This international, multicenter retrospective study aims to develop a deep learning (DL)-based predictive model to identify malignant transformation in pancreatic cystic lesions, improving upon current clinical guidelines. The model will integrate clinical, biochemical, and multimodal imaging data. Several 3D convolutional neural networks will be trained using advanced preprocessing, data augmentation, and hybrid fusion techniques. Model performance will be compared to that of existing international guidelines. The study involves no additional procedures for patients and adheres to strict data anonymization and privacy regulations.

Inclusion Criteria:

• Patients diagnosed with PCL(s ) who underwent pancreatic surgery in one of the participant centers. Surgical indication must adhere to at least one of current guidelines on PCLs management (6), based on clinical, biochemical, and radiological (MR and/or EUS) features.
• Pancreatic surgery B83performed for supposed increased risk of cyst(s) malignant degeneration following current guidelines on PCLs management (6).
• Absence of clinical, biochemical, radiological, and anatomopathological evidence of pancreatic cancer at pancreatic surgery.
• Non-opposition to the anonymous data processing by the included patients.

Exclusion Criteria:

• Patients presenting with evidence of pancreatic cancer at surgery.
• PCL(s) diagnosis and treatment performed without one between EUS and pancreatic MR. surgery performed in the absence of the criteria proposed by current guidelines.
• Unavailability of both preoperative EUS and pancreatic MR data.
• Unavailability of postoperative PCL(s) anatomopathological analysis results.
• SBO diagnosis performed without CT-scan.

Patients who underwent pancreatic surgery for pancreatic cystic lesions, in the absence of preoperative evidence of cancer. Indication of surgery must be based on one or more current guidelines concerning pancreatic cystic lesions management.