Pancreatic Cystic LesionMucinous Cystadenoma of PancreasIntraductal Papillary Mucinous Neoplasm of PancreasPseudocyst PancreasSerous CystadenomaNeuroendocrine Tumors, NET
Investigational drug(s) / intervention(s)
Cyst-AI model
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.
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.
Eligibility
Sex
ALL
Min age
18 Years
Max age
—
Healthy volunteers
No
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.
Primary outcome measure(s)
The performance of the diagnostic model in differentiating mucinous from non-mucinous PCLs — Within 3 months upon completion of the diagnostic model training. 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.
The risk stratification performance of the clinical management model for mucinous PCLs — Within 3 months upon completion of the risk stratification model training. 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.
Trial sites (2)
Facility
City
Region
Status
Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology
Wuhan
Hubei
Not Yet Recruiting
Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology
Wuhan
Hubei
Recruiting
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This page summarises publicly available registry data for informational purposes — not medical advice. Eligibility is determined by each study team; patients should discuss participation with their clinician.
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