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.
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).
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;
The cohort will be selected from several hospitals in China, including Tongji Hospital, Tongji Medical College, HUST.
Cohort
Retrospective
Cyst-AI model
Diagnostic testThe 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.
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.
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.
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.
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.
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.
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.
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