Clinical Research Directory

Physical Activity and Nutrition to Halt Elevated Risk in the Pancreas Interception Center

The purpose of this Study is to assist in implementing a practical, easy-to-adopt lifestyle intervention that optimizes patient outcomes and minimizes pancreatic ductal adenocarcinoma (PDAC) risk.

Intra-operative Pancreatoscopy in Patients With IPMN

To demonstrate the added value of intraoperative pancreatoscopy in patients undergoing partial pancreatic resection for the treatment of Intraductal Papillary Mucinous Neoplasm (IPMN) as it pertains to detection of discontinuous (skip) lesions in the remnant pancreas; to generate a hypothesis for a subsequent randomized control trial.

Intent of Surgery for IPMN

This multicenter retrospective observational cohort study seeks to: 1. Classify surgical intent in patients with resected Intraductal Papillary Mucinous Neoplasms (IPMN) and quantify the proportion of IPMN-associated cancers diagnosed as overt pancreatic cancer with incidental IPMN association on pathology. 2. Compare clinicopathologic features and outcomes between surveillance-detected and incidentally detected IPMN-derived pancreatic cancers. 3. Revise and redefine risk features limited to patients undergoing surgery for IPMN-related indications, identifying optimal predictors of malignant IPMN (high-grade dysplasia or invasive cancer).

UCSF PANC Cyst Registry

Pancreatic cysts are found incidentally on 15-50% of CT and MRIs for all indications and their prevalence is increasing. Many of these cysts may be precursors to pancreatic cancer, and thus pose a substantial risk, however, the vast majority are benign. Increased detection of pancreatic cysts provides an opportunity to diagnose pancreatic malignancy at an early, curable stage yet also increases the potential to over-treat clinically insignificant lesions. This presents a clinical challenge to prevent unnecessary resection of indolent disease, with associated risks of infections, bleeding, diabetes, and costly disability. Unfortunately, there is little information on the epidemiology and natural history of pancreatic cysts to help guide management.

Artificial Intelligence-powered Low-Dose Computed Tomography for Screening of Pancreatic Cancer

Pancreatic ductal adenocarcinoma (PDAC) has a poor prognosis, with early diagnosis crucial for improving survival. Due to the absence of effective screening methods, most patients are diagnosed at advanced stages. The population undergoing low-dose computed tomography (LDCT) screening significantly overlaps with those at high risk for PDAC; however, traditional imaging methods have limited sensitivity for detecting pancreatic lesions. This study utilizes the Pancreatic Cancer Detection with Artificial Intelligence (PANDA) system to enhance LDCT for pancreatic cancer screening in a prospective, multicenter, observational cohort. PANDA will analyze LDCT images, followed by a multidisciplinary team (MDT) reassessment of abnormal interpretations. Based on MDT evaluation, individuals will be recalled for further examination, placed under a personalized follow-up plan, or monitored for at least one year. The primary outcomes include pancreatic cancer detection rate, positive predictive value, consensus rate, and recall rate, while secondary outcomes focus on early-stage cancers, resectable tumors, and safety indicators such as false positive rates and unnecessary procedures. This study aims to assess the effectiveness and safety of AI-assisted LDCT for PDAC detection, providing a practical solution for improving public health and enhancing early diagnostic capabilities.

AI-powered Early Detection for Pancreatic Cancer Via Non-contrast CT in Opportunistic Screening Cohort

Pancreatic ductal adenocarcinoma (PDAC) remains a therapeutic challenge with 5-year survival rates of 13%, primarily attributable to advanced-stage diagnosis (AJCC Stage III/IV in \>80% of cases). This prospective, observational, multi-center study will evaluate the performance of an AI-powered opportunistic screening system utilizing non-contrast computed tomography (NCCT) acquired during routine clinical encounters or health check-ups. The proposed AI model will perform automated detection of pancreatic parenchymal abnormalities, including PDAC and precursor lesions (intraductal papillary mucinous neoplasms \[IPMN\], mucinous cystic neoplasms \[MCN\]). Algorithm-positive cases will be independently reviewed by two radiologists. Highly suspected individuals will undergo further diagnostic verification, including serological tests and multimodal imaging confirmation. Patients with confirmed positive diagnosis will receive multidisciplinary consultation and specialized treatment, whereas those with negative results will undergo at least one-year clinical follow-up. This study will quantitatively evaluate the AI system's performance, and aims to advance PDAC early detection, improve patient outcomes, and make it accessible in underserved populations.

Exploring Biomarkers of the Carcinogenesis of Intraductal Papillary Mucinous Neoplasm (IPMN) of the Pancreas

This is a prospective, open large cohort study to explore biomarkers for detecting early carcinogenesis of IPMN.