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DeepComp for Prediction of Gastric Cancer Postoperative Complications (DeepComp-Prospective)

NCT07401173 · Status: RECRUITING · Type: OBSERVATIONAL · Enrollment: 500

Last updated 2026-04-09

No results posted yet for this study

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

  • Gastric Cancer (Diagnosis)
  • Postoperative Complications

Sponsors & Collaborators

  • Qun Zhao

    lead OTHER

Principal Investigators

  • Qun Zhao · th

Eligibility

Min Age
18 Years
Max Age
85 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2026-03-01
Primary Completion
2026-05-01
Completion
2026-05-01

Countries

  • China

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT07401173 on ClinicalTrials.gov