AI-assisted Decision-making of Reoperation for Postoperative Bleeding of Gastric Cancer

April 7, 2026 updated by: First Affiliated Hospital of Zhejiang University

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.

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.

Study Overview

Status

Recruiting

Conditions

Detailed Description

This study employs a hybrid design, collecting both retrospective and prospective data.

Study Type

Observational

Enrollment (Estimated)

7000

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: Jianghao Li, B.S. in Computer Science
  • Phone Number: 86+15968774033
  • Email: 12518934@zju.edu.cn

Study Locations

  • China
    • Zhejiang
      • Hangzhou, Zhejiang, China, 330100
        • Recruiting
        • The First Affiliated Hospital, Zhejiang University School of Medicine Yuhang Campus
        • Contact:

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

No

Sampling Method

Non-Probability Sample

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.

Description

Inclusion Criteria:

  1. Age: Patients aged ≥ 18 years.
  2. Diagnosis: Histologically confirmed primary gastric cancer.
  3. Surgical Procedure: Underwent radical gastrectomy (including proximal, distal, or total gastrectomy).
  4. Consent: Provision of written informed consent (required specifically for the prospective phase).
  5. Data Completeness: Availability of complete preoperative clinical data and postoperative follow-up records covering at least the first 15 days post-surgery.
  6. Oncological History: No history of other primary malignant tumors.

Exclusion Criteria:

  1. Surgical Type: Patients who underwent non-radical resection or emergency surgery.
  2. Data Quality: Missing rate of key data fields exceeds 20%.
  3. Preoperative Condition: Presence of severe preoperative infection or organ failure.
  4. Follow-up Compliance: Unwillingness to participate in prospective follow-up or inability to complete the follow-up schedule (applicable only to the prospective phase).

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

Cohorts and Interventions

Group / Cohort
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 the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
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.
The primary endpoint is the AUC-ROC of the model in predicting postoperative bleeding requiring re-operation within 30 days after surgery

Collaborators and Investigators

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

Sponsor

First Affiliated Hospital of Zhejiang University

Collaborators

Jinhua Municipal Central Hospital
Second Affiliated Hospital of Nanchang University

Investigators

  • Study Chair: Jichao Qin, M.D., Zhejiang University

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 (Estimated)

April 10, 2026

Primary Completion (Estimated)

December 31, 2027

Study Completion (Estimated)

January 31, 2028

Study Registration Dates

First Submitted

March 10, 2026

First Submitted That Met QC Criteria

April 7, 2026

First Posted (Actual)

April 13, 2026

Study Record Updates

Last Update Posted (Actual)

April 13, 2026

Last Update Submitted That Met QC Criteria

April 7, 2026

Last Verified

March 1, 2026

More Information

Terms related to this study

Additional Relevant MeSH Terms

Other Study ID Numbers

  • IIT20260161B

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

NO

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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