Pancreatic Mucinous Cystic NeoplasmNCT07919704
AI-Assisted Differentiation of Pancreatic Cystic Lesions Using EUS Images and Clinical Features
An observational study of Pancreatic Mucinous Cystic Neoplasm and Pancreatic Serous Cystic Neoplasm, run by Ruijin Hospital at 1 site in China.
Trial summary
Pancreatic mucinous cystic neoplasms (MCNs), serous cystic neoplasms (SCNs), and pancreatic pseudocysts (PPCs) often exhibit overlapping imaging features on endoscopic ultrasound (EUS). However, these conditions require different clinical management strategies. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Pancreatic Mucinous Cystic Neoplasm, Pancreatic Serous Cystic Neoplasm, Pancreatic Pseudocyst
- How many people
- 400 participants
- Where
- 1 siteChina
- Study dates
- 1 Nov 2026to 30 Dec 2028
- 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: The study population will consist of approximately 400 adult patients aged 18 years or older with a final diagnosis of pancreatic mucinous cystic neoplasm (MCN), serous cystic neoplasm (SCN), or pancreatic pseudocyst (PPC) who have undergone endoscopic ultrasound (EUS) at Ruijin Hospital, Shanghai Jiao Tong University School of Medicine. Approximately 310 retrospective patients will be identified from existing clinical records for AI model development and internal validation. An additional 80-100 eligible patients will be consecutively enrolled for prospective temporal validation after model finalization. MCN and SCN diagnoses will be confirmed by pathology, whereas PPC diagnoses will be established using a predefined composite reference standard. Each patient will contribute one independent study sample regardless of the number of available EUS images.
Who may be able to join
- Inclusion Criteria:
- Adults aged 18 years or older, regardless of sex.
- Patients who have undergone endoscopic ultrasound (EUS) examination.
- A final diagnosis of pancreatic mucinous cystic neoplasm (MCN), serous cystic neoplasm (SCN), or pancreatic pseudocyst (PPC).
- For MCN and SCN, a confirmed diagnosis based on surgical or histopathological findings.
- For PPC, a diagnosis established according to a predefined composite reference standard incorporating clinical history, imaging findings, and relevant diagnostic evidence.
Show all 10Show fewer
- Availability of at least one EUS image clearly showing the target lesion.
- EUS examination performed before surgery, ablation, drainage, or other treatment of the target lesion.
- Availability of essential clinical information corresponding to the EUS examination.
- For the prospective validation cohort, enrollment after model finalization and provision of written informed consent according to the approved ethics protocol.
Who cannot join
- Severely degraded EUS image quality preventing reliable identification of the target lesion.
- Availability of only post-treatment or postoperative EUS images, without pretreatment imaging.
- Lack of reliable pathological confirmation for MCN or SCN.
- Inability to establish a clear correspondence between the EUS images and the pathologically confirmed lesion.
- Uncertain or disputed final diagnosis that remains unresolved after expert review.
- Concurrent pancreatic cystic neoplasms for which the target lesion cannot be clearly identified.
Show all 10Show fewer
- Other pancreatic lesions, including intraductal papillary mucinous neoplasms (IPMNs), solid pseudopapillary neoplasms (SPNs), cystic neuroendocrine tumors, or cystic degeneration of solid tumors.
- Substantial missing clinical information preventing multimodal analysis.
- Refusal to provide or withdrawal of informed consent during the prospective phase.
- -
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
China1 site
Ruijin Hospital, Shanghai Jiao Tong University School of Medicine
Shanghai, Shanghai Municipality, 200025
About this study
Official title: Artificial Intelligence-Assisted Differentiation of Pancreatic Mucinous Cystic Neoplasms, Serous Cystic Neoplasms, and Pseudocysts Using Endoscopic Ultrasound Images and Clinical Features: An Ambispective Observational Study
Pancreatic mucinous cystic neoplasms (MCNs), serous cystic neoplasms (SCNs), and pancreatic pseudocysts (PPCs) often exhibit overlapping imaging features on endoscopic ultrasound (EUS). However, these conditions require different clinical management strategies. Accurate differentiation is therefore important for appropriate treatment planning.
This study aims to develop and validate an artificial intelligence (AI) model that integrates EUS images and clinical features to differentiate MCNs, SCNs, and PPCs. The study is designed as an ambispective observational diagnostic study consisting of retrospective model development, prospective temporal validation, and a reader study involving EUS physicians.
Read more
Approximately 400 adult patients with confirmed MCN, SCN, or PPC will be included. Retrospective clinical data and EUS images will be used for model development and internal validation. After model development is completed, newly diagnosed patients will be prospectively enrolled to independently evaluate the diagnostic performance of the model.
The primary outcome is the patient-level macro-averaged area under the receiver operating characteristic curve (macro-AUROC) for three-class classification in the prospective validation cohort. Secondary outcomes include diagnostic accuracy, sensitivity, specificity, calibration, clinical net benefit, and the performance of EUS physicians with and without AI assistance.
