Machine Learning-driven Noninvasive Screening of Transcriptomics Liquid Biopsies for Early Diagnosis of Occult Peritoneal Metastases in Locally Advanced Gastric Cancer

Brief Summary: Machine Learning-Driven Noninvasive Screening of Transcriptomics Liquid Biopsies for Early Diagnosis of Occult Peritoneal Metastases in Locally Advanced Gastric Cancer Gastric cancer, commonly known as stomach cancer, is a significant health issue worldwide, especially when it progresses to an advanced stage. One of the major challenges in treating locally advanced gastric cancer (LAGC) is the detection of occult (hidden) peritoneal metastases. These metastases are cancer cells that spread to the peritoneum (the lining of the abdominal cavity) but are not easily detectable with standard imaging techniques or during surgery. Early and accurate detection of these hidden metastases can significantly improve treatment strategies and outcomes for patients. This clinical study explores an innovative approach to tackle this problem using machine learning (ML) technology and liquid biopsies. Liquid biopsies are a noninvasive method that involves analyzing blood samples to detect cancer-related biomarkers, such as circulating tumor DNA or RNA. This study specifically focuses on the transcriptomics of liquid biopsies, which refers to the analysis of RNA molecules to understand the gene expression profiles associated with cancer. Hypothesis The hypothesis of this study is that machine learning algorithms can effectively analyze transcriptomics data from liquid biopsies to detect occult peritoneal metastases in patients with locally advanced gastric cancer. By doing so, this method could provide a noninvasive, accurate, and early diagnosis of metastases, which are otherwise difficult to identify through traditional methods. Study Design 1. Participants: The study will enroll patients diagnosed with locally advanced gastric cancer. These patients will undergo standard diagnostic and staging procedures to confirm their cancer stage and overall health status. 2. Sample Collection: Blood samples will be collected from the participants at various stages of their