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.

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.

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.

The cohort will be selected from several hospitals in China, including Tongji Hospital, Tongji Medical College, HUST.