[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-studies-list:{\"conditionNormalized\":\"mucinous-cystadenoma-of-pancreas\",\"overallStatus\":[\"RECRUITING\",\"AVAILABLE\",\"NOT_YET_RECRUITING\"],\"orderBy\":\"LastUpdateSubmitDate:desc\",\"size\":25,\"offset\":0}":3,"health-study-condition:mucinous-cystadenoma-of-pancreas":25},{"pageToken":4,"total":5,"offset":6,"count":5,"results":7},null,1,0,[8],{"id":9,"slug":10,"hasResults":11,"nctId":12,"briefTitle":13,"officialTitle":14,"acronym":4,"eligibilityCriteria":15,"healthyVolunteers":11,"sex":16,"minAge":17,"maxAge":4,"enrollmentInfo":18,"targetDuration":4,"studyType":21,"phases":4,"briefSummary":22,"conditions":23,"keywords":30,"overallStatus":38,"whyStopped":4,"lastUpdateSubmitDate":39,"lastUpdatePostDateStruct":40,"startDateStruct":43,"completionDateStruct":45,"leadSponsor":47,"locationsCount":50},"100628610","management-of-pancreatic-cystic-lesions-using-artificial-intelligence-based-on-eus-and-multimodal-data-100628610",false,"NCT07463872","Management of Pancreatic Cystic Lesions Using Artificial Intelligence Based on EUS and Multimodal Data","A Multimodal Artificial Intelligence Model for Subtyping Diagnosis and Clinical Management of Pancreatic Cystic Lesions Based on Endoscopic Ultrasound and Clinical Information","Inclusion criteria:\n\n* Patients whose EUS results indicates pancreatic cystic or cystoid lesions;\n* Mucinous lesions: including mucinous cystic neoplasm (MCN), intraductal papillary mucinous neoplasm (IPMN);\n* Non-mucinous lesions: including pancreatic pseudocyst, serous cystic neoplasm (SCN), cystic neuroendocrine tumor (cNET).\n\nExclusion criteria:\n\n* Patients whose age is less than 18 years old;\n* Patients who have undergone pancreatic surgery before the EUS examination;\n* Patients who have received chemotherapy and radiotherapy for pancreatic tumors before the EUS examination;\n* Pathological results indicate that pancreatic lesions are metastatic lesions from other sites;\n* Patients whose EUS images or reports are missing;\n* EUS image quality does not meet the requirements for review, such as blurry imaging or containing artifacts, biopsy needles, measuring scales, or other additional annotations that are not part of the original EUS image;\n* Patients whose final diagnosis is unclear.","ALL","18 Years",{"count":19,"type":20},500,"ESTIMATED","OBSERVATIONAL","The primary objective is to construct a multimodal AI model (Cyst-AI) based on EUS images and clinical data such as imaging features(CT or MRI) and laboratory tests to assist endoscopists in the diagnosis of pancreatic cystic lesions(PCLs), mainly differentiating mucinous from non-mucinous lesions.\n\nThe secondary objective is to evaluate the model's effectiveness in risk stratification and clinical management for patients with PCLs.",[24,25,26,27,28,29],"Pancreatic Cystic Lesion","Mucinous Cystadenoma of Pancreas","Intraductal Papillary Mucinous Neoplasm of Pancreas","Pseudocyst Pancreas","Serous Cystadenoma","Neuroendocrine Tumors, NET",[31,32,33,34,35,36,37],"pancreatic cystic lesions","artificial intelligence","endoscopic ultrasound","multimodal","differentiation","risk stratification","clinical management","RECRUITING","2026-03-05",{"date":41,"type":42},"2026-03-11","ACTUAL",{"date":44,"type":42},"2025-01-01",{"date":46,"type":20},"2026-06",{"name":48,"class":49},"Huazhong University of Science and Technology","OTHER",2]