Artificial Intelligence-based Early Screening of Pancreatic Cancer and High Risk Tracing (ESPRIT-AI)

Study Overview
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Brief Summary
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Pancreatic cancer is one of the most fatal malignancies with a 5-year survival rate of only  6%[1]. The reasons for this high mortality rate can be attributed to several factors, of which perhaps the most important is delayed diagnosis due to vague symptoms and consequently missed opportunities for surgical resection. Therefore, the ability to detect pancreatic cancer at an early, more curable stage is urgently needed. Identifying risk factors and biomarkers of early pancreatic cancer could facilitate screening for individuals at higher than average risk and expedite the diagnosis in individuals with symptoms and substantially improve an individual's chance of surviving the disease. Thus, the investigators propose this longitudinal study entitled, Artificial Intelligence-based Early Screening of Pancreatic Cancer and High Risk Tracing (ESPRIT-AI) in order to generate clinical data sets and bank serial blood specimens of high risk individuals.

Detailed Description
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The study is being run by a team of dedicated physicians and researchers, led by Jin Gang, MD, Director of Department of general surgery of Shanghai Changhai Hospital. The trial will include individuals with new-onset diabetes (diagnosed within the past 3 year), familial pancreatic cancer, inherited syndromes associated with pancreatic cancer (including hereditary pancreatitis, familial atypical multiple mole and melanoma syndrome, hereditary nonpolyposis colon cancer, Peutz-Jeghers syndrome, hereditary breast and ovarian cancer syndromes, etc), pancreatic cystic neoplasm (including IPMN, MCN) as well as chronic pancreatitis. Participants will undergo annual laboratory tests and high-resolution MRI/CT examinations of the pancreas. Any suspicious lesions will be further examined by endoscopic ultrasound (EUS). If pancreatic cancer or a pre-cancerous lesion is identified, the individual will be referred for surgery. We will also be collecting a blood sample from all participants for DNA isolation. Clinical data and biological specimens contained in this study may be used for a wide variety of future related studies to the cause, diagnosis, outcome and treatment of pancreatic cancer.

Official Title
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Artificial Intelligence-based Health Information Management System and Key Technology Study of Early Screening and Hierarchical Diagnosis and Treatment of Pancreatic Cancer

Conditions
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Pancreatic Cancer, Diabetes, Familial Pancreatic Cancer, Pancreatic Cystic Neoplasm, Chronic Pancreatitis, Hereditary Pancreatitis

Intervention / Treatment
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* Diagnostic Test: high-resolution MRI/CT examinations


Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: Subject is able and willing to provide informed consent and sign an informed consent form. Subject or authorized representative must be willing to complete a detailed questionnaire. Subject must meet one of the following criteria: New onset diabetes (diagnosed within the past 3 years) Familial pancreatic cancer Inherited syndromes associated with pancreatic cancer (including Hereditary pancreatitis, Familial atypical multiple mole and melanoma syndrome, Hereditary nonpolyposis colon cancer, Peutz-Jeghers syndrome, Hereditary breast and ovarian cancer syndromes, etc) Pancreatic cystic neoplasm (including IPMN, MCN) Chronic pancreatitis Exclusion Criteria: Subject has been diagnosed with pancreatic cancer or other malignant tumors in the last 5 years; Subject has any medical condition that contraindicates high-resolution MRI or CT; Subject cannot be followed up or is participating in other clinical trials.

Ages Eligible for Study
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Minimum Age: 50 Years
Maximum Age: 75 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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No

Study Plan
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How is the study designed?
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Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| New Onset Diabetes<br>New Onset Diabetes must meet one of the following criteria: Documented diabetes diagnosed within the past 3 years. Definite new-onset diabetes based on recent fasting blood glucose (FBG) values ≥126 mg/dl (7.0 mmol/L) or Hemoglobin A1c (HbA1c) ≥ 6.5%. All glycemic parameters must be measured in an outpatient setting. | Diagnostic Test: high-resolution MRI/CT examinations<br>* Participants will undergo annual questionnaire survey, laboratory tests and high-resolution MRI/CT examinations of the pancreas. Any suspicious lesions will be further examined by endoscopic ultrasound (EUS).<br>* Other names: laboratory tests;|
| Familial pancreatic cancer<br>Familial pancreatic cancer must meet one of the following criteria: ≥ 2 blood relatives with pancreatic cancer (includes 1st-3rd degree relatives) One 1st degree relative with PDAC diagnosed before age 60 | Diagnostic Test: high-resolution MRI/CT examinations<br>* Participants will undergo annual questionnaire survey, laboratory tests and high-resolution MRI/CT examinations of the pancreas. Any suspicious lesions will be further examined by endoscopic ultrasound (EUS).<br>* Other names: laboratory tests;|
| Inherited syndromes associated with pancreatic cancer<br>Family history includes with inherited syndromes associated with pancreatic cancer ( ≥ 2 blood relative, includes 1st-3rd degree relatives). Inherited syndromes must meet one of the following criteria: Hereditary pancreatitis Familial atypical multiple mole and melanoma syndrome Hereditary nonpolyposis colon cancer Peutz-Jeghers syndrome Hereditary breast and ovarian cancer syndromes | Diagnostic Test: high-resolution MRI/CT examinations<br>* Participants will undergo annual questionnaire survey, laboratory tests and high-resolution MRI/CT examinations of the pancreas. Any suspicious lesions will be further examined by endoscopic ultrasound (EUS).<br>* Other names: laboratory tests;|
| Pancreatic Cystic Neoplasm<br>Pancreatic Cystic Neoplasm, including intraductal papillary mucinous neoplasms (IPMN) and mucinous cystic neoplasms (MCN), which are defined by endoscopic ultrasound or serial imaging. | Diagnostic Test: high-resolution MRI/CT examinations<br>* Participants will undergo annual questionnaire survey, laboratory tests and high-resolution MRI/CT examinations of the pancreas. Any suspicious lesions will be further examined by endoscopic ultrasound (EUS).<br>* Other names: laboratory tests;|
| Chronic pancreatitis<br>Chronic pancreatitis, defined by cross-sectional imaging, endoscopic ultrasound, functional testing abnormalities OR as diagnosed by a gastroenterologist. | Diagnostic Test: high-resolution MRI/CT examinations<br>* Participants will undergo annual questionnaire survey, laboratory tests and high-resolution MRI/CT examinations of the pancreas. Any suspicious lesions will be further examined by endoscopic ultrasound (EUS).<br>* Other names: laboratory tests;|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Incidence | Determine incidence of pancreatic cancer or precursor lesions among high risk individuals. | 5 years | 
| Hazard ratio (HR) | Assesses the influence of risk factors on the incidence of pancreatic cancer or precursor lesions among high risk individuals. | 5 years | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Survival time | Calculate survival time from point of diagnosis and treatment among the identified patients with pancreatic cancer. | 5 years | 
| HR | Assesses the influence of risk factors on survival time among the identified patients with pancreatic cancer. | 5 years | 
| Diagnostic yield | Determine diagnostic yield (sensitivity, specificity, positive/negative predictive value and accuracy) of AI-based surveillance program to predict early stage pancreatic cancer. | 5 years | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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Screening, Early Diagnosis, Artificial Intelligence, Pancreatic Cancer