AI-Driven Digital Twin and Drug Simulation for Esophageal Cancer

This study aims to integrate multi-omics data (genomics, transcriptomics, proteomics) from esophageal cancer patients with artificial intelligence and digital twin technology to construct personalized virtual patient models that precisely simulate individual responses to targeted therapies. Through a drug simulation platform, this study will rapidly screen potential effective drug combinations and optimize dosage and treatment regimens. The project attempts to replace portions of traditional clinical trials with virtual clinical trial technology, substantially shortening the R\&D cycle, reducing costs, and effectively addressing the complexity of individualized treatment. Specifically, this study will conduct a head-to-head virtual clinical trial parallel to a real-world investigator-initiated trial (IIT) in patients with locally advanced esophageal squamous cell carcinoma, comparing the efficacy predictions from the virtual model with actual clinical outcomes. The ultimate goal is to explore the application of large-scale AI models in esophageal cancer targeted therapy, provide personalized treatment recommendations, and advance the implementation of precision medicine in esophageal cancer.