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

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 Devel