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

Overview

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 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. 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.