AI to help tell apart types of pancreatic cysts on ultrasound images
Tests an AI model to sort pancreatic cysts using ultrasound images and clinical information.
Plain-language title generated from the registry record
Registry title: AI-Assisted Differentiation of Pancreatic Cystic Lesions Using EUS Images and Clinical Features
Key facts
- Where
- Shanghai, ChinaSee the site
Where in China
- Ruijin Hospital, Shanghai Jiao Tong University School of MedicineShanghai, ChinaNo longer taking part
Questions to ask the study team
- How often do I need to come in, and for how long each time?
- Are travel costs paid?
- Can I keep my own doctor?
- What happens if I want to stop?
Trial details
- Who runs it
- Ruijin Hospital
- Trial code
- NCT07919704
- Record updated
- 5 November 2026
- Original description
Pancreatic mucinous cystic neoplasms (MCNs), serous cystic neoplasms (SCNs), and pancreatic pseudocysts (PPCs) often exhibit overlapping imaging features on endoscopic ultrasound (EUS). However, these conditions require different clinical management strategies. Accurate differentiation is therefore important for appropriate treatment planning.Read full original descriptionShow less
Artificial Intelligence-Assisted Differentiation of Pancreatic Mucinous Cystic Neoplasms, Serous Cystic Neoplasms, and Pseudocysts Using Endoscopic Ultrasound Images and Clinical Features: An Ambispective Observational Study
Pancreatic mucinous cystic neoplasms (MCNs), serous cystic neoplasms (SCNs), and pancreatic pseudocysts (PPCs) often exhibit overlapping imaging features on endoscopic ultrasound (EUS). However, these conditions require different clinical management strategies. Accurate differentiation is therefore important for appropriate treatment planning.
This study aims to develop and validate an artificial intelligence (AI) model that integrates EUS images and clinical features to differentiate MCNs, SCNs, and PPCs. The study is designed as an ambispective observational diagnostic study consisting of retrospective model development, prospective temporal validation, and a reader study involving EUS physicians.
Approximately 400 adult patients with confirmed MCN, SCN, or PPC will be included. Retrospective clinical data and EUS images will be used for model development and internal validation. After model development is completed, newly diagnosed patients will be prospectively enrolled to independently evaluate the diagnostic performance of the model.
The primary outcome is the patient-level macro-averaged area under the receiver operating characteristic curve (macro-AUROC) for three-class classification in the prospective validation cohort. Secondary outcomes include diagnostic accuracy, sensitivity, specificity, calibration, clinical net benefit, and the performance of EUS physicians with and without AI assistance.
This study is expected to provide evidence for the potential clinical value of AI-assisted differentiation of pancreatic cystic lesions. No additional invasive procedures or study-specific treatments will be performed.
Inclusion: **Inclusion Criteria:**; Adults aged 18 years or older, regardless of sex; Patients who have undergone endoscopic ultrasound (EUS) examination; A final diagnosis of pancreatic mucinous cystic neoplasm (MCN), serous cystic neoplasm (SCN), or pancreatic pseudocyst (PPC); For MCN and SCN, a confirmed diagnosis based on surgical or histopathological findings; For PPC, a diagnosis established according to a predefined composite reference standard incorporating clinical history, imaging findings, and relevant diagnostic evidence; Availability of at least one EUS image clearly showing the target lesion; EUS examination performed before surgery, ablation, drainage, or other treatment of the target lesion; Availability of essential clinical information corresponding to the EUS examination; For the prospective validation cohort, enrollment after model finalization and provision of written informed consent according to the approved ethics protocol; **Exclusion Criteria:**; Severely degraded EUS image quality preventing reliable identification of the target lesion; Availability of only post-treatment or postoperative EUS images, without pretreatment imaging; Lack of reliable pathological confirmation for MCN or SCN; Inability to establish a clear correspondence between the EUS images and the pathologically confirmed lesion; Uncertain or disputed final diagnosis that remains unresolved after expert review; Concurrent pancreatic cystic neoplasms for which the target lesion cannot be clearly identified; Other pancreatic lesions, including intraductal papillary mucinous neoplasms (IPMNs), solid pseudopapillary neoplasms (SPNs), cystic neuroendocrine tumors, or cystic degeneration of solid tumors; Substantial missing clinical information preventing multimodal analysis; Refusal to provide or withdrawal of informed consent during the prospective phase.
Exclusion: -.