Pancreatic Ductal Adenocarcinoma
Conditions
Brief 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.
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
Sponsors
Mayo Clinic
Study design
Allocation
NON_RANDOMIZED
Intervention model
PARALLEL
Primary purpose
DIAGNOSTIC
Masking
SINGLE (Outcomes Assessor)
Masking description
All CT scans obtained under the study will be interpreted by qualified radiologists who are not part of the study team and are blinded to study objectives.
Eligibility
Sex/Gender
ALL
Age
50 Years to 85 Years
Healthy volunteers
No
Inclusion criteria
* Age ≥ 50 and ≤ 85 years * Glycemically-defined new-onset diabetes (gNOD) with onset ≤ 180 days preceding enrollment * Enriching New-onset Diabetes for Pancreatic Cancer (ENDPAC) score ≥ 3, based on validated risk stratification models * Provide written or remote informed consent
Exclusion criteria
* Prior diagnosis of pancreatic ductal adenocarcinoma (PDA) * Known hereditary cancer syndromes (e.g., BRCA1/2, Lynch syndrome, Peutz-Jeghers) * Prior history of pancreatic surgery * Pancreatic cyst surveillance at time of registration * Contraindications to contrast-enhanced CT imaging per standard clinical practice at time of registration
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Recruitment yield (Feasibility) | Up to 3 years | 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. |
| Imaging adherence rates (Feasibility) | Up to 3 years | 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. |
| Blood collection success rates (Feasibility) | Up to 3 years | 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. |
| Completeness of electronic medical record (EMR)-based follow-up (Feasibility) | Up to 3 years | 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. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Time from glycemically-defined new-onset diabetes (gNOD) onset to pancreatic ductal adenocarcinoma (PDA) diagnosis | Up to 3 years | Time to PDA diagnosis will be compared between cohorts. |
| Proportion of PDAs diagnosed at stage 0/I | Up to 3 years | Comparisons between cohorts will employ log-rank tests. Will also employ Cox proportional hazards models adjusted for baseline covariates (exploratory only). |
| Rate and type of incidental findings requiring downstream evaluation | Up to 3 years | Comparisons between cohorts will employ log-rank tests. Will also employ Cox proportional hazards models adjusted for baseline covariates (exploratory only). |
| Artificial intelligence (AI)-detected imaging signatures and standard radiologist interpretations | Up to 3 years | Will complete discordance analysis between AI-detected imaging signatures and standard radiologist interpretations, including rates of earlier detection and false positives. |
Countries
United States
Contacts
CONTACTClinical Trials Referral Office
CONTACTAlyssa Johnson
PRINCIPAL_INVESTIGATORAjit H. Goenka, MD
Mayo Clinic in Rochester
Outcome results
None listed