DeepComp for Prediction of Gastric Cancer Postoperative Complications (DeepComp-Prospective)
For patients and families
In plain language
An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.
- What is being studied
- This is an observational study: the protocol does not assign a study treatment.
- Who it may be relevant to
- Registry conditions: Gastric Cancer (Diagnosis), Postoperative Complications. Basic parameters: 18 years — 85 years · All.
- What needs checking
- Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
- Where it takes place
- China
- Next step
- Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
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Official title
A Prospective, Multicenter, Observational Study Validating the Multimodal Deep Learning Radiomics Model (DeepComp) for Preoperative Prediction of Major Postoperative Complications in Patients With Gastric Cancer
Overview
Gastric cancer is a leading cause of cancer-related mortality, and radical surgery remains the primary treatment. However, postoperative complications are common and can significantly impact patient recovery and quality of life. Currently, doctors lack precise tools to accurately predict which patients are at high risk for developing severe complications before surgery. This study aims to validate a novel artificial intelligence (AI) model called "DeepComp." The DeepComp model integrates clinical data with advanced radiomic features derived from routine preoperative CT scans. Specifically, it analyzes both the tumor characteristics and the patient's body composition (including skeletal muscle and fat distribution) to assess physiological reserve. In this prospective, multicenter observational study, researchers will enroll patients scheduled for gastric cancer surgery across five medical centers. The DeepComp model will be used to predict the risk of moderate-to-severe postoperative complications (Clavien-Dindo grade II or higher). These predictions will then be compared with the actual clinical outcomes observed 30 days after surgery. The goal is to determine the accuracy and reliability of the DeepComp model in a real-world clinical setting, potentially providing a powerful tool for personalized surgical risk assessment.
Primary outcome measures
- Incidence of Major Postoperative Complications (Clavien-Dindo Grade ≥ II) [Time frame: Postoperative 30 days]
- Human-AI Collaborative Diagnostic Performance in Gastric Cancer Surgery: Accuracy and Observer Agreement [Time frame: From preoperative assessment through 30 days post-surgery]
Secondary outcome measures (2)
- Predictive Performance of the DeepComp Model (AUC) [Time frame: Postoperative 30 days]
- Length of Hospital Stay [Time frame: Up to 30 days]
Eligibility criteria
Inclusion criteria
Age ≥ 18 years.
Histologically confirmed gastric adenocarcinoma.
Scheduled for elective radical gastrectomy (open, laparoscopic, or robotic) with curative intent.
Standard preoperative contrast-enhanced abdominal CT scans (venous phase) performed within 14 days prior to surgery.
Willingness to sign informed consent.
Exclusion criteria
Emergency surgery due to perforation, obstruction, or massive bleeding.
Intraoperative findings of distant metastasis (Stage IV) or unresectable disease preventing R0 resection.
Concurrent or previous malignant tumors within the last 5 years (except gastric cancer).
Pregnancy or lactation.
Severe metallic artifacts on CT images preventing radiomic analysis.
Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.
Healthy volunteers: No
Study design
- Observational model
- Cohort
Study locations
China · 1 center
- the Fourth Hospital of Hebei Medical University — Shijiazhuang
Identifiers
NCT: NCT07401173 · DeepComp-Pro-001