a ResNet18-based deep learning model. Significant features were selected via univariate Cox and LASSO regression. ImmuneScore: Calculated from RNA-seq data using the ESTIMATE algorithm to assess tumor immune infiltration. Nomogram Construction: A multi-omics nomogram was developed using multivariate Cox regression incorporating DeepScore, ImmuneScore, and ypTNM stage. Validation: Model performance was evaluated using time-dependent ROC analysis (AUC) and Kaplan-Meier survival analysis with log-rank tests in both cohorts. Primary Outcomes: Disease-free survival (DFS) and overall survival (OS). Statistical Analysis: Survival analyses were performed using Kaplan-Meier and Cox regression models. AUC values were computed for 1-, 2-, and 3-year DFS predictions. All analyses were conducted in R (v4.4.3).

Status (registry)
Completed
Study type
Observational
Enrollment
179 (actual)
First submitted
13 Sept 2025
Start date
1 Jan 2019
Primary completion date
31 Dec 2022
Lead sponsor
Chang-Ming Huang, Prof.
NCT number
NCT07190040

What you commit to

  • Study type: Observational
  • Participants enrolled: 179
  • From 1 Jan 2019 to the primary completion date, 31 Dec 2022
  • 1 site in 1 country
  • Visits and tests: ask the trial team, the registry does not list them

Built from the registry fields of ClinicalTrials.gov. It describes the study, not your situation.

Who cannot take part

Copied verbatim from ClinicalTrials.gov and grouped by topic. Only the trial team decides who can take part.

Prior treatment

  • Absence of baseline computed tomography (CT) data prior to treatment or suboptimal CT image quality that could compromise the accuracy of radiomic information extraction

Other illness

  • Gastric cancer originating from the remnant stomach

Other

  • Absence of postoperative transcriptome data

Who the study is looking for

  • Gastric adenocarcinoma confirmed pathologically via gastroscopy;
  • Clinical staging of cT3/T4N0/+M0 with a history of receiving at least two cycles of neoadjuvant therapy
  • No prior history of other malignant tumors
  • Completion of radical gastrectomy

Age in the registry: 18 years to - · Sex: All

Full criteria on ClinicalTrials.gov

Sites (1)

Sites in registry order. Pick your city on the disease page to sort them by distance.

SiteCityCountrySite status
Fujian Medical University
Coordinator of this site? Confirm its status
FuzhouChinaUnknown status

This page is information, not medical advice. Discuss any trial with your doctor.