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NCT Number: NCT07401173

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

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.

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Key information

Conditions

Gastric Cancer (Diagnosis) Digestive System Diseases Digestive System Neoplasms Disease Gastrointestinal Diseases Gastrointestinal Neoplasms Neoplasms Neoplasms by Site Pathologic Processes Pathological Conditions, Signs and Symptoms Postoperative Complications Stomach Diseases Stomach Neoplasms

Age range

18 year–85 year

Sex eligibility

All sexes

Study type

Observational

Primary location

the Fourth Hospital of Hebei Medical University

Shijiazhuang, None Selected, 050011, China

Location status: Recruiting

Location contact

Ping'an Ding

CONTACT

[email protected]

031186095363

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

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.

Treatment and study plan

Primary outcomes

  1. 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.

  2. 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.

Secondary outcomes

  1. 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.

  2. Length of Hospital Stay

    Time frame: Up to 30 days

    Defined as the number of days from surgery to discharge.

Study contacts

Contact information is provided by the study sponsor or research team.

Ping'an Ding, PhD

CONTACT

[email protected]

+8631186095363

Qun Zhao, PhD

CONTACT

[email protected]

031186095363

Sponsors and collaborators

Lead sponsor

Qun Zhao

Other

Registry information

Official study title

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

Important dates

Study start
2026
Primary completion
2026
Study completion
2026
First posted
Feb 10, 2026
Registry last updated
Apr 9, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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