AI-Assisted Differentiation of Autoimmune Pancreatitis and Pancreatic Cancer Using Endoscopic Ultrasound
Autoimmune Pancreatitis · Pancreatic Ductal Adenocarcinoma (PDAC)
What this study involves
The registry does not currently label this study as recruiting. Ask about its status before planning a screening visit.
- Study type
- Observational
- Phase
- Not applicable / not listed
- Age range
- 18 years - Not specified
- Sex eligible
- All sexes
- Healthy volunteers
- Not accepted
- Participants
- 690 Estimated
These are starting points, not an eligibility decision. The research team must review the full requirements.
About the research
Focal autoimmune pancreatitis (f-AIP) and pancreatic ductal adenocarcinoma (PDAC) exhibit substantial overlap in clinical manifestations and imaging features, posing a major diagnostic challenge in pancreatic diseases. Given the marked differences in treatment strategies and prognosis, accurate differentiation is essential to avoid unnecessary surgery and ensure timely initiation of anticancer treatment.
This study aims to collect endoscopic ultrasound (EUS) images from patients with confirmed f-AIP or PDAC across multiple centers, using a previously established multicenter database. Data collected before January 2026 will be used to develop an artificial intelligence (AI) model based on EUS radiomics for differential diagnosis. Subsequently, prospectively collected data from participating centers from 2026 onward will be used for model testing and validation. The diagnostic performance of the model will be evaluated and compared with that of endoscopists with different levels of experience.
Furthermore, this study will investigate the clinical value of interpretable radiomic features, with particular emphasis on challenging diagnostic scenarios, including IgG4-negative AIP, AIP with elevated carbohydrate antigen 19-9 (CA19-9) levels, and PDAC with imaging features resembling those of AIP. The ultimate goal is to provide a reliable tool for accurate, noninvasive preoperative differential diagnosis while promoting the standardization of multicenter AIP databases and the sharing and utilization of imaging data resources.
Who may be able to participate?
The list below is the sponsor’s full registry wording. It can include conditions, prior treatment, test results and other requirements. A match in search does not mean you qualify.
Inclusion C\*\*Inclusion Criteria:\*\*
\*\*1. Focal Autoimmune Pancreatitis (f-AIP) Group\*\*
- Diagnosis of type 1 or type 2 autoimmune pancreatitis (AIP) according to the 2023 Chinese guidelines for the diagnosis and treatment of AIP or the 2011 International Consensus Diagnostic Criteria.
- Imaging evidence of a focal pancreatic mass confined to a specific pancreatic segment.
- Preoperative endoscopic ultrasound (EUS) examination with at least five clear images of the lesion obtained from different imaging planes.
- Histopathological confirmation of AIP based on EUS-guided fine-needle aspiration/biopsy (EUS-FNA/FNB) or surgical pathology.
- Complete clinical data, including demographic characteristics, serological findings, imaging results, treatment information, and follow-up records.
- Written informed consent from the patient or legal representative, as applicable.
\*\*2. Pancreatic Ductal Adenocarcinoma (PDAC) Group\*\*
- Histological or cytological confirmation of pancreatic ductal adenocarcinoma (PDAC) by EUS-FNA/FNB or surgical pathology.
- Preoperative EUS examination with at least five clear images of the lesion obtained from different imaging planes.
- A pancreatic parenchymal lesion comparable to f-AIP, with a maximum diameter of 5 cm or less.
- Complete clinical data, including demographic characteristics, serological findings, imaging results, treatment information, and follow-up records.
- Written informed consent from the patient or legal representative, as applicable.
\*\*Exclusion Criteria:\*\*
The following criteria apply to both groups:
- Poor-quality EUS images, including severe blurring, artifacts, or inadequate lesion visualization, preventing radiomic analysis.
- Uncertain or inconclusive pathological diagnosis.
- Concurrent pancreatic diseases, such as acute pancreatitis, non-AIP chronic pancreatitis, pancreatic cystic neoplasms, or pancreatic neuroendocrine tumors.
- Previous treatment for pancreatic disease, including corticosteroid therapy, chemotherapy, radiotherapy, or surgery, before EUS examination.
- Missing essential clinical or imaging data.
- Pregnancy or breastfeeding.
- Inability to complete the required study-related examinations.iteria:
Exclusion Criteria:
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Study population
This multicenter ambispective observational study will include patients with confirmed focal autoimmune pancreatitis (f-AIP) or pancreatic ductal adenocarcinoma (PDAC) who undergo endoscopic ultrasound (EUS) examination at participating hospitals in China. The retrospective cohort will include at least 450 patients (150 with f-AIP and 300 with PDAC) for AI model development and internal validation. An independent prospective cohort will include at least 240 patients (80 with f-AIP and 160 with PDAC) for external validation. EUS images, clinical characteristics, laboratory findings, and pathological data will be collected. All examinations and treatments will follow routine clinical practice, without study-specific interventions.
Understand screeningTreatment, groups and participation
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- Assignment
- Not reported
- Masking
- Not reported
Focal Autoimmune Pancreatitis (f-AIP) Group
Patients diagnosed with focal autoimmune pancreatitis (f-AIP) according to established diagnostic criteria. Eligible patients will undergo endoscopic ultrasound (EUS) as part of routine clinical care. EUS images, clinical characteristics, laboratory findings, and relevant pathological data will be collected for the development and validation of an artificial intelligence-based differential diagnostic model. Both retrospective and prospective cases will be included. No study-specific interventions will be performed.
