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| ID | Type | Description | Link |
|---|---|---|---|
| NCI-2025-09279 | Registry Identifier | CTRP (Clinical Trial Reporting Program) | |
| 25-009433 | Other Identifier | Mayo Clinic Institutional Review Board | |
| MC250406 | Other Identifier | Mayo Clinic |
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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.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Group A1 (CT, blood, EMR surveillance) | Experimental | Patients undergo contrast-enhanced abdominal CT and blood sample collection at baseline, 6 months, and 12 months in the absence of unacceptable toxicity. Patients also undergo EMR surveillance for PDA diagnosis for up to 36 months. |
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| Group A2 (blood, EMR surveillance) | Experimental | Patients undergo blood sample collection at baseline, 6 months, and 12 months in the absence of unacceptable toxicity. Patients also undergo EMR surveillance for PDA diagnosis for up to 36 months. |
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| Group B (EMR surveillance) | Active Comparator | Patients undergo EMR surveillance for PDA diagnosis for up to 36 months. |
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| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Biospecimen Collection | Procedure | Undergo blood sample collection |
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| Measure | Description | Time Frame |
|---|---|---|
| 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. | 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. | 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. | 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. | Up to 3 years |
| Measure | Description | Time Frame |
|---|---|---|
| Time from glycemically-defined new-onset diabetes (gNOD) onset to pancreatic ductal adenocarcinoma (PDA) diagnosis | Time to PDA diagnosis will be compared between cohorts. | Up to 3 years |
| Proportion of PDAs diagnosed at stage 0/I |
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Inclusion Criteria:
Exclusion Criteria:
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| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Clinical Trials Referral Office | Contact | 855-776-0015 | mayocliniccancerstudies@mayo.edu | |
| Alyssa Johnson | Contact | 507-422-9721 |
| Name | Affiliation | Role |
|---|---|---|
| Ajit H. Goenka, MD | Mayo Clinic in Rochester | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Mayo Clinic in Rochester | Recruiting | Rochester | Minnesota | 55905 | United States |
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| Label | URL |
|---|---|
| Mayo Clinic Clinical Trials | View source |
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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.
| Computed Tomography with Contrast | Procedure | Undergo contrast-enhanced abdominal CT |
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| Electronic Health Record Review | Other | Undergo electronic medical record (EMR) surveillance |
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Comparisons between cohorts will employ log-rank tests. Will also employ Cox proportional hazards models adjusted for baseline covariates (exploratory only).
| Up to 3 years |
| Rate and type of incidental findings requiring downstream evaluation | Comparisons between cohorts will employ log-rank tests. Will also employ Cox proportional hazards models adjusted for baseline covariates (exploratory only). | Up to 3 years |
| Artificial intelligence (AI)-detected imaging signatures and standard radiologist interpretations | Will complete discordance analysis between AI-detected imaging signatures and standard radiologist interpretations, including rates of earlier detection and false positives. | Up to 3 years |
| ID | Term |
|---|---|
| D013048 | Specimen Handling |
| D003287 | Contrast Media |
| ID | Term |
|---|---|
| D019411 | Clinical Laboratory Techniques |
| D019937 | Diagnostic Techniques and Procedures |
| D003933 | Diagnosis |
| D008919 | Investigative Techniques |
| D064907 | Diagnostic Uses of Chemicals |
| D020228 | Pharmacologic Actions |
| D020164 | Chemical Actions and Uses |
| D020313 | Specialty Uses of Chemicals |
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