Overview
The goal of this prospective observational study is to evaluate how well a previously developed prediction model can estimate outcomes in adults with gastric cancer. The model combines information from computed tomography (CT) images obtained before treatment with routine clinical information. The main questions are how well the model predicts how long participants live after treatment begins and whether their cancer progresses.
Participants will receive their usual medical care. Researchers will collect pretreatment CT images, basic clinical and tumor information, laboratory and tumor marker results, treatment information, and follow-up outcomes obtained during routine care. These data will be entered into the predefined prediction model, and the model's predictions will be compared with what actually happens during follow-up.
The study will not change the participants' usual treatment. Model predictions will be used for research purposes only and will not be used to make treatment decisions.
Description
This is a single-center, non-interventional prospective observational cohort study designed to validate a prognostic prediction model for gastric cancer that integrates pretreatment CT imaging features with clinical variables. The model was developed from a previous retrospective cohort of patients with gastric cancer using pretreatment CT images, clinicopathological information, treatment data, and survival outcomes.
Adults with newly diagnosed gastric cancer who are scheduled to receive surgery, chemotherapy, immunotherapy, or combined treatment at the study center will be prospectively enrolled. During routine clinical care, researchers will collect pretreatment CT images and relevant demographic, clinicopathological, laboratory, tumor marker, treatment, and follow-up data. Quantitative CT features related to tumor morphology, density, texture, and spatial heterogeneity will be integrated with clinical variables such as age, sex, TNM stage, Lauren classification, serum tumor markers, and treatment modality to generate individualized prognostic risk estimates.
Model predictions will be compared with observed clinical outcomes during follow-up to evaluate prognostic performance and risk stratification. Model performance will also be assessed in clinically relevant subgroups, including groups defined by treatment modality, clinical stage, age, and sex. Statistical analyses will include Kaplan-Meier survival analysis, log-rank tests, and Cox proportional hazards regression with adjustment for potential confounding factors.
This study will not assign treatment or alter routine clinical care. All treatment decisions will be made by treating clinicians according to each participant's clinical condition and current clinical practice. Model-generated predictions will be stored for research purposes only and will not be provided to clinicians for treatment decision-making. Clinical and imaging data will be coded and de-identified and stored in a password-protected research database. Data quality will be supported by standardized data collection procedures, investigator training, independent data checking, imaging quality review, and standardized follow-up procedures.
Eligibility
Inclusion Criteria:
Histologically or pathologically confirmed gastric cancer and receiving treatment at the study center.
Age ≥18 years. Complete pretreatment CT imaging data available from the study center.
Exclusion Criteria:
Pretreatment CT images of insufficient quality for analysis. Incomplete clinical data. Presence of another malignant tumor in addition to gastric cancer.
