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Recruiting NCT07463872

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

— Observational Pancreatic Cystic Lesion Mucinous Cystadenoma of Pancreas Intraductal Papillary Mucinous Neoplasm of Pancreas Pseudocyst Pancreas

For patients and families

In plain language

An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.

What is being studied
The protocol lists: Cyst-AI model.
Who it may be relevant to
Registry conditions: Pancreatic Cystic Lesion, Mucinous Cystadenoma of Pancreas, Intraductal Papillary Mucinous Neoplasm of Pancreas, Pseudocyst Pancreas. Basic parameters: from 18 years · All.
What needs checking
Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
Where it takes place
China
Next step
Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →

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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

Overview

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.

Detailed description

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.

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.

Primary outcome measures

  • The performance of the diagnostic model in differentiating mucinous from non-mucinous PCLs [Time frame: Within 3 months upon completion of the diagnostic model training.]
  • The risk stratification performance of the clinical management model for mucinous PCLs [Time frame: Within 3 months upon completion of the risk stratification model training.]
Secondary outcome measures (4)
  • The performance of the diagnostic model in differentiating specific types of PCLs [Time frame: Within 3 months upon completion of the diagnostic model training.]
  • The clinical management performance of the clinical management model for mucinous PCLs [Time frame: 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 [Time frame: Within 1 months upon completion of the human-machine confrontational crossover study]
  • The impact of the model on the decision-making process of endoscopists [Time frame: Within 1 months upon completion of the human-machine confrontational crossover study.]

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.

Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.

Healthy volunteers: No

Study design

Observational model
Cohort

Study locations

China · 2 centers
  • Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology — Wuhan
  • Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology — Wuhan

Identifiers

NCT: NCT07463872 · Cyst-AI 2024

Primary sources (government registries)

View this study on ClinicalTrials.gov ↗