Clinical trial · Observational
Management of Pancreatic Cystic Lesions Using Artificial Intelligence Based on EUS and Multimodal Data
A Multimodal Artificial Intelligence Model for Subtyping Diagnosis and Clinical Management of Pancreatic Cystic Lesions Based on Endoscopic Ultrasound and Clinical Information
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 8, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260908-000001
Summary
Brief summary (as posted)
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
Conditions (6)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Intraductal Papillary Mucinous Neoplasm of Pancreas | — | UNRESOLVED | — |
| Mucinous Cystadenoma of Pancreas | — | UNRESOLVED | — |
| Neuroendocrine Tumors, NET | Neuroendocrine Tumor | ONTOLOGY_EXACT | 0.85 |
| Pancreatic Cystic Lesion | — | UNRESOLVED | — |
| Pseudocyst Pancreas | — | UNRESOLVED | — |
| Serous Cystadenoma | Serous Cystadenoma | ONTOLOGY_EXACT | 0.98 |
Interventions
Interventions (1)
| Intervention | Type | Mapped drug | Match |
|---|---|---|---|
| Cyst-AI model | Diagnostic Test | — | UNRESOLVED |
Design
Arms and outcomes
Arms (1)
- label
- Cyst-EUS
- description
- Patients before 2026 with EUS pictures of pancreatic cystic lesions or cystoid-material lesions have been included in this cohort.
- interventionNames
- Diagnostic Test: Cyst-AI model
Primary outcomes (2)
- measure
- The performance of the diagnostic model in differentiating mucinous from non-mucinous PCLs
- timeFrame
- Within 3 months upon completion of the diagnostic model training.
- description
- 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.
- measure
- The risk stratification performance of the clinical management model for mucinous PCLs
- timeFrame
- Within 3 months upon completion of the risk stratification model training.
Eligibility
Eligibility (as posted)
- Sex
- All
- Minimum age
- 18 Years
Show eligibility criteria text
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.
References
Publications (0)
Data not yet available