Autoimmune PancreatitisNCT07900776
AI-Assisted Differentiation of Autoimmune Pancreatitis and Pancreatic Cancer Using EUS: A Multicenter Ambispective Study
An observational study of Autoimmune Pancreatitis and Pancreatic Ductal Adenocarcinoma (PDAC), run by Ruijin Hospital.
Trial summary
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. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Autoimmune Pancreatitis, Pancreatic Ductal Adenocarcinoma (PDAC)
- How many people
- 690 participants
- Where
- No sites listed yet
- Study dates
- 1 Nov 2026to 30 Dec 2028
- How long will I take part?
- 6 Months
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
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.
Who may be able to join
- 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.
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- 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.
Who cannot join
- 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.
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- Pregnancy or breastfeeding.
- Inability to complete the required study-related examinations.iteria:
- -
Trial locations and contacts
No sites are listed yet. Contact the study team or a site near you to ask about taking part.
About this study
Official title: Multimodal EUS and Clinical Feature-Based Radiomics for AI-Assisted Differentiation of Mass-Forming Autoimmune Pancreatitis and Pancreatic Cancer: A Multicenter Ambispective Study
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.
- Medical subject headings
- Autoimmune Pancreatitis
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| 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 is being measured
The primary outcome is the main question the study is designed to answer.
Area Under the Receiver Operating Characteristic Curve (AUC) of the AI Model for Differentiating f-AIP From PDAC
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.
Measured: At completion of data collection and diagnostic assessment in the prospective validation cohort, through December 2028.
Secondary outcomes (7)
Sensitivity of the AI Model for Differentiating f-AIP From PDAC
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.
Measured: At completion of diagnostic assessment in the prospective validation cohort, through December 2028.
Specificity of the AI Model for Differentiating f-AIP From PDAC
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.
Measured: At completion of diagnostic assessment in the prospective validation cohort, through December 2028.
Diagnostic Accuracy of the AI Model
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.
Measured: At completion of diagnostic assessment in the prospective validation cohort, through December 2028.
Positive Predictive Value of the AI Model
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.
Measured: At completion of diagnostic assessment in the prospective validation cohort, through December 2028.
Negative Predictive Value of the AI Model
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.
Measured: At completion of diagnostic assessment in the prospective validation cohort, through December 2028.
Comparison of Diagnostic Performance Between the AI Model and Endoscopists
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.
Measured: At completion of diagnostic assessment in the prospective validation cohort, through December 2028.
Diagnostic Performance of the AI Model Across Participating Centers
The generalizability of the AI model will be evaluated across participating centers in the prospective validation cohort. Diagnostic performance will be assessed using center-specific AUC, sensitivity, specificity, and accuracy. Differences in model performance across centers will be explored.
Measured: At completion of diagnostic assessment in the prospective validation cohort, through December 2028.
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted28 Oct 2026
- Study start1 Nov 2026 (estimated)
- Primary completion30 Jun 2028 (estimated)
- Study completion30 Dec 2028 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Autoimmune PancreatitisStudies of the same condition, open and under way.
- More trials run by Ruijin HospitalThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Other
- Enrolment
- 690 participants (estimated)
- Target duration
- 6 Months
Sponsor and investigators
- Lead sponsor
- Ruijin Hospital
- Sponsor type
- University, hospital or other
- Collaborators
- The First People's Hospital of Changzhou, Zhongshan Hospital Xiamen University, The First People's Hospital of Yunnan, The Affiliated Hospital of Qingdao University, Second Affiliated Hospital of Suzhou University, Tianjin Medical University General Hospital, Shengjing Hospital, Zunyi Medical College, First Hospital of China Medical University, Songjiang Hospital Affiliated to Shanghai Jiaotong University School of Medicine, The First Affiliated Hospital of Nanchang University, Anhui Provincial Hospital
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07900776
- Sponsor's protocol ID
- 2026780
- Status last verified
- Oct 2026
- First submitted
- 22 Oct 2026
- Last updated
- 28 Oct 2026
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: Individual participant data will not be made publicly available due to patient privacy considerations, institutional data protection requirements, and restrictions on sharing multicenter clinical imaging and health records. De-identified data will be accessed only by authorized researchers within the participating institutions in accordance with applicable ethical approvals and data management agreements.
Study at a glance
From the registry record, last updated by the sponsor on 28 Oct 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Multimodal EUS and Clinical Feature-Based Radiomics for AI-Assisted Differentiation of Mass-Forming Autoimmune Pancreatitis and Pancreatic Cancer: A Multicenter Ambispective Study
- ClinicalTrials.gov ID
- NCT07900776
- Acronym / sponsor ID
- 2026780
- Study type
- Observational
- Recruitment status
- Not yet recruiting
- Enrolment
- 690 participants (estimated)
- Lead sponsor
- Ruijin HospitalUniversity, hospital or other
- Collaborators
- The First People's Hospital of Changzhou, Zhongshan Hospital Xiamen University, The First People's Hospital of Yunnan, The Affiliated Hospital of Qingdao University, Second Affiliated Hospital of Suzhou University, Tianjin Medical University General Hospital, Shengjing Hospital, Zunyi Medical College, First Hospital of China Medical University, Songjiang Hospital Affiliated to Shanghai Jiaotong University School of Medicine, The First Affiliated Hospital of Nanchang University, Anhui Provincial Hospital
- Study start
- 1 Nov 2026 (estimated)
- Primary completion
- 30 Jun 2028 (estimated)
- Study completion
- 30 Dec 2028 (estimated)
- First posted
- 28 Oct 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Observational model
- Cohort
- Time perspective
- Other
- Target follow-up
- 6 Months
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| 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. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- 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.
Key inclusion criteria (13 listed)
- 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.
Key exclusion criteria (9 listed)
- 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.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
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.
7 secondary outcomes
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.
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.
Diagnostic Accuracy of the AI Model
Time frame: At completion of diagnostic assessment in the prospective validation cohort, through December 2028.
Positive Predictive Value of the AI Model
Time frame: At completion of diagnostic assessment in the prospective validation cohort, through December 2028.
Negative Predictive Value of the AI Model
Time frame: At completion of diagnostic assessment in the prospective validation cohort, through December 2028.
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.
Diagnostic Performance of the AI Model Across Participating Centers
Time frame: At completion of diagnostic assessment in the prospective validation cohort, through December 2028.
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 28 Oct 2026.
- Individual participant dataSharing planned: No. Individual participant data will not be made publicly available due to patient privacy considerations, institutional data protection requirements, and restrictions on sharing multicenter clinical imaging and health records. De-identified data will be accessed only by authorized researchers within the participating institutions in accordance with applicable ethical approvals and data management agreements.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Questions about this trial
What is this clinical trial studying?
An observational study of Autoimmune Pancreatitis and Pancreatic Ductal Adenocarcinoma (PDAC), run by Ruijin Hospital.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 13 inclusion and 9 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: opening soon, as of the sponsor's last update on 28 Oct 2026. Status can change between updates, so confirm with the study contact before making plans.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Beibei Liu (+86-21-64370045, liubeibei915@gmail.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
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.). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 28 Oct 2026.