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

NCT07463872 · Status: RECRUITING · Type: OBSERVATIONAL · Enrollment: 500

Last updated 2026-03-11

No results posted yet for this study

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

Interventions

DIAGNOSTIC_TEST

Cyst-AI model

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.

Sponsors & Collaborators

  • Huazhong University of Science and Technology

    lead OTHER

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2025-01-01
Primary Completion
2026-04-30
Completion
2026-06-30

Countries

  • China

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT07463872 on ClinicalTrials.gov