This study is expected to provide evidence for the potential clinical value of AI-assisted differentiation of pancreatic cystic lesions. No additional invasive procedures or study-specific treatments will be performed.
Read the detailed description
This study is an ambispective observational diagnostic study designed to develop and independently validate a multimodal artificial intelligence (AI) model for differentiating pancreatic mucinous cystic neoplasms (MCNs), serous cystic neoplasms (SCNs), and pancreatic pseudocysts (PPCs) using endoscopic ultrasound (EUS) images and clinical features.
Study Design
Approximately 400 adult patients with a final diagnosis of MCN, SCN, or PPC will be included. The study consists of two stages: retrospective model development and prospective temporal validation. A reader study will also be conducted to evaluate the potential benefits of AI assistance for EUS physicians.
Stage 1: Retrospective Model Development
Approximately 310 patients will be retrospectively identified from existing clinical databases. EUS images, clinical characteristics available before EUS examination, and final diagnostic information will be collected.
MCN and SCN diagnoses will be established using surgical or histopathological findings. PPC will be diagnosed according to a predefined composite reference standard based on clinical history, imaging findings, and other relevant diagnostic evidence.
Separate models will be developed using EUS images alone, clinical features alone, and the combination of EUS images and clinical features. Multiple EUS images from the same patient will be aggregated to generate a single patient-level prediction. Patient-level stratified cross-validation will be used for internal validation, with measures implemented to prevent data leakage.
Stage 2: Prospective Temporal Validation
After the AI model and analysis procedures have been finalized, approximately 80-100 consecutive eligible patients will be prospectively enrolled for independent temporal validation. These patients will not be used for model training, feature selection, or parameter optimization.
The primary outcome will be the patient-level macro-averaged area under the receiver operating characteristic curve (macro-AUROC) for three-class classification of MCN, SCN, and PPC. Each disease category will be evaluated using a one-vs-rest approach, and the three AUROC values will be averaged. A 95% confidence interval will be estimated using bootstrap resampling.
Secondary evaluations will include overall accuracy, macro-F1 score, weighted F1 score, class-specific sensitivity, specificity, positive predictive value, negative predictive value, model calibration, and clinical net benefit.
Reader Study
EUS physicians with different levels of experience will independently evaluate selected cases. Diagnostic performance will be compared among physicians without AI assistance, the AI model alone, and physicians with AI assistance. Diagnostic confidence, interobserver agreement, and interpretation time will also be assessed.
Model Interpretability
Gradient-weighted class activation mapping (Grad-CAM) or attention-based visualization will be used to identify image regions contributing to model predictions. Shapley additive explanations (SHAP) will be used to evaluate the contributions of clinical variables.
Exploratory analyses will investigate model performance across predefined patient subgroups, the potential incremental value of EUS videos, and differences among pathological subtypes of MCN.
Clinical Procedures and Patient Safety
All EUS examinations, pathological assessments, and clinical treatments will be performed according to routine clinical practice. No additional invasive procedures or study-specific therapeutic interventions will be required. AI predictions during prospective validation will not be used to guide clinical decisions.
- Conditions
- Pancreatic Mucinous Cystic Neoplasm, Pancreatic Serous Cystic Neoplasm, Pancreatic Pseudocyst
- Keywords
- Endoscopic ultrasound, Artificial intelligence, Deep learning, Pancreatic cystic lesions, Multimodal learning, Prospective validation
- Medical subject headings
- Pancreatic Pseudocyst
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 |
|---|---|---|
| Mucinous Cystic Neoplasm (MCN) | Adult patients with pancreatic mucinous cystic neoplasms (MCNs) confirmed by surgical or histopathological examination. Eligible patients will have undergone endoscopic ultrasound (EUS) as part of routine clinical care. EUS images and pre-examination clinical data will be collected for AI model development and validation. Both retrospective and prospective cases will be included. | |
| Serous Cystic Neoplasm (SCN) | Adult patients with pancreatic serous cystic neoplasms (SCNs) confirmed by surgical or histopathological examination. Eligible patients will have undergone endoscopic ultrasound (EUS) as part of routine clinical care. EUS images and pre-examination clinical data will be collected for AI model development and validation. Both retrospective and prospective cases will be included. | |
| Pancreatic Pseudocyst (PPC) | Adult patients diagnosed with pancreatic pseudocysts (PPCs) according to a predefined composite reference standard incorporating clinical history, imaging findings, and relevant diagnostic evidence. Eligible patients will have undergone endoscopic ultrasound (EUS) as part of routine clinical care. EUS images and pre-examination clinical data will be collected for AI model development and validation. Both retrospective and prospective cases will be included. |
What is being measured
The primary outcome is the main question the study is designed to answer.
Patient-Level Macro-AUROC of the Multimodal AI Model for Three-Class Classification of MCN, SCN, and PPC
The diagnostic performance of the locked multimodal artificial intelligence (AI) model will be evaluated in an independent prospective temporal validation cohort using the patient-level macro-averaged area under the receiver operating characteristic curve (macro-AUROC). One-vs-rest AUROC will be calculated separately for mucinous cystic neoplasms (MCNs), serous cystic neoplasms (SCNs), and pancreatic pseudocysts (PPCs). The three AUROC values will then be averaged with equal weighting. Each patient will contribute one final prediction, and 95% confidence intervals will be estimated using bootstrap resampling. Higher values indicate better discrimination.
