Prediction of Occult Peritoneal Metastasis of Locally Advanced Gastric Cancer Using Multimodal Data Based on Artificial Intelligence Combined With Intraoperative Dynamic Video

NCT06478368 | Completed

• 1 other identifier: FUTURE06
• Study Type: observational
• Target: 1,000
• Locations: 1 country
• Sites: 1

Brief Summary

Brief Summary: Prediction of Occult Peritoneal Metastasis of Locally Advanced Gastric Cancer Using Multimodal Data Based on Artificial Intelligence Combined with Intraoperative Dynamic Video Gastric cancer, or stomach cancer, is a major health concern worldwide. For patients diagnosed with locally advanced gastric cancer (LAGC), one of the critical challenges is the detection of occult peritoneal metastasis. These metastases are cancerous cells that have spread to the peritoneum (the lining of the abdominal cavity) but are not easily detected by traditional imaging techniques or during surgery. Early and accurate detection of these hidden metastases can greatly influence treatment strategies and improve patient outcomes. This clinical study explores an innovative approach to address this challenge by combining artificial intelligence (AI) with multimodal data, including intraoperative dynamic video. This method leverages the power of AI to analyze complex and diverse data sources, providing a comprehensive and precise prediction of occult peritoneal metastasis during surgery. \*\*Hypothesis\*\* The study hypothesizes that an AI model integrating multimodal data, including intraoperative dynamic video, can accurately predict the presence of occult peritoneal metastasis in patients with locally advanced gastric cancer. By doing so, this approach aims to offer a noninvasive, real-time diagnostic tool that enhances the detection capabilities beyond traditional methods. Study Design

• Improved Surgical Decision-Making: Real-time prediction of occult metastasis can inform surgical strategies, enabling more precise and targeted interventions.
• Enhanced Patient Outcomes: Early and accurate detection allows for timely and appropriate treatments, potentially improving survival rates and quality of life for patients.
• Reduced Invasiveness: This method provides a noninvasive means of detecting metastasis, reducing the need for additional invasive procedures.
• Cost-Effectiveness: Early detection and treatment can lower overall healthcare costs by preventing the progression of the disease and reducing the need for extensive treatments at later stages. Conclusion This clinical study represents a significant advancement in the field of gastric cancer diagnostics. By leveraging AI to analyze multimodal data, including intraoperative dynamic video, it aims to provide a powerful tool for the early and accurate prediction of occult peritoneal metastasis in patients with locally advanced gastric cancer. The success of this approach could revolutionize the way metastases are detected and managed, ultimately leading to better outcomes for patients.

Conditions

This Study Uses AI and Multimodal Data to Noninvasively Detect Occult Peritoneal Metastasis

Outcome Measures

Primary Outcomes (1)

• Peritoneal metastasis

  Peritoneal metastasis

  2025-12-31

Secondary Outcomes (1)

• Free cancer cells in the peritoneal cavity

  2025-12-31

Interventions

Laparoscopic exploration DIAGNOSTIC_TEST

Laparoscopic exploration

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Probability Sample

Study Population

The study population consists of adult patients diagnosed with locally advanced gastric cancer (LAGC) who are scheduled for surgical treatment. These patients are at a stage where early detection of occult peritoneal metastasis is crucial for optimizing treatment strategies and improving outcomes.

You may qualify if:

• Diagnosis of Locally Advanced Gastric Cancer (LAGC): Patients must have a confirmed diagnosis of locally advanced gastric cancer.
• Age: Participants must be 18 years or older.
• Consent: Patients must be able to provide informed consent.
• Adequate Organ Function: Participants must have sufficient bone marrow, liver, and kidney function, as defined by specific laboratory criteria.
• Performance Status: Patients should have an Eastern Cooperative Oncology Group (ECOG) performance status of 0 to 2.
• Willingness to Provide Data: Participants must agree to provide intraoperative dynamic video and other required data for analysis.
• Scheduled for Surgery: Patients must be scheduled for surgical treatment of their gastric cancer.

You may not qualify if:

• Distant Metastases: Patients with confirmed distant metastases (beyond the peritoneum) are excluded.
• Other Malignancies: Individuals with a history of other malignancies within the past five years, except for adequately treated basal cell or squamous cell skin cancer, or carcinoma in situ of the cervix.
• Severe Comorbid Conditions: Patients with severe or uncontrolled comorbid conditions, such as significant cardiovascular disease, uncontrolled diabetes, severe infections, or other conditions that could interfere with study participation or outcomes.
• Pregnancy and Lactation: Pregnant or lactating women are excluded due to potential risks to the fetus or infant.
• Immunocompromised Status: Patients who are immunocompromised, such as those with HIV/AIDS, or who are receiving immunosuppressive therapy.
• Concurrent Participation in Other Clinical Trials: Individuals currently participating in another clinical trial that could interfere with this study's procedures or outcomes.
• Allergies to Study Materials: Patients with known allergies to any components of the study materials used for data collection and analysis.
• Non-compliance: Individuals deemed unable or unwilling to comply with the study procedures and follow-up requirements.

Sponsors & Collaborators

• Qun Zhao: lead

Study Sites (1)

Department of General Surgery

Shijiazhuang, Hebei, 050011, China

Location

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR INVESTIGATOR
• PI Title: Professor

Study Record Dates

• First Submitted: June 22, 2024
• First Posted: June 27, 2024
• Study Start: January 1, 2024
• Primary Completion: June 30, 2024
• Study Completion: June 30, 2024
• Last Updated: August 6, 2025

Record last verified: 2024-06

Locations

CN (1)