A Multimodal Artificial Intelligence Model for Subtyping Diagnosis and Clinical Management of Pancreatic Cystic Lesions Based on Endoscopic Ultrasound and Clinical Information
Status: Recruiting
Location: See all (2) locations...
Intervention Type: Diagnostic test
Study Type: Observational
SUMMARY
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. The secondary objective is to evaluate the model's effectiveness in risk stratification and clinical management for patients with PCLs.
Eligibility
Participation Requirements
Sex: All
Minimum Age: 18
Healthy Volunteers: f
View:
• Patients whose EUS results indicates pancreatic cystic or cystoid lesions;
• Mucinous lesions: including mucinous cystic neoplasm (MCN), intraductal papillary mucinous neoplasm (IPMN);
• Non-mucinous lesions: including pancreatic pseudocyst, serous cystic neoplasm (SCN), cystic neuroendocrine tumor (cNET).
Locations
Other Locations
China
Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology
RECRUITING
Wuhan
Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology
NOT_YET_RECRUITING
Wuhan
Contact Information
Primary
Bin Cheng
b.cheng@tjh.tjmu.edu.cn
86-13986097542
Time Frame
Start Date: 2025-01-01
Estimated Completion Date: 2026-06
Participants
Target number of participants: 500
Treatments
Cyst-EUS
Patients before 2026 with EUS pictures of pancreatic cystic lesions or cystoid-material lesions have been included in this cohort.
Related Therapeutic Areas
Sponsors
Leads: Huazhong University of Science and Technology