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

NCT07525765 | Recruiting

• 1 other identifier: IIT20260161B
• Study Type: observational
• Target: 7,000
• Locations: 1 country
• Sites: 1

Brief 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. 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.

Conditions

Gastric Cancer; Gastrectomy for Gastric Cancer

Outcome Measures

Primary Outcomes (1)

• 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.

  The primary endpoint is the AUC-ROC of the model in predicting postoperative bleeding requiring re-operation within 30 days after surgery

Study Arms (3)

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

Eligibility Criteria

• Age: 18 Years - 90 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• 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.

You may qualify if:

• 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.

You may not qualify if:

• 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).

Sponsors & Collaborators

• Jinhua Municipal Central Hospital: collaborator
• First Affiliated Hospital of Zhejiang University: lead
• Second Affiliated Hospital of Nanchang University: collaborator

Study Sites (1)

The First Affiliated Hospital, Zhejiang University School of Medicine Yuhang Campus

Hangzhou, Zhejiang, 330100, China

RECRUITING

MeSH Terms

Conditions

Stomach Neoplasms

Condition Hierarchy (Ancestors)

Gastrointestinal Neoplasms; Digestive System Neoplasms; Neoplasms by Site; Neoplasms; Digestive System Diseases; Gastrointestinal Diseases; Stomach Diseases

Study Officials

• Jichao Qin, M.D.

  Zhejiang University

  STUDY CHAIR

Central Study Contacts

Jianghao Li, B.S. in Computer Science

CONTACT

86+15968774033; 12518934@zju.edu.cn

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: OTHER
• Target Duration: 30 Days
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: March 10, 2026
• First Posted: April 13, 2026
• Study Start: April 10, 2026
• Primary Completion (Estimated): December 31, 2027
• Study Completion (Estimated): January 31, 2028
• Last Updated: April 13, 2026

Record last verified: 2026-03

Data Sharing

• IPD Sharing: Will not share

Locations

CN (1)