Confocal Laser Endomicroscopy for Pancreatic Cysts
This observational study is looking at how well Confocal Laser Endomicroscopy (nCLE) can help doctors diagnose pancreatic cysts. Pancreatic cysts are fluid-filled sacs on the pancreas. Researchers want to see if nCLE, along with other tests like EUS-FNA (Endoscopic Ultrasound with Fine Needle Aspiration) and cyst fluid analysis, can accurately identify different types of cysts and their risk. The study aims to enroll 500 participants aged 18 or older who are considering surgery for their pancreatic cyst, and whose cyst is at least 2.0 cm. The goal is to see how accurate these diagnostic methods are after 48 months, using surgical results as the "gold standard."
- Study design
- This is an observational study aiming to enroll 500 participants. It is not specified if it is randomized or blinded.
- What's involved
- You would undergo an EUS-guided nCLE imaging procedure, followed by EUS-guided FNA and aspiration of cyst fluid. Your cyst fluid would be analyzed, and surgery may be performed if indicated by your care team.
- Compensation
- Not stated in the trial record.
- Follow-up
- The accuracy of the diagnosis and risk-stratification will be measured at 48 months.
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Confocal Laser Endomicroscopy as an Imaging Biomarker for the Diagnosis of Pancreatic Cystic Lesions
At a glance
Conditions
Where it's being run
10 sites across 9 statesStudy leadership
- Somashekar Krishna, MD, MPH · PRINCIPAL_INVESTIGATOR · Ohio State University
Who to contact
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Do you actually qualify for this trial?
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Inclusion
Exclusion
What this trial measures
- Accuracy of PCL diagnosis48 months
Diagnostic accuracy of confocal laser endomicroscopy and/or cyst fluid molecular markers and/or composite clinical and imaging features for the diagnosis of mucinous PCLs, PCLs with malignant potential, specific PCL types, and mucinous PCLs with advanced neoplasia.
- Accuracy of risk-stratification of IPMNs48 months
Diagnostic accuracy of confocal laser endomicroscopy and/or composite clinical and imaging features for the risk stratification of IPMNs