Ignite Creation Date:
2024-07-17 @ 10:41 AM
Last Modification Date:
2024-10-26 @ 3:33 PM
Study NCT ID:
NCT06478368
Status:
NOT_YET_RECRUITING
Last Update Posted:
2024-06-27
First Post:
2024-06-22
Brief Title:
Prediction of Occult Peritoneal Metastasis of Locally Advanced Gastric Cancer Using Multimodal Data Based on Artificial Intelligence Combined With Intraoperative Dynamic Video
Sponsor:
Qun Zhao
Organization:
Hebei Medical University
Study Overview
Official Title:
Prediction of Occult Peritoneal Metastasis of Locally Advanced Gastric Cancer Using Multimodal Data Based on Artificial Intelligence Combined With Intraoperative Dynamic Video
Status:
NOT_YET_RECRUITING
Status Verified Date:
2024-06
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
None
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
1 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
2 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
3 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
4 Evaluation and Validation The AI models 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
5 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
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
1 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
2 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
3 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
4 Evaluation and Validation The AI models 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
5 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
Detailed Description:
None
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None