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

NCT07190040 | Completed

• 1 other identifier: 2025NAT01
• Study Type: observational
• Target: 179
• Locations: 1 country
• Sites: 1

Brief Summary

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).

Conditions

Gastric Cancer (GC)

Outcome Measures

Primary Outcomes (1)

• the Area Under the Curve

  The model's predictive accuracy was evaluated by computing the Area Under the Curve for predicting 1-year, 2-year, and 3-year disease-free survival.

  2023.01.31-2025.05.31

Secondary Outcomes (1)

• Disease-free survival

  2023.01.31-2025.05.31

Other Outcomes (1)

• Overall survival

  2023.01.31-2025.05.31

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Patients with locally advanced gastric cancer who underwent neoadjuvant therapy

You may qualify if:

• 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

You may not qualify if:

• Gastric cancer originating from the remnant stomach
• Absence of baseline computed tomography (CT) data prior to treatment or suboptimal CT image quality that could compromise the accuracy of radiomic information extraction
• Absence of postoperative transcriptome data

Sponsors & Collaborators

• Chang-Ming Huang, Prof.: lead

Study Sites (1)

Fujian Medical University

Fuzhou, Fujian, 350001, China

Location

MeSH Terms

Conditions

Stomach Neoplasms

Condition Hierarchy (Ancestors)

Gastrointestinal Neoplasms; Digestive System Neoplasms; Neoplasms by Site; Neoplasms; Digestive System Diseases; Gastrointestinal Diseases; Stomach Diseases

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR INVESTIGATOR
• PI Title: Prof.

Study Record Dates

• First Submitted: September 13, 2025
• First Posted: September 24, 2025
• Study Start: January 1, 2019
• Primary Completion: December 31, 2022
• Study Completion: September 1, 2025
• Last Updated: September 24, 2025

Record last verified: 2025-09

Locations

CN (1)