Trials / Recruiting
RecruitingNCT07401173
DeepComp for Prediction of Gastric Cancer Postoperative Complications (DeepComp-Prospective)
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
- Status
- Recruiting
- Phase
- —
- Study type
- Observational
- Enrollment
- 500 (estimated)
- Sponsor
- Qun Zhao · Academic / Other
- Sex
- All
- Age
- 18 Years – 85 Years
- Healthy volunteers
- Not accepted
Summary
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.
Conditions
Timeline
- Start date
- 2026-03-01
- Primary completion
- 2026-05-01
- Completion
- 2026-05-01
- First posted
- 2026-02-10
- Last updated
- 2026-04-09
Locations
1 site across 1 country: China
Source: ClinicalTrials.gov record NCT07401173. Inclusion in this directory is not an endorsement.