Gastrectomy for Gastric CancerNCT07525765
AI-assisted Decision-making of Reoperation for Postoperative Bleeding of Gastric Cancer
An observational study of Gastrectomy for Gastric Cancer and Gastric Cancer, run by First Affiliated Hospital of Zhejiang University at 1 site in China.
Trial summary
The goal of this observational study is to develop and validate a deep learning model to dynamically assess postoperative bleeding risk and assist in decision-making for re-operation in adult patients (≥18 years) diagnosed with primary gastric cancer undergoing radical gastrectomy. Read more about this study
- Who is it for?
- Ages 18 to 90 years
- Conditions the study is for
- Gastrectomy for Gastric Cancer, Gastric Cancer
- How many people
- 7,000 participants
- Where
- 1 siteChina
- Study dates
- 10 Apr 2026to 31 Jan 2028
- 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 90 years
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: This study includes adults (≥18y) with confirmed primary gastric cancer undergoing elective radical gastrectomy (proximal/distal/total) with D1+/D2 lymphadenectomy at [Center]. The cohort comprises retrospective ([2015.6]-[2026.2]) and prospective ([2026.3]-present) arms. Emergency, palliative, or multi-organ resections are excluded to ensure homogeneity. The investigators anticipate enrolling 7000 patients. The primary outcome is postoperative hemorrhage requiring surgical re-intervention within 30 days. Estimated incidence is 0.5%-2.0%. To address this class imbalance, the AI model will employ stratified sampling and cost-sensitive learning.This population represents standard candidates for curative surgery in tertiary centers. By excluding extreme cases, the model is optimized for risk stratification in routine elective settings, where early warnings prevent catastrophic outcomes. Prospective data will validate real-time generalizability.
Who may be able to join
- Age: Patients aged ≥ 18 years.
- Diagnosis: Histologically confirmed primary gastric cancer.
- Surgical Procedure: Underwent radical gastrectomy (including proximal, distal, or total gastrectomy).
- Consent: Provision of written informed consent (required specifically for the prospective phase).
- Data Completeness: Availability of complete preoperative clinical data and postoperative follow-up records covering at least the first 15 days post-surgery.
- Oncological History: No history of other primary malignant tumors.
Who cannot join
- Surgical Type: Patients who underwent non-radical resection or emergency surgery.
- Data Quality: Missing rate of key data fields exceeds 20%.
- Preoperative Condition: Presence of severe preoperative infection or organ failure.
- Follow-up Compliance: Unwillingness to participate in prospective follow-up or inability to complete the follow-up schedule (applicable only to the prospective phase).
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 First Affiliated Hospital, Zhejiang University School of Medicine Yuhang Campus
Hangzhou, Zhejiang, 330100
Gastroenterological Surgery, 86+0571-87235877, 12518934@zju.edu.cn
About this study
Official title: A Multicenter Observational Study to Develop and Validate a Deep Learning Model for Dynamic Assessment of Postoperative Bleeding Risk to Assist Re-operation Decision-Making in Patients With Gastric Cancer
The goal of this observational study is to develop and validate a deep learning model to dynamically assess postoperative bleeding risk and assist in decision-making for re-operation in adult patients (≥18 years) diagnosed with primary gastric cancer undergoing radical gastrectomy. The main question[s] it aims to answer [is/are]:
Can an AI model based on perioperative dynamic physiological parameters and precise intraoperative blood loss accurately predict the risk of postoperative bleeding requiring re-operation? Does the application of this AI model improve clinical decision-making (e.g., earlier warning time, optimal intervention timing) and patient outcomes (e.g., mortality, length of stay)? Since there is no comparison group (this is a pure observational study without intervention arms), researchers will not compare different treatment groups. Instead, the investigators will evaluate the model's performance (sensitivity, negative predictive value, AUC, calibration) using retrospective data for training and prospective multi-center data for external validation.
Read more
Participants will:
Undergo standard radical gastrectomy and routine postoperative care as per clinical practice (no study-specific interventions).
