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
Huazhong University of Science and Technology
500 participants
Jan 1, 2025
OBSERVATIONAL
Conditions
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
Inclusion Criteria3
- 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).
Exclusion Criteria7
- Patients whose age is less than 18 years old;
- Patients who have undergone pancreatic surgery before the EUS examination;
- Patients who have received chemotherapy and radiotherapy for pancreatic tumors before the EUS examination;
- Pathological results indicate that pancreatic lesions are metastatic lesions from other sites;
- Patients whose EUS images or reports are missing;
- 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;
- Patients whose final diagnosis is unclear.
Interventions
The multi-center collected data will be divided into a training set, a validation set, and a test set for developing and testing the cyst-AI model.
Locations(2)
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NCT07463872