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

NCT07401173 | Recruiting

• 1 other identifier: DeepComp-Pro-001
• Study Type: observational
• Target: 500
• Locations: 1 country
• Sites: 1

Brief 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

Outcome Measures

Primary Outcomes (2)

• Incidence of Major Postoperative Complications (Clavien-Dindo Grade ≥ II)

  Postoperative complications will be graded according to the Clavien-Dindo classification system. Major complications are defined as Grade II or higher, which require pharmacological treatment, surgical/endoscopic/radiological intervention, or life-threatening complications (including death). The occurrence of these events will be recorded and compared with the model's preoperative predictions.

  Postoperative 30 days

• Human-AI Collaborative Diagnostic Performance in Gastric Cancer Surgery: Accuracy and Observer Agreement

  In a subset of 120 randomly selected gastric cancer surgery patients, ten surgeons of varying experience levels (Junior \<5 years, n=4; Intermediate 5-10 years, n=3; Senior ≥10 years, n=3) will first independently assess postoperative complication risk using blinded preoperative data. Subsequently, they will receive predictions from the DeepComp AI model and update their assessments.

  From preoperative assessment through 30 days post-surgery

Secondary Outcomes (2)

• Predictive Performance of the DeepComp Model (AUC)

  Postoperative 30 days

• Length of Hospital Stay

  Up to 30 days

Study Arms (1)

Gastric Cancer Surgery Cohort

Patients diagnosed with gastric cancer who are scheduled to undergo radical gastrectomy (open, laparoscopic, or robotic). All participants will receive standard preoperative contrast-enhanced CT scans. The DeepComp AI model will be applied to these scans to predict the risk of postoperative complications.

Eligibility Criteria

• Age: 18 Years - 85 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Adult patients diagnosed with potentially resectable gastric cancer who are admitted to the participating centers for surgical treatment.

You may qualify if:

• 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.

You may not qualify if:

• 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.

Sponsors & Collaborators

• Qun Zhao: lead

Study Sites (1)

the Fourth Hospital of Hebei Medical University

Shijiazhuang, None Selected, 050011, China

RECRUITING

MeSH Terms

Conditions

Stomach Neoplasms; Disease; Postoperative Complications

Condition Hierarchy (Ancestors)

Gastrointestinal Neoplasms; Digestive System Neoplasms; Neoplasms by Site; Neoplasms; Digestive System Diseases; Gastrointestinal Diseases; Stomach Diseases; Pathologic Processes; Pathological Conditions, Signs and Symptoms

Study Officials

• Qun Zhao

  th

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Ping'an Ding, PhD

CONTACT

+8631186095363; ding_ping_an@hebmu.edu.cn

Qun Zhao, PhD

CONTACT

031186095363; zhaoqun@hebmu.edu.cn

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Target Duration: 30 Days
• Sponsor Type: OTHER
• Responsible Party: SPONSOR INVESTIGATOR
• PI Title: Professor

Study Record Dates

• First Submitted: February 3, 2026
• First Posted: February 10, 2026
• Study Start: March 1, 2026
• Primary Completion: May 1, 2026
• Study Completion: May 1, 2026
• Last Updated: April 9, 2026

Record last verified: 2026-04

Locations

CN (1)