Have their perioperative data collected, including demographics, medical history, vital signs, laboratory tests (blood gas analysis), surgical details, and precise intraoperative blood loss measurements.
(For prospective participants only) Provide informed consent and complete follow-up assessments up to 30 days post-surgery.
Read the detailed description
This study employs a hybrid design, collecting both retrospective and prospective data.
- Conditions
- Gastrectomy for Gastric Cancer, Gastric Cancer
- Medical subject headings
- Stomach Neoplasms
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 |
|---|---|---|
| Training set (led by the Principal Investigator) | The main part of retrospective data for model construction, parameter learning, without interventions | |
| Validation set (led by the Principal Investigator) | The remainder of the retrospective data for hyperparameter tuning to prevent overfitting, without interventions | |
| External validation set (conducted by other investigators) | Prospective collected data for final performance evaluation, without interventions |
What is being measured
The primary outcome is the main question the study is designed to answer.
predictive performance of the deep learning model for identifying patients at high risk of postoperative bleeding requiring re-operation
The Area Under the Receiver Operating Characteristic Curve (AUC-ROC) of the AI model for predicting postoperative bleeding requiring re-operation in the external validation cohort.
Measured: The primary endpoint is the AUC-ROC of the model in predicting postoperative bleeding requiring re-operation within 30 days after surgery
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted13 Apr 2026
- Study start10 Apr 2026 (estimated)
- Primary completion31 Dec 2027 (estimated)
- Study completion31 Jan 2028 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Gastrectomy for Gastric CancerStudies of the same condition, open and under way.
- More trials run by First Affiliated Hospital of Zhejiang UniversityThe sponsor's other registered studies.
- 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
- Other
- Enrolment
- 7,000 participants (estimated)
- Target duration
- 30 Days
Sponsor and investigators
- Lead sponsor
- First Affiliated Hospital of Zhejiang University
- Sponsor type
- University, hospital or other
- Collaborators
- Jinhua Municipal Central Hospital, Second Affiliated Hospital of Nanchang University
- Responsible party
- Sponsor
- Investigators
- Jichao Qin, M.D., Study chair, Zhejiang University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07525765
- Sponsor's protocol ID
- IIT20260161B
- Status last verified
- Mar 2026
- First submitted
- 10 Mar 2026
- Last updated
- 13 Apr 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 13 Apr 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- A Multicenter Observational Study to Develop and Validate a Deep Learning Model for Dynamic Assessment of Postoperative Bleeding Risk to Assist Re-operation Decision-Making in Patients With Gastric Cancer
- ClinicalTrials.gov ID
- NCT07525765
- Acronym / sponsor ID
- IIT20260161B
- Conditions
- Gastrectomy for Gastric Cancer, Gastric Cancer
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 7,000 participants (estimated)
- Lead sponsor
- First Affiliated Hospital of Zhejiang UniversityUniversity, hospital or other
- Collaborators
- Jinhua Municipal Central Hospital, Second Affiliated Hospital of Nanchang University
- Study start
- 10 Apr 2026 (estimated)
- Primary completion
- 31 Dec 2027 (estimated)
- Study completion
- 31 Jan 2028 (estimated)
- First posted
- 13 Apr 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.
The goal of this observational study is to develop and validate a deep learning model to dynamically assess postoperative bleeding risk and assist in decision-making for re-operation in adult patients (≥18 years) diagnosed with primary gastric cancer undergoing radical gastrectomy. The main question[s] it aims to answer [is/are]:
Can an AI model based on perioperative dynamic physiological parameters and precise intraoperative blood loss accurately predict the risk of postoperative bleeding requiring re-operation? Does the application of this AI model improve clinical decision-making (e.g., earlier warning time, optimal intervention timing) and patient outcomes (e.g., mortality, length of stay)? Since there is no comparison group (this is a pure observational study without intervention arms), researchers will not compare different treatment groups. Instead, the investigators will evaluate the model's performance (sensitivity, negative predictive value, AUC, calibration) using retrospective data for training and prospective multi-center data for external validation.