Pancreatic Ductal Adenocarcinoma (PDAC) Group
Patients with histologically or cytologically confirmed pancreatic ductal adenocarcinoma (PDAC) who have undergone endoscopic ultrasound (EUS) as part of routine clinical care. EUS images, clinical characteristics, laboratory findings, and relevant pathological data will be collected for comparison with patients with f-AIP and for the development and validation of an artificial intelligence-based differential diagnostic model. Both retrospective and prospective cases will be included. No study-specific interventions will be performed.
What researchers are measuring
Outcome measures describe what is being studied. They are not proof that a treatment works.
Primary measures
Area Under the Receiver Operating Characteristic Curve (AUC) of the AI Model for Differentiating f-AIP From PDAC
Time frame: At completion of data collection and diagnostic assessment in the prospective validation cohort, through December 2028.
The diagnostic performance of the multimodal artificial intelligence (AI) model in differentiating focal autoimmune pancreatitis (f-AIP) from pancreatic ductal adenocarcinoma (PDAC) will be evaluated using the area under the receiver operating characteristic curve (AUC) in an independent prospective multicenter validation cohort. The AUC ranges from 0.5 (no discrimination) to 1.0 (perfect discrimination), with higher values indicating better diagnostic performance. The AUC and its 95% confidence interval will be calculated.
Secondary measures
Sensitivity of the AI Model for Differentiating f-AIP From PDAC
Time frame: At completion of diagnostic assessment in the prospective validation cohort, through December 2028.
Sensitivity will be calculated as the proportion of patients with focal autoimmune pancreatitis (f-AIP) correctly identified by the AI model among all patients with confirmed f-AIP in the prospective validation cohort. Results will be reported as percentages with 95% confidence intervals.
Specificity of the AI Model for Differentiating f-AIP From PDAC
Time frame: At completion of diagnostic assessment in the prospective validation cohort, through December 2028.
Specificity will be calculated as the proportion of patients with pancreatic ductal adenocarcinoma (PDAC) correctly identified as not having f-AIP among all patients with confirmed PDAC in the prospective validation cohort. Results will be reported as percentages with 95% confidence intervals.
Diagnostic Accuracy of the AI Model
Time frame: At completion of diagnostic assessment in the prospective validation cohort, through December 2028.
Diagnostic accuracy will be defined as the proportion of correctly classified f-AIP and PDAC cases among all evaluated patients in the prospective validation cohort. Results will be reported as percentages with 95% confidence intervals.
Positive Predictive Value of the AI Model
Time frame: At completion of diagnostic assessment in the prospective validation cohort, through December 2028.
Positive predictive value (PPV) will be calculated as the proportion of patients with confirmed f-AIP among all patients classified as f-AIP by the AI model in the prospective validation cohort. Results will be reported as percentages with 95% confidence intervals.
Negative Predictive Value of the AI Model
Time frame: At completion of diagnostic assessment in the prospective validation cohort, through December 2028.
Negative predictive value (NPV) will be calculated as the proportion of patients with confirmed PDAC among all patients classified as PDAC by the AI model in the prospective validation cohort. Results will be reported as percentages with 95% confidence intervals.
Comparison of Diagnostic Performance Between the AI Model and Endoscopists
Time frame: At completion of diagnostic assessment in the prospective validation cohort, through December 2028.
The diagnostic performance of the AI model will be compared with that of endoscopists at different experience levels (junior: less than 5 years; intermediate: 5-10 years; senior: 10 years or more). Diagnostic performance will be assessed using AUC, sensitivity, specificity, and accuracy in the prospective validation cohort. Differences in AUC will be evaluated using the DeLong test where applicable.
Comparison of Diagnostic Sensitivity Between the AI Model and Endoscopists
Time frame: At completion of diagnostic assessment in the prospective validation cohort, through December 2028.
The sensitivity of the AI model for differentiating mass-forming autoimmune pancreatitis from pancreatic ductal adenocarcinoma will be compared with that of endoscopists at different experience levels in the prospective validation cohort. Sensitivity will be calculated as the proportion of patients with pancreatic ductal adenocarcinoma correctly identified as having pancreatic ductal adenocarcinoma.
Comparison of Diagnostic Specificity Between the AI Model and Endoscopists
Time frame: At completion of diagnostic assessment in the prospective validation cohort, through December 2028.
The specificity of the AI model for differentiating mass-forming autoimmune pancreatitis from pancreatic ductal adenocarcinoma will be compared with that of endoscopists at different experience levels in the prospective validation cohort. Specificity will be calculated as the proportion of patients with mass-forming autoimmune pancreatitis correctly identified as having mass-forming autoimmune pancreatitis.
Comparison of Diagnostic Accuracy Between the AI Model and Endoscopists
Time frame: At completion of diagnostic assessment in the prospective validation cohort, through December 2028.
The diagnostic accuracy of the AI model will be compared with that of endoscopists at different experience levels in the prospective validation cohort. Accuracy will be calculated as the proportion of correctly classified patients among all evaluated patients.
Review the complete result tables and limitations in the official record.
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Study team and timeline
- Lead sponsor
- Ruijin Hospital
- Study start
- Nov 1, 2026 Estimated
- Primary completion
- Jun 30, 2028 Estimated
- Study completion
- Dec 30, 2028 Estimated
- First posted
- Oct 28, 2026 Actual
- Last updated
- Nov 9, 2026 Actual
Official study contacts
Official study title
Multimodal EUS and Clinical Feature-Based Radiomics for AI-Assisted Differentiation of Mass-Forming Autoimmune Pancreatitis and Pancreatic Cancer: A Multicenter Ambispective Study
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Source: ClinicalTrials.gov API v2. Information is submitted by sponsors and investigators. NLM does not approve the safety or science of all listed studies. Registry disclaimer.
Retrieved: Nov 12, 2026 · Last registry update: Nov 9, 2026
