Management of Pancreatic Cystic Lesions Using Artificial Intelligence Based on EUS and Multimodal Data

NCT07463872 | Recruiting

• 1 other identifier: Cyst-AI 2024
• Study Type: observational
• Target: 500
• Locations: 1 country
• Sites: 2

Brief 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.

Conditions

Pancreatic Cystic Lesion; Mucinous Cystadenoma of Pancreas; Intraductal Papillary Mucinous Neoplasm of Pancreas; Pseudocyst Pancreas; Serous Cystadenoma; Neuroendocrine Tumors, NET

Keywords

pancreatic cystic lesions; artificial intelligence; endoscopic ultrasound; multimodal; differentiation; risk stratification; clinical management

Outcome Measures

Primary Outcomes (2)

• The performance of the diagnostic model in differentiating mucinous from non-mucinous PCLs

  The performance of the Cyst-AI diagnostic model will be evaluated using the area under the receiver operating characteristic curve (AUC-ROC), with sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV) calculated from the model's predictions on the independent validation dataset. PCLs: pancreatic cystic lesions.

  Within 3 months upon completion of the diagnostic model training.

• The risk stratification performance of the clinical management model for mucinous PCLs

  The performance of the Cyst-AI risk stratification model to correctly classify lesions into "low risk", "intermediate risk" and "high risk", will be evaluated using the area under the receiver operating characteristic curve (AUC-ROC), with sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV) calculated from the model's predictions on the independent validation dataset. PCLs: pancreatic cystic lesions.

  Within 3 months upon completion of the risk stratification model training.

Secondary Outcomes (4)

• The performance of the diagnostic model in differentiating specific types of PCLs

  Within 3 months upon completion of the diagnostic model training.

• The clinical management performance of the clinical management model for mucinous PCLs

  Within 3 months upon completion of the clinical management model training.

• The performance of the model in assisting endoscopists of different levels in diagnosing and managing PCLs

  Within 1 months upon completion of the human-machine confrontational crossover study

• The impact of the model on the decision-making process of endoscopists

  Within 1 months upon completion of the human-machine confrontational crossover study.

Study Arms (1)

Cyst-EUS

Patients before 2026 with EUS pictures of pancreatic cystic lesions or cystoid-material lesions have been included in this cohort.

Diagnostic Test: Cyst-AI model

Interventions

Cyst-AI model DIAGNOSTIC_TEST

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.

Cyst-EUS

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

The cohort will be selected from several hospitals in China, including Tongji Hospital, Tongji Medical College, HUST.

You may qualify if:

• 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).

You may not qualify if:

• 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.

Sponsors & Collaborators

• Huazhong University of Science and Technology: lead

Study Sites (2)

Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology

Wuhan, Hubei, 430030, China

NOT YET RECRUITING

Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology

Wuhan, Hubei, 430030, China

RECRUITING

MeSH Terms

Conditions

Pancreatic Intraductal Neoplasms; Pancreatic Pseudocyst; Cystadenoma, Serous; Neuroendocrine Tumors

Condition Hierarchy (Ancestors)

Neoplasms, Ductal, Lobular, and Medullary; Neoplasms, Glandular and Epithelial; Neoplasms by Histologic Type; Neoplasms; Pancreatic Neoplasms; Digestive System Neoplasms; Neoplasms by Site; Endocrine Gland Neoplasms; Digestive System Diseases; Pancreatic Diseases; Endocrine System Diseases; Pancreatic Cyst; Cysts; Cystadenoma; Adenoma; Neoplasms, Cystic, Mucinous, and Serous; Neuroectodermal Tumors; Neoplasms, Germ Cell and Embryonal; Neoplasms, Nerve Tissue

Central Study Contacts

Bin Cheng

CONTACT

86-13986097542; b.cheng@tjh.tjmu.edu.cn

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Professor

Study Record Dates

• First Submitted: December 9, 2025
• First Posted: March 11, 2026
• Study Start: January 1, 2025
• Primary Completion: April 1, 2026
• Study Completion: June 1, 2026
• Last Updated: March 11, 2026

Record last verified: 2026-03

Locations

CN (2)