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A Prospective Registry for Patients at High-Risk for Pancreatic Cancer (HRPCR)
Rochester, Minn.
This study aims to facilitate discovery and validation of tests for early detection in subjects at high risk for pancreatic ductal adenocarcinoma (PDAC). and to facilitate the use of state-of-the-art machine learning-based algorithms that utilize databases and images with the purpose of identifying early stages of pancreatic cancer, as well as people at high-risk.The study also aims to provide a platform for development of an interventional protocol for early detection of PDAC.
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Molecular Detection of Advanced Neoplasia in Pancreatic Cysts (IN-CYST)
Rochester, Minn.
The goal of this protocol is to collect biospecimens in order to prospectively evaluate diagnostic and prognostic performance of MDMs for detection of advanced neoplasia in pancreatic cysts.
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Pancreatic Cancer Detection Consortium (PCDC) Prospective Cohorts
Rochester, Minn.
The purpose of this study is to develop a cohort (biobank of biospecimens and data) of subjects without pancreatic cancer who are at high-risk for pancreatic cancer due to: a strong family history, a mutation in a known pancreatic cancer predisposition gene, or Fukuoka worrisome or high-risk pancreatic cysts. Additionally to follow the cohort subjects longitudinally and collect biospecimens and follow-up data and record medical outcomes and to make biospecimen available to PCDC-approved projects to validate potential biomarkers for performance using nested case-control prospective designs.
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PancREatic Cancer DetectIon Using Circulating Tumor Markers (PREDICT): A Prospective Case-control Study (PREDICT)
Rochester, Minn.
The primary purpose of this study is to select and validate candidate methylated DNA markers (MDMs) (individually and in combined panels) for the detection of pancreative ductal adenocarcinoma (PDAC). The potential complementary value of mutant KRAS and CA19-9 will also be evaluated.
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Predicting High-Grade Lesions in Surgically Resected Pancreatic Cysts using Biomarkers Assayed in Tissue, Cyst Fluid and Pancreatic Juice
Rochester, Minn.
The purpose of this study is to evaluate molecular markers which might be a predictor of pancreatic cancer or precancer by analyzing the secretions (fluid) from a pancreatic cyst, pancreas fluid and tissue from a resected pancreatic cyst.
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Closed for Enrollment
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Biospecimen Resource for Pancreas Research (Substudy # 3 - Controls)
Rochester, Minn.
The purpose of this study is to develop a panel of primary care controls to be used to compare the pancreatic disease patients who are recruited into the ongoing IRB protocol 354-06.
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Cohort Study of Pancreatic Cancer Risk
Rochester, Minn.
The primary purpose of this study is to develop a cohort (biobank of biospecimens and data) of individuals aged 50 and older without pancreatic cancer who are members of kindreds containing three blood relatives with pancreatic cancer, OR who carry a mutation in a known predisposition gene for pancreatic cancer.
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Elotuzumab in IgG4-Related Disease (IgG4)
Rochester, Minn.
This is a two-part multi-center clinical trial in participants with active IgG4-RD. Part 1 is an open-label, dose escalation phase to determine the safety of elotuzumab for investigation in IgG4-RD. If the addition of elotuzumab to prednisone is determined to be safe, Part 2: a randomized, placebo-controlled, double-blinded trial will be initiated. Part 2 will compare the effects of the addition of elotuzumab versus placebo to prednisone in participants with IgG4-RD. Approximately 75 participants with active IgG4-RD will be enrolled in the overall program, 12 in Part 1 and 63 in Part 2.
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Optimizing Pancreatic Juice Collection and Processing for Molecular Marker Assays
Rochester, Minn.
To optimize pancreatic juice collection methods for use in molecular studies.
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Pancreatic Cancer Risk Factor Identification in Electronic Medical Records Using Natural Language Processing (NLP) Algorithms: A Clinical Pilot Study (The PRIME Study) (PRIME)
Rochester, Minn.
The objective of this study is to measure real-world recall and precision of Natural Language Processing (NLP) algorithms in identifying the presence of known risk factors for Pancreatic (PDAC) in a defined primary care population using unstructured clinical notes.
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