Pancreatic Cancer Detection Consortium (PCDC)

The NIH National Cancer Institute’s Pancreatic Cancer Detection Consortium (PCDC) develops and tests new molecular and imaging  biomarkers to diagnose pancreatic cancer in its precursor or early stages. These detection methods are used to identify high-risk individuals eligible for early intervention. 

Click this link to read more about PCDC.

The OHSU PCDC Research Unit

Our research strategy is to: (1) Define barriers to pancreatic cancer surveillance for underrepresented and underserved communities, (2) Study novel blood and imaging tests for early detection of pancreatic cancer, and (3) Measure changes of blood and imaging biomarkers for pancreatic cancer in high-risk patients over time.

There are substantial disparities in access to and participation in cancer screening across medically underserved populations such as rural, Black, Indigenous, Hispanic and Latino communities. Understanding and addressing these barriers is imperative to address disparities and effectively implement pancreatic cancer screening for those at-risk. Working with OHSU’s Community Outreach, Research and Engagement (CORE) team and community advisory board, we will develop partnerships with communities to characterize their specific views of and barriers to cancer screening, participation in research, and engagement with the health care system.

The Brenden-Colson Center for Pancreatic Care is collaborating with Biological Dynamics, Inc. to test the sensitivity and specificity of their ExoVita™ Pancreas assay on blood samples from patients diagnosed at early stages of pancreatic cancer and those with precancerous lesions. The ExoVita Pancreas assay is a promising diagnostic test, measuring specific extracellular vesicle protein biomarkers in blood samples, detecting stage 1 and 2 pancreatic cancer with high sensitivity.

Magnetic resonance fingerprinting (MRF) is an imaging technique that avoids radiation and intravenous contrast, is fast, and quantifies fibrosis and inflammation – key characteristics of pancreatic cancer and its precancerous lesions. We are performing a study to determine the effectiveness of MRF at identifying pancreatic cancer and diseases in at-risk patients and healthy volunteers.

We will be conducting a longitudinal biomarker study enrolling high-risk patients to detect measurable changes in the novel blood- or imaging-based screening test described above. With this study, we will also refine the optimal surveillance frequency for various high-risk populations.


The Brenden-Colson Center Research Unit is uniquely positioned to collaborate with the PCDC Consortium by sharing existing resources from the Oregon Pancreas Tissue Registry, the OHSU High Risk Clinic, and through OHSU’s involvement in the PRECEDE consortium.

In addition, our team includes experts who have designed the Healthy Oregon Project, an app-based platform for genetic data collection from mail-in kits, as well as population-based interactions to find and interact with high-risk individuals. This work specifically involves community outreach to understand barriers to screening for underrepresented and underserved communities.

Meet the Team

The Brenden-Colson Center for Pancreatic Care PCDC research unit is a multidisciplinary team with substantial experience in cancer biology, early detection, community outreach, and other critical subject areas required for the successful implementation of the research strategy.

Program Leaders: Rosalie C. Sears, PhD, Gregory A. Coté, MD, MS, Alexander S. Guimaraes, MD, PhD, Brett C. Sheppard, MD, Dove Keith, PhD

Cancer Biology: Rosalie C. Sears, PhD

Biomedical Engineering: Stuart Ibsen, PhD

Advanced Imaging: Alexander S. Guimaraes, MD, PhDCory Wyatt, PhD

High-risk Surveillance: Brett C. Sheppard, MD, Carmen Curry, MS, FNP-BC

Clinical Trials and Pancreatology: Gregory A. Coté, MD, MS, Brett C. Sheppard, MD, Alexander S. Guimaraes, MD, PhD

Population-based Outreach and Cancer Screening: Jackilen Shannon, PhD, Gregory A. Coté, MD, MS

Biostatistics: Jeong Youn Lim, PhD

Machine Learning and Modeling: Xubo Song, PhD