Participants will:
Undergo standard radical gastrectomy and routine postoperative care as per clinical practice (no study-specific interventions).
Have their perioperative data collected, including demographics, medical history, vital signs, laboratory tests (blood gas analysis), surgical details, and precise intraoperative blood loss measurements.
(For prospective participants only) Provide informed consent and complete follow-up assessments up to 30 days post-surgery.
Detailed description
This study employs a hybrid design, collecting both retrospective and prospective data.
Study design
- Observational model
- Cohort
- Time perspective
- Other
- Target follow-up
- 30 Days
- Number of arms
- 3
| Arm | Type | Receives |
|---|---|---|
| Training set (led by the Principal Investigator) | – | –The main part of retrospective data for model construction, parameter learning, without interventions |
| Validation set (led by the Principal Investigator) | – | –The remainder of the retrospective data for hyperparameter tuning to prevent overfitting, without interventions |
| External validation set (conducted by other investigators) | – | –Prospective collected data for final performance evaluation, without interventions |
Who may qualify
- Ages
- 18 to 90 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- This study includes adults (≥18y) with confirmed primary gastric cancer undergoing elective radical gastrectomy (proximal/distal/total) with D1+/D2 lymphadenectomy at [Center]. The cohort comprises retrospective ([2015.6]-[2026.2]) and prospective ([2026.3]-present) arms. Emergency, palliative, or multi-organ resections are excluded to ensure homogeneity. The investigators anticipate enrolling 7000 patients. The primary outcome is postoperative hemorrhage requiring surgical re-intervention within 30 days. Estimated incidence is 0.5%-2.0%. To address this class imbalance, the AI model will employ stratified sampling and cost-sensitive learning.This population represents standard candidates for curative surgery in tertiary centers. By excluding extreme cases, the model is optimized for risk stratification in routine elective settings, where early warnings prevent catastrophic outcomes. Prospective data will validate real-time generalizability.
Key inclusion criteria (6 listed)
- Age: Patients aged ≥ 18 years.
- Diagnosis: Histologically confirmed primary gastric cancer.
- Surgical Procedure: Underwent radical gastrectomy (including proximal, distal, or total gastrectomy).
- Consent: Provision of written informed consent (required specifically for the prospective phase).
- Data Completeness: Availability of complete preoperative clinical data and postoperative follow-up records covering at least the first 15 days post-surgery.
Key exclusion criteria (4 listed)
- Surgical Type: Patients who underwent non-radical resection or emergency surgery.
- Data Quality: Missing rate of key data fields exceeds 20%.
- Preoperative Condition: Presence of severe preoperative infection or organ failure.
- Follow-up Compliance: Unwillingness to participate in prospective follow-up or inability to complete the follow-up schedule (applicable only to the prospective phase).
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
predictive performance of the deep learning model for identifying patients at high risk of postoperative bleeding requiring re-operation
Time frame: The primary endpoint is the AUC-ROC of the model in predicting postoperative bleeding requiring re-operation within 30 days after surgery
The Area Under the Receiver Operating Characteristic Curve (AUC-ROC) of the AI model for predicting postoperative bleeding requiring re-operation in the external validation cohort.
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 13 Apr 2026.
- Individual participant dataSharing planned: No
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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.
Jichao Qin, M.D. study chair
Zhejiang University
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 Gastrectomy for Gastric Cancer and Gastric Cancer, run by First Affiliated Hospital of Zhejiang University 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 6 inclusion and 4 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 13 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 Jianghao Li, B.S. in Computer Science (86+15968774033, 12518934@zju.edu.cn), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
predictive performance of the deep learning model for identifying patients at high risk of postoperative bleeding requiring re-operation (time frame: The primary endpoint is the AUC-ROC of the model in predicting postoperative bleeding requiring re-operation within 30 days after surgery). 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 13 Apr 2026.