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

April 6, 2026 updated by: Qun Zhao

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

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.

Study Overview

Status

Recruiting

Conditions

Study Type

Observational

Enrollment (Estimated)

500

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Name: Ping'an Ding, PhD
Phone Number: +8631186095363
Email: ding_ping_an@hebmu.edu.cn

Study Contact Backup

Name: Qun Zhao, PhD
Phone Number: 031186095363
Email: zhaoqun@hebmu.edu.cn

Study Locations

China
- None Selected
  - Shijiazhuang, None Selected, China, 050011
    - Recruiting
    - The Fourth Hospital of Hebei Medical University
    - Contact:
      
      Ping'an Ding
      
      Phone Number: 031186095363
      
      Email: ding_ping_an@hebmu.edu.cn

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

Adult
Older Adult

Accepts Healthy Volunteers

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.

Description

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.

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

Number of groups / cohorts

Cohorts and Interventions

Group / Cohort
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.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Incidence of Major Postoperative Complications (Clavien-Dindo Grade ≥ II) Time Frame: Postoperative 30 days	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 Time Frame: From preoperative assessment through 30 days post-surgery	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 Outcome Measures

Outcome Measure	Measure Description	Time Frame
Predictive Performance of the DeepComp Model (AUC) Time Frame: Postoperative 30 days	The discrimination performance of the DeepComp model in predicting major postoperative complications will be evaluated using the Area Under the Receiver Operating Characteristic Curve (AUC). Sensitivity, specificity, positive predictive value, and negative predictive value will also be calculated.	Postoperative 30 days
Length of Hospital Stay Time Frame: Up to 30 days	Defined as the number of days from surgery to discharge.	Up to 30 days

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Qun Zhao

Investigators

Principal Investigator: Qun Zhao, th

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

March 1, 2026

Primary Completion (Estimated)

May 1, 2026

Study Completion (Estimated)

May 1, 2026

Study Registration Dates

First Submitted

February 3, 2026

First Submitted That Met QC Criteria