Actively Recruiting

Age: 18Years - 85Years
All Genders
ID07401173

DeepComp: A Prospective Multicenter Observational Study Validating AI Prediction of Major Postoperative Complications in Gastric Cancer Surgery

Led by Qun Zhao · Updated on 2026-04-09

500

Participants Needed

1

Research Sites

N/A

Total Duration

On this page

AI-Summary

What this Trial Is About

Gastric cancer is a leading cause of cancer-related deaths, with radical surgery as the main treatment. Postoperative complications are common and can greatly affect recovery and quality of life. Doctors currently lack precise tools to predict which patients are at high risk of severe complications before surgery. This research aims to validate an artificial intelligence model called DeepComp, which combines clinical data and advanced features from routine preoperative CT scans to analyze tumor and body composition, including muscle and fat, to assess patient physiological reserve. In this prospective, multicenter observational study, patients scheduled for radical gastrectomy (open, laparoscopic, or robotic) will undergo standard preoperative contrast-enhanced CT scans. The DeepComp AI model will be applied to these scans to predict the risk of moderate-to-severe postoperative complications. Researchers will then compare these predictions with actual clinical outcomes observed 30 days after surgery to evaluate the model's accuracy and reliability. Participants will be followed for 30 days after surgery to monitor postoperative complications using the Clavien-Dindo grading system, focusing on grade II or higher events. Researchers will assess the AI model's diagnostic performance and agreement with clinical observations. Additional measures include length of hospital stay up to 30 days after surgery. This study will help determine if DeepComp can be a useful tool for personalized surgical risk assessment in gastric cancer patients.

CONDITIONS

Brief Title

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

Who Can Participate

Age: 18Years - 85Years
All Genders

Eligibility Criteria

Eligible

You may qualify if you...

  • Age 18 years or older
  • Histologically confirmed gastric adenocarcinoma
  • Scheduled for elective radical gastrectomy (open, laparoscopic, or robotic) with curative intent
  • Standard preoperative contrast-enhanced abdominal CT scan performed within 14 days before surgery
  • Willingness to sign informed consent
Not Eligible

You will not qualify if you...

  • Emergency surgery due to perforation, obstruction, or massive bleeding
  • Intraoperative findings of distant metastasis (Stage IV) or unresectable disease preventing complete tumor removal
  • 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

AI-Screening

AI-Powered Screening

Complete this quick 3-step screening to check your eligibility

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Your Study Journey

Screening

Duration - 2 to 4 weeks

Participants are screened for eligibility to participate in the trial.

1 visit (in-person)

Diagnostic Evaluation

Duration - Up to 14 days before surgery

Participants undergo standard preoperative contrast-enhanced CT scans. These scans are analyzed by the DeepComp AI model to predict the risk of postoperative complications.

1 visit (in-person) for CT scan

Surgery and Immediate Post-operative Care

Duration - Surgery day and immediate recovery period

Participants undergo radical gastrectomy (open, laparoscopic, or robotic) followed by immediate post-operative care.

1 surgical procedure visit and immediate post-operative care

Post-operative Follow-up

Duration - Up to 30 days post-surgery

Participants are monitored for major postoperative complications and recovery for up to 30 days after surgery.

Approximately 3 to 5 post-operative visits over 30 days

Trial Site Locations

Total: 1 location

1

the Fourth Hospital of Hebei Medical University

Shijiazhuang, None Selected, China, 050011

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Research Team

P

Ping'an Ding, PhD

Q

Qun Zhao, PhD

How is the study designed?

Study Type

OBSERVATIONAL

Masking

N/A

Allocation

N/A

Model

N/A

Primary Purpose

N/A

Number of Arms

1

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Frequently Asked Questions

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