AI Evaluation of Pancreatic Exocrine Insufficiency in CP Patients
Artificial Intelligence Assistance of Pancreatic Exocrine Insufficiency Evaluation in Chronic Pancreatitis From Contrast-Enhanced CT
Study Overview
Status
Detailed Description
Chronic pancreatitis (CP) is a chronic inflammatory disease characterized by abdominal pain, recurrent inflammation, and pancreatic fibrosis. The global incidence of CP is about 10/100,000 and shows an increasing trend year by year.
Pancreatic exocrine insufficiency (PEI) is a significant functional alteration in chronic pancreatitis. The prevalence of PEI in CP patients within 10 to 15 years after diagnosis is about 35% to 50%, and the prevalence increases significantly 15 years after diagnosis.Clinical manifestations of PEI vary among individuals and may include symptoms such as diarrhea, weight loss, abdominal pain, bloating, and steatorrhea. Some patients may remain asymptomatic, leading to deficiencies in fat-soluble vitamins and micronutrients due to abnormal digestion of macronutrients, a condition referred to as "subclinical PEI".Due to nutritional deficiencies, PEI patients are at an increased risk of complications related to malnutrition, osteoporosis, cardiovascular diseases, and elevated mortality rates. Importantly, the quality of life for PEI patients is significantly reduced, and timely diagnosis and treatment are crucial to prevent severe consequences. Early and appropriate use of pancreatic enzyme replacement therapy can enhance patient well-being and improve overall quality of life. Therefore, regular screening for PEI and prompt treatment are essential for CP patients to reduce the risk of complications and improve prognosis.It is recommended by current clinical guidelines that pancreatic exocrine function should be assessed at the time of CP diagnosis, and if no PEI is detected, annual screening is advised. Despite these recommendations, PEI is still frequently misdiagnosed and inadequately treated in clinical practice.
Pancreatic exocrine function testing involves both direct and indirect methods. Direct tests, such as the cholecystokinin-pancreozymin stimulation test, are considered the most sensitive and specific means of assessing pancreatic exocrine function. This test requires intravenous infusion or injection of cholecystokinin to stimulate pancreatic secretion, followed by duodenal content collection via a catheter for the measurement of pancreatic fluid secretion. However, due to its high cost, invasiveness, and significant discomfort for patients, this test is seldom used in clinical practice.Indirect tests encompass fecal tests, breath tests, urine tests, and blood tests. Among these, fecal elastase-1 (Fe-1) detection is a currently stable, accurate, and convenient indirect method. When Fe-1 levels range between 100-200 μg/g, it suggests mild to moderate PEI, while Fe-1 levels below 100 μg/g indicate severe PEI. Several studies have demonstrated that a Fe-1 level below 200 μg/g has a specificity greater than 90% for diagnosing PEI. However, the limited availability of the assay kit has restricted its widespread use.
In imaging examinations, the use of secretin-enhanced magnetic resonance cholangiopancreatography to measure pancreatic fluid flow after secretin stimulation is a non-invasive method for assessing PEI. However, its clinical feasibility is limited due to its time-consuming nature, high cost, complexity, and the lack of widespread availability of the required reagents. Computed Tomography (CT) is the most widely used imaging examination in the clinical assessment of CP, but there is no evidence supporting its utility in detecting PEI. Therefore, there is a need for a new, non-invasive, and convenient method for early detection of PEI.
In recent years, deep learning has shown significant promise in medical image analysis. Compared to traditional machine learning, deep learning utilizes models to analyze data and extract higher-level features. It then uses these features to derive results, enabling the modeling of complex relationships within the data. Commonly used methods include convolutional neural networks, generative adversarial models, and others. Currently, deep learning has been widely applied in pancreatic diseases, including tumor detection, differential diagnosis of pancreatic cancer, and predicting patient prognosis. However, there has been no research utilizing deep learning to assess PEI.This study aims to develop and validate a deep learning model based on fully automated pancreatic segmentation for evaluating pancreatic exocrine function. Additionally, The investigators will collaborate with radiologists to jointly assess the model's performance.
Study Type
Enrollment (Actual)
Contacts and Locations
Study Locations
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Shanghai, China, 200433
- Changhai Hospital
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Participation Criteria
Eligibility Criteria
Ages Eligible for Study
- Child
- Adult
- Older Adult
Accepts Healthy Volunteers
Sampling Method
Study Population
Description
Inclusion Criteria:
- gender and age are not restricted
- diagnosis of chronic pancreatitis
- fe-1 test was conducted following recent pancreatic enhanced CT
- sign informed consent
Exclusion Criteria:
- enhanced CT was taken during the acute episode of chronic pancreatitis
- history of malignant tumor
- previous pancreatic surgery
- systemic diseases
- poor image quality or image loss
Study Plan
How is the study designed?
Design Details
What is the study measuring?
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
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Classification of pancreatic exocrine insufficiency
Time Frame: Up to 10 months
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Pancreatic exocrine insufficiency are classified by FE-1 test into severe (0-100), mild to moderate (100-200) and normal (>200).
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Up to 10 months
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Collaborators and Investigators
Sponsor
Study record dates
Study Major Dates
Study Start (Actual)
Primary Completion (Actual)
Study Completion (Actual)
Study Registration Dates
First Submitted
First Submitted That Met QC Criteria
First Posted (Estimated)
Study Record Updates
Last Update Posted (Estimated)
Last Update Submitted That Met QC Criteria
Last Verified
More Information
Terms related to this study
Additional Relevant MeSH Terms
Other Study ID Numbers
- AIPEI
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
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