Union Hospital, Tongji Medical College, Huazhong University of Science and Technology
Wuhan, Hubei, 430022, China
Location status: Recruiting
NCT Number: NCT07776067
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.
Interested in participating?
Request Info18 year and older
All sexes
Observational
Wuhan, Hubei, 430022, China
Location status: Recruiting
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.
Healthy volunteers accepted: No
Only the study team can determine whether someone qualifies for participation.
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.
No intervention or treatment will be assigned by the study protocol. All diagnostic and treatment decisions, including surgery, chemotherapy, immunotherapy, or combined treatment, will be made by treating clinicians as part of routine clinical care. The predefined CT radiomics-based prognostic model will be applied to collected imaging and clinical data for research validation only. Model-generated predictions will not be used to guide or modify clinical treatment decisions.
Time frame: From initiation of treatment until death or last follow-up, assessed up to 12 months
Overall survival is defined as the time from initiation of initial treatment to death from any cause. Participants who are alive at the last available clinical follow-up will be censored at the date of last follow-up. Survival status will be obtained from medical records, telephone follow-up, and death certificates.
Time frame: From initiation of treatment until disease progression, death, or last follow-up, assessed up to 12 months
Progression-free survival is defined as the time from initiation of treatment to the first documented disease progression or death from any cause, whichever occurs first. Participants without disease progression or death will be censored at the date of the last available clinical assessment.
Union Hospital, Tongji Medical College, Huazhong University of Science and Technology
Other
Prospective Validation of a CT Radiomics-Based Model Combined With Clinical Features for Prognosis Prediction in Gastric Cancer Patients
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View the official ClinicalTrials.gov record (opens in a new tab)This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.
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