Clinical trials

This study aims to evaluate the efficacy and safety of HIPEC combined with NIPS and tislelizumab conversion therapy for gastric/gastroesophageal junction cancer with positive cytology alone (CY1P0) or a Peritoneal Carcinomatosis Index (PCI) ≤10

For patients diagnosed with early gastric cancer involving submucosal invasion, super-ESD indications, or lymph node metastasis, a combination of preoperative endoscopic ultrasound and abdominal contrast-enhanced CT was utilized to ascertain the depth of tumor invasion and to identify any suspicious metastatic lymph nodes in the vicinity of the stomach. Subsequently, a local full-thickness resection, coupled with or followed by individualized precise lymph node dissection, was conducted to fulfill the following objectives: ① To investigate the safety, feasibility, and efficacy of local resection for patients meeting super-ESD criteria; ② To offer a clinical foundation for the individualized and precise lymph node dissection treatment of early gastric cancer.

The clinical trial titled "Development and Application of an Artificial Intelligence-Driven Accurate Identification Model for Gastric Cancer Lymph Node Metastasis" aims to enhance the detection and treatment of gastric cancer through the utilization of cutting-edge artificial intelligence (AI) technology. This study will develop an AI-driven model designed to accurately identify lymph node metastasis in patients with gastric cancer, which is crucial for staging the disease and planning effective treatment strategies. The trial will involve a multidisciplinary team of oncologists, radiologists, data scientists, and AI experts who will collaborate to create a robust and precise identification system. Participants will undergo standard diagnostic procedures, and the AI model will analyze imaging and pathological data to predict lymph node involvement. By comparing the AI model's predictions with traditional diagnostic methods, the study seeks to validate the model's accuracy and efficiency. This approach is expected to improve early detection rates, reduce diagnostic errors, and ultimately lead to better clinical outcomes for patients with gastric cancer. The successful implementation of this AI-driven model could revolutionize the current standards of care and serve as a blueprint for integrating AI technologies in other cancer diagnoses and treatments.