This Study Uses AI and Multimodal Data to Noninvasively Detect Occult Peritoneal MetastasisNCT06478368
Prediction of Occult Peritoneal Metastasis of Locally Advanced Gastric Cancer Using Multimodal Data Based on Artificial Intelligence Combined With Intraoperative Dynamic Video
An observational study of This Study Uses AI and Multimodal Data to Noninvasively Detect Occult Peritoneal Metastasis, run by Qun Zhao at 1 site in China.
Trial 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. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- This Study Uses AI and Multimodal Data to Noninvasively Detect Occult Peritoneal Metastasis
- What is being tested?
- Laparoscopic exploration
- How many people
- 1,000 participants
- Where
- 1 siteChina
- Study dates
- 1 Jan 2024to 30 Jun 2024
- Phase
- Observational
- Are there results?
- None posted in the registry
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 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
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.
Who may be able to join
- 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.
Who cannot join
- 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.
Trial locations and contacts
1 site in 1 country.
About this study
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.
Read more
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
- Participants: The study will involve patients diagnosed with locally advanced gastric cancer who are scheduled for surgical treatment. These patients will undergo standard preoperative assessments to confirm their eligibility.
- Data Collection: During surgery, dynamic video recordings of the abdominal cavity will be captured. Additionally, other relevant multimodal data such as imaging results, histopathological findings, and clinical parameters will be collected.
- AI Model Development: The collected data will be used to train and validate an AI model. The model will analyze the dynamic video along with other multimodal data to identify patterns and markers indicative of occult peritoneal metastasis.
- Evaluation and Validation: The AI model's predictions will be compared with the actual surgical and histopathological outcomes to assess its accuracy. The performance of the AI model will be evaluated in terms of sensitivity, specificity, and overall diagnostic accuracy.
- Outcome Measures: The primary outcome measure will be the accuracy of the AI model in predicting occult peritoneal metastasis. Secondary outcomes will include the impact of this prediction on surgical decision-making, patient outcomes, and potential improvements in survival rates.
Significance
The detection of occult peritoneal metastasis in locally advanced gastric cancer is crucial for effective treatment planning. Traditional diagnostic methods often fail to identify these hidden metastases until they have significantly progressed, limiting treatment options and adversely affecting prognosis. By integrating AI with intraoperative dynamic video and other multimodal data, this study aims to develop a real-time, noninvasive diagnostic tool that can detect these metastases more accurately and earlier than conventional methods.
The potential benefits of this approach include:
- 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
- Being tested
- Laparoscopic exploration
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Laparoscopic explorationDiagnostic test
Laparoscopic exploration
What is being measured
The primary outcome is the main question the study is designed to answer.
Peritoneal metastasis
Peritoneal metastasis
Measured: 2025-12-31
Secondary outcomes (1)
Free cancer cells in the peritoneal cavity
Free cancer cells in the peritoneal cavity
Measured: 2025-12-31
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted27 Jun 2024
- Study start1 Jan 2024
- Primary completion30 Jun 2024
- Study completion30 Jun 2024
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for This Study Uses AI and Multimodal Data to Noninvasively Detect Occult Peritoneal MetastasisStudies 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
- 1,000 participants (actual)
Sponsor and investigators
- Lead sponsor
- Qun Zhao
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor-investigator, Qun Zhao, Hebei Medical University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06478368
- Sponsor's protocol ID
- FUTURE06
- Status last verified
- Jun 2024
- First submitted
- 22 Jun 2024
- Last updated
- 6 Aug 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided
Study at a glance
From the registry record, last updated by the sponsor on 6 Aug 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Prediction of Occult Peritoneal Metastasis of Locally Advanced Gastric Cancer Using Multimodal Data Based on Artificial Intelligence Combined With Intraoperative Dynamic Video
- ClinicalTrials.gov ID
- NCT06478368
- Acronym / sponsor ID
- FUTURE06
- Conditions
- This Study Uses AI and Multimodal Data to Noninvasively Detect Occult Peritoneal Metastasis
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 1,000 participants (actual)
- Lead sponsor
- Qun ZhaoUniversity, hospital or other
- Study start
- 1 Jan 2024 (actual)
- Primary completion
- 30 Jun 2024 (actual)
- Study completion
- 30 Jun 2024 (actual)
- First posted
- 27 Jun 2024 (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.
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
- Participants: The study will involve patients diagnosed with locally advanced gastric cancer who are scheduled for surgical treatment. These patients will undergo standard preoperative assessments to confirm their eligibility.
- Data Collection: During surgery, dynamic video recordings of the abdominal cavity will be captured. Additionally, other relevant multimodal data such as imaging results, histopathological findings, and clinical parameters will be collected.
- AI Model Development: The collected data will be used to train and validate an AI model. The model will analyze the dynamic video along with other multimodal data to identify patterns and markers indicative of occult peritoneal metastasis.
- Evaluation and Validation: The AI model's predictions will be compared with the actual surgical and histopathological outcomes to assess its accuracy. The performance of the AI model will be evaluated in terms of sensitivity, specificity, and overall diagnostic accuracy.
- Outcome Measures: The primary outcome measure will be the accuracy of the AI model in predicting occult peritoneal metastasis. Secondary outcomes will include the impact of this prediction on surgical decision-making, patient outcomes, and potential improvements in survival rates.
Significance
The detection of occult peritoneal metastasis in locally advanced gastric cancer is crucial for effective treatment planning. Traditional diagnostic methods often fail to identify these hidden metastases until they have significantly progressed, limiting treatment options and adversely affecting prognosis. By integrating AI with intraoperative dynamic video and other multimodal data, this study aims to develop a real-time, noninvasive diagnostic tool that can detect these metastases more accurately and earlier than conventional methods.
The potential benefits of this approach include:
- 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.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- 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.
Key inclusion criteria (7 listed)
- 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.
Key exclusion criteria (8 listed)
- 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.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Laparoscopic explorationLaparoscopic exploration | Diagnostic test | – |
Primary outcome
Peritoneal metastasis
Time frame: 2025-12-31
Peritoneal metastasis
1 secondary outcome
Free cancer cells in the peritoneal cavity
Time frame: 2025-12-31
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 6 Aug 2025.
- Individual participant dataSharing planned: Undecided
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. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
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 This Study Uses AI and Multimodal Data to Noninvasively Detect Occult Peritoneal Metastasis, 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 7 inclusion and 8 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: finished, as of the sponsor's last update on 6 Aug 2025. 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?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Peritoneal metastasis (time frame: 2025-12-31). 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 6 Aug 2025; AzTrials last refreshed its copy on 8 Nov 2026.