Measured: At completion of diagnostic assessment in the prospective temporal validation cohort, through December 2028.
Secondary outcomes (8)
Overall Patient-Level Classification Accuracy of the AI Model
Overall accuracy will be calculated as the proportion of patients correctly classified as MCN, SCN, or PPC among all patients evaluated in the prospective validation cohort. Accuracy will be reported as a percentage with a 95% confidence interval.
Measured: At completion of diagnostic assessment in the prospective temporal validation cohort, through December 2028.
Macro-Averaged F1 Score of the AI Model
The F1 score will be calculated separately for MCN, SCN, and PPC using the harmonic mean of precision and recall. The macro-F1 score will be calculated as the unweighted average of the three class-specific F1 scores. Values range from 0 to 1, with higher values indicating better classification performance.
Measured: At completion of diagnostic assessment in the prospective temporal validation cohort, through December 2028.
Difference in Patient-Level Macro-AUROC Between Multimodal and Unimodal AI Models
The difference in patient-level macro-AUROC between the locked multimodal AI model and each unimodal model (EUS image-only and clinical feature-only) will be calculated in the independent prospective validation cohort. Each comparison will be reported as the arithmetic difference in macro-AUROC, with positive values indicating better discrimination by the multimodal model.
Measured: At completion of diagnostic assessment in the prospective temporal validation cohort, through December 2028.
Calibration Intercept of the Multimodal AI Model
The calibration intercept of the locked multimodal AI model will be estimated in the prospective validation cohort to assess systematic overestimation or underestimation of predicted probabilities. A value closer to 0 indicates better calibration.
Measured: At completion of diagnostic assessment in the prospective temporal validation cohort, through December 2028.
Change in Physicians' Diagnostic Accuracy With AI Assistance
EUS physicians with different levels of experience will evaluate cases with and without AI assistance. The primary measure for the reader study will be the paired difference in patient-level three-class diagnostic accuracy between AI-assisted and unaided interpretation. Additional reader outcomes will include macro-AUROC, macro-F1 score, diagnostic confidence, interobserver agreement, and interpretation time.
Measured: At completion of diagnostic assessment in the prospective temporal validation cohort, through December 2028.
Difference in Patient-Level Classification Accuracy Between Multimodal and Unimodal AI Models
The difference in overall patient-level classification accuracy between the locked multimodal AI model and each unimodal model (EUS image-only and clinical feature-only) will be calculated in the independent prospective validation cohort. Differences will be expressed in percentage points, with positive values indicating higher accuracy for the multimodal model.
Measured: At completion of diagnostic assessment in the prospective temporal validation cohort, through December 2028.
Calibration Slope of the Multimodal AI Model
The calibration slope of the locked multimodal AI model will be estimated in the prospective validation cohort to assess the agreement between the spread of predicted probabilities and observed outcomes. A value closer to 1 indicates better calibration.
Measured: At completion of diagnostic assessment in the prospective temporal validation cohort, through December 2028.
Multiclass Brier Score of the Multimodal AI Model
The multiclass Brier score will be calculated as the mean, across patients, of the sum of squared differences between the predicted probabilities for MCN, SCN, and PPC and their corresponding observed class indicators. The unnormalised score ranges from 0 to 2, with lower values indicating better probabilistic predictions.
Measured: At completion of diagnostic assessment in the prospective temporal 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 posted5 Nov 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 Pancreatic Mucinous Cystic NeoplasmStudies 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
- 400 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Ruijin Hospital
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07919704
- Sponsor's protocol ID
- MR-31-26-085874
- Status last verified
- Oct 2026
- First submitted
- 22 Oct 2026
- Last updated
- 5 Nov 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 5 Nov 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Artificial Intelligence-Assisted Differentiation of Pancreatic Mucinous Cystic Neoplasms, Serous Cystic Neoplasms, and Pseudocysts Using Endoscopic Ultrasound Images and Clinical Features: An Ambispective Observational Study
- ClinicalTrials.gov ID
- NCT07919704
- Acronym / sponsor ID
- MR-31-26-085874
- Conditions
- Pancreatic Mucinous Cystic Neoplasm, Pancreatic Serous Cystic Neoplasm, Pancreatic Pseudocyst
- Keywords
- Endoscopic ultrasound, Artificial intelligence, Deep learning, Pancreatic cystic lesions, Multimodal learning, Prospective validation
- Study type
- Observational
- Recruitment status
- Not yet recruiting
- Enrolment
- 400 participants (estimated)
- Lead sponsor
- Ruijin HospitalUniversity, hospital or other
- Study start
- 1 Nov 2026 (estimated)
- Primary completion
- 30 Jun 2028 (estimated)
- Study completion
