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Management of Pancreatic Cystic Lesions Using Artificial Intelligence Based on EUS and Multimodal Data
NCT07463872 · Huazhong University of Science and Technology
In plain English
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Official title
A Multimodal Artificial Intelligence Model for Subtyping Diagnosis and Clinical Management of Pancreatic Cystic Lesions Based on Endoscopic Ultrasound and Clinical Information
About this study
With the development of medical imaging technology, the detection rate of pancreatic cystic lesions (PCLs) has been increasing notably. Although most cysts are benign, a considerable subset has the potential for malignant transformation. Clinical management is based on diagnosis and risk stratification. For PCLs，different diagnosis and risk stratification lead to entirely different clinical strategies and outcomes, which are closely related to the quality of life, economic burden, and psychological stress of patients. Endoscopic ultrasound (EUS) has played a crucial role in the further differential diagnosis of PCLs. Artificial intelligence (AI) has also shown great potential in clinical diagnosis and management. Thus, we plan to retrospectively collect patients' EUS imaging data, radiological and laboratory tests, and other clinical information to construct a model named Cyst-AI which integrates the function of diagnosis and clinical management, to assist in clinical decision-making.
Eligibility criteria
Inclusion criteria:
* 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 criteria:
* 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.
Study design
Enrollment target: 500 participants
Age groups: adult, older_adult
Timeline
Starts: 2025-01-01
Estimated completion: 2026-06
Last updated: 2026-03-11
Interventions
Diagnostic Test: Cyst-AI model
Primary outcomes
- • The performance of the diagnostic model in differentiating mucinous from non-mucinous PCLs (Within 3 months upon completion of the diagnostic model training.)
- • The risk stratification performance of the clinical management model for mucinous PCLs (Within 3 months upon completion of the risk stratification model training.)
Sponsor
Huazhong University of Science and Technology · other
Contacts & investigators
ContactBin Cheng · contact · b.cheng@tjh.tjmu.edu.cn · 86-13986097542
All locations (2)
Tongji Hospital, Tongji Medical College, Huazhong University of Science and TechnologyNot Yet Recruiting
Wuhan, Hubei, China
Tongji Hospital, Tongji Medical College, Huazhong University of Science and TechnologyRecruiting
Wuhan, Hubei, China