MC250406 Feasibility Study: Automated Risk Stratification, Serial AI-Augmented Imaging, and Biobanking for Early Detection of Sporadic Pancreatic Cancer (AI-PACED)
Summary
This clinical trial studies a new screening program to improve the early detection of sporadic pancreatic cancer in individuals with a high risk of developing pancreatic cancer. Pancreatic cancer remains one of the deadliest solid tumors, characterized by a long phase without symptoms followed by rapid progression once clinically evident. Despite advancements in treatment, the survival rate for pancreatic cancer remains low. Research has helped to identify a subset of individuals with a markedly high short-term risk for developing pancreatic cancer, which includes adults aged 50 and older with glycemically-defined new-onset diabetes and an Enriching New-Onset Diabetes for Pancreatic Cancer (ENDPAC) score ≥ 3. However, current practice guidelines do not provide clear pathways for surveillance or early detection. The screening program in this trial combines repeated contrast-enhanced computed tomography (CT) scans using artificial intelligence (AI) and blood draws. Contrast-enhanced CT is an imaging technique which creates a series of detailed pictures of areas inside the body; the pictures are created by a computer linked to an x-ray machine and a contrast agent is used to enhance the images. The images are then reviewed using AI, which may make it easier to spot cancer earlier on the CT scans than with the human eye. Studying samples of blood in the laboratory from high-risk individuals may help doctors understand more about why they may develop pancreatic cancer. This may be an effective way to screen high-risk individuals and improve the early detection of sporadic pancreatic cancer.
Arms & interventions
- ProcedureBiospecimen Collection
Undergo blood sample collection
- ProcedureComputed Tomography with Contrast
Undergo contrast-enhanced abdominal CT
- OtherElectronic Health Record Review
Undergo electronic medical record (EMR) surveillance
Outcome measures
Primary
Recruitment yield (Feasibility)
Will assess the feasibility of protocol implementation as defined by recruitment yield (% of flagged high-risk individuals who consent). Descriptive statistics will be used to summarize feasibility endpoints.
Time frame: Up to 3 years
Imaging adherence rates (Feasibility)
Will assess the feasibility of protocol implementation as defined by imaging adherence rates (% completing 3 scheduled computed tomography scans). Descriptive statistics will be used to summarize feasibility endpoints.
Time frame: Up to 3 years
Blood collection success rates (Feasibility)
Will assess the feasibility of protocol implementation as defined by blood collection success rates (% completing 3 scheduled blood collections). Descriptive statistics will be used to summarize feasibility endpoints.
Time frame: Up to 3 years
Completeness of electronic medical record (EMR)-based follow-up (Feasibility)
Will assess the feasibility of protocol implementation as defined by completeness of EMR-based follow-up (% of participants with outcome ascertainment). Descriptive statistics will be used to summarize feasibility endpoints.
Time frame: Up to 3 years
Secondary
Time from glycemically-defined new-onset diabetes (gNOD) onset to pancreatic ductal adenocarcinoma (PDA) diagnosis
Time frame: Up to 3 years
Proportion of PDAs diagnosed at stage 0/I
Time frame: Up to 3 years
Rate and type of incidental findings requiring downstream evaluation
Time frame: Up to 3 years
Artificial intelligence (AI)-detected imaging signatures and standard radiologist interpretations
Time frame: Up to 3 years
Eligibility criteria
Study locations (1)
Mayo Clinic in Rochester
Rochester, Minnesota, 55905