Gastric Cancer (Diagnosis)NCT07401173
DeepComp for Prediction of Gastric Cancer Postoperative Complications (DeepComp-Prospective)
An observational study of Gastric Cancer (Diagnosis) and Postoperative Complications, run by Qun Zhao at 1 site in China.
Trial 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. Read more about this study
- Who is it for?
- Ages 18 to 85 years
- Conditions the study is for
- Gastric Cancer (Diagnosis), Postoperative Complications
- How many people
- 500 participants
- Where
- 1 siteChina
- Study dates
- 1 Mar 2026to 1 May 2026
- How long will I take part?
- 30 Days
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 to 85 years
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: Adult patients diagnosed with potentially resectable gastric cancer who are admitted to the participating centers for surgical treatment.
Who may be able to join
- 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.
Who cannot join
- 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.
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
China1 site, 1 recruiting
- Recruiting
the Fourth Hospital of Hebei Medical University
Shijiazhuang, None Selected, 050011
Ping'an Ding, 031186095363, ding_ping_an@hebmu.edu.cn
About this study
Official 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
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.
- Medical subject headings
- Stomach Neoplasms, Disease, Postoperative Complications
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| 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 being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: 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.
Measured: From preoperative assessment through 30 days post-surgery
Secondary outcomes (2)
Predictive Performance of the DeepComp Model (AUC)
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.
Measured: Postoperative 30 days
Length of Hospital Stay
Defined as the number of days from surgery to discharge.
Measured: Up to 30 days
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted10 Feb 2026
- Study start1 Mar 2026
- Primary completion1 May 2026 (estimated)
- Study completion1 May 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Gastric Cancer (Diagnosis)Studies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 500 participants (estimated)
- Target duration
- 30 Days
Sponsor and investigators
- Lead sponsor
- Qun Zhao
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor-investigator, Qun Zhao, Hebei Medical University
- Investigators
- Qun Zhao, Principal investigator, th
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07401173
- Sponsor's protocol ID
- DeepComp-Pro-001
- Status last verified
- Apr 2026
- First submitted
- 3 Feb 2026
- Last updated
- 9 Apr 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 9 Apr 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official 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
- ClinicalTrials.gov ID
- NCT07401173
- Acronym / sponsor ID
- DeepComp-Pro-001
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 500 participants (estimated)
- Lead sponsor
- Qun ZhaoUniversity, hospital or other
- Study start
- 1 Mar 2026 (actual)
- Primary completion
- 1 May 2026 (estimated)
- Study completion
- 1 May 2026 (estimated)
- First posted
- 10 Feb 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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 design
- Observational model
- Cohort
- Time perspective
- Prospective
- Target follow-up
- 30 Days
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| 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. |
Who may qualify
- Ages
- 18 to 85 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Adult patients diagnosed with potentially resectable gastric cancer who are admitted to the participating centers for surgical treatment.
Key inclusion criteria (5 listed)
- 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.
Key exclusion criteria (5 listed)
- 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.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
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.
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.
2 secondary outcomes
Predictive Performance of the DeepComp Model (AUC)
Time frame: Postoperative 30 days
Length of Hospital Stay
Time frame: Up to 30 days
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 9 Apr 2026.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Qun Zhao principal investigator
th
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Gastric Cancer (Diagnosis) and Postoperative Complications, run by Qun Zhao at 1 site in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 5 inclusion and 5 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: looking for participants now, as of the sponsor's last update on 9 Apr 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Ping'an Ding, PhD (+8631186095363, ding_ping_an@hebmu.edu.cn), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Incidence of Major Postoperative Complications (Clavien-Dindo Grade ≥ II) (time frame: Postoperative 30 days). The study lists 2 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 9 Apr 2026.