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This study focused on exploring new comprehensive treatment strategies for patients with unresectable combined hepatocellular-cholangiocarcinoma, classifying patients with CHC subtypes based on the combination of artificial intelligence and multi-omics, and exploring the optimal treatment strategies for patients with different subtypes, helping clinicians to screen the most beneficial groups of various treatment schemes, and providing new ideas for safe treatment of high-risk patients.
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This will be a multicenter observational study that will be conducted at several leading liver cancer treatment centers. The study will include adult patients with histologically/cytologically confirmed unresectable CHC. Collect patient genomics, proteomics, immune microenvironment, pathology reports, medical images and clinical electronic medical records, etc., to form high-quality and deeply labeled data sets to support the subsequent development and application of AI large models. Based on multi-source heterogeneous data of CHC patients, a large model for comprehensive diagnosis and treatment was constructed. Firstly, multi-modal data of different stages of disease were integrated by using cross-modal multi-course fusion technology to achieve efficient fusion of complex data. Secondly, by fine-tuning the large model, tasks such as CHC classification, prognosis inference and treatment plan recommendation are accurately completed, and potential information in the diagnosis and treatment process is mined.
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Gao-Jun Teng, M.D
Data sourced from clinicaltrials.gov
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