Pancreatic Cystic Lesions

The purpose of this research is to learn if the PanCystPro assay can help doctors in making decisions about treatment and monitoring of pancreatic cysts. The PanCystPro test measures glucose, carcinoembryonic antigen (CEA), and gastricsin biomarkers on fluid obtained from a pancreatic cyst. The test reports if the cyst fluid should be considered "Non-mucinous" or "Mucinous". Mucinous pancreatic cysts are more likely to progress to cancer while non-mucinous cysts seldom progress to cancer.

This multicenter clinical trial evaluates an artificial intelligence (AI) system designed to assist in the diagnosis and management of pancreatic diseases. Using contrast-enhanced CT scans, the study compares the AI's recommendations against the decisions of experienced clinicians to verify the system's accuracy and safety in a real-world setting. Patients are categorized into three management groups: Intervention (surgery/treatment), Intensive Surveillance (close monitoring), or Routine Surveillance (standard follow-up). The primary goal is to determine if the AI system can reliably classify patients, reduce the risk of missing malignant lesions, and prevent unnecessary surgeries, thereby improving clinical decision-making for pancreatic conditions.

The purpose To determine the diagnostic potential of various biological markers in blood and cyst fluid aspirates from patients with Pancreatic Cystic Lesions (PCLs). Research design This is a 10-year prospective cohort and pancreatic cyst fluid repository study enrolling all patients diagnosed with pancreatic cyst and undergoing the cyst aspiration. Procedures to be used Blood Sample Cyst Fluid Sample Data Collection: Medical Record Number Demographics (age, sex, gender, race) Contact information History of alcohol use and IV and other recreational drugs and narcotics use/abuse Medication history Past hospitalizations, diagnoses, and treatment Physical examination findings Imaging data of abdominal and chest regions, including and not limited to ultrasonography, magnetic resonance imaging (MRI), computed tomography (CT) Future admissions, diagnoses, treatment including histopathological findings of resected specimens and blood reports End of study data: clinical progression of disease, cyst size, wall thickening, calcification, communication with pancreatic duct, string sign, cytology, immunohistochemical findings, assay levels of lipase, amylase CEA (carcinoembryonic antigen), carbohydrate antigen19-9 (CA 19-9), and other biomarkers. Risks and potential benefits The risks associated with this study are slight discomfort or bruising from the blood sampling and the possible loss of confidentiality if the patient data or information is inadvertently disclosed outside of this study. The patient will not receive any additional benefit from the study aside from those received as part of routine standard of care. Importance of knowledge that may reasonably be expected to result The knowledge gained from this study may benefit other patients with Pancreatic Cyst Lesions in the future.

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.