Pancreatic Cystic LesionNCT07463872
Management of Pancreatic Cystic Lesions Using Artificial Intelligence Based on EUS and Multimodal Data
An observational study of Pancreatic Cystic Lesion and Mucinous Cystadenoma of Pancreas, run by Huazhong University of Science and Technology at 2 sites in China.
Trial 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. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Pancreatic Cystic Lesion, Mucinous Cystadenoma of Pancreas, Intraductal Papillary Mucinous Neoplasm of Pancreas
- What is being tested?
- Cyst-AI model
- How many people
- 500 participants
- Where
- 2 sitesChina
- Study dates
- 1 Jan 2025to Jun 2026
- Phase
- Observational
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: The cohort will be selected from several hospitals in China, including Tongji Hospital, Tongji Medical College, HUST.
Who may be able to join
- 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).
Who cannot join
- 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.
Trial locations and contacts
2 sites in 1 country. Contact the study team or a site near you to ask about taking part.
China2 sites, 2 recruiting
- Not yet recruiting
Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology
Wuhan, Hubei, 430030
Bin Cheng, 86-13986097542, b.cheng@tjh.tjmu.edu.cn
- Recruiting
Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology
Wuhan, Hubei, 430030
Bin Cheng, +8613986097542, b.cheng@tjh.tjmu.edu.cn
About this study
Official title: A Multimodal Artificial Intelligence Model for Subtyping Diagnosis and Clinical Management of Pancreatic Cystic Lesions Based on Endoscopic Ultrasound and Clinical Information
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.
Read the 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.
- Conditions
- Pancreatic Cystic Lesion, Mucinous Cystadenoma of Pancreas, Intraductal Papillary Mucinous Neoplasm of Pancreas, Pseudocyst Pancreas, Serous Cystadenoma, Neuroendocrine Tumors, NET
- Being tested
- Cyst-AI model
- Keywords
- pancreatic cystic lesions, artificial intelligence, endoscopic ultrasound, multimodal, differentiation, risk stratification, clinical management
- Medical subject headings
- Pancreatic Intraductal Neoplasms, Pancreatic Pseudocyst, Cystadenoma, Serous, Neuroendocrine Tumors
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Cyst-AI modelDiagnostic 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.
| Study group | Role | What they receive |
|---|---|---|
| Cyst-EUS | Patients before 2026 with EUS pictures of pancreatic cystic lesions or cystoid-material lesions have been included in this cohort. Receives: Diagnostic Test: Cyst-AI model |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: 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.
Measured: 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
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.
Measured: Within 3 months upon completion of the diagnostic model training.
The clinical management performance of the clinical management model for mucinous PCLs
The performance of the Cyst-AI clinical management model to provide accurate clinical recommendations, 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.
Measured: 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
The performance of the Cyst-AI model in assisting endoscopists 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.
Measured: Within 1 months upon completion of the human-machine confrontational crossover study
The impact of the model on the decision-making process of endoscopists
Questionnaire for endoscopists after assessment will be used to evaluate the degree of impact.
Measured: Within 1 months upon completion of the human-machine confrontational crossover study.
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted11 Mar 2026
- Study start1 Jan 2025
- Primary completionApr 2026 (estimated)
- Study completionJun 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Pancreatic Cystic LesionStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Retrospective
- Enrolment
- 500 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Huazhong University of Science and Technology
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Bin Cheng, Huazhong University of Science and Technology
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07463872
- Sponsor's protocol ID
- Cyst-AI 2024
- Status last verified
- Mar 2026
- First submitted
- 9 Dec 2025
- Last updated
- 11 Mar 2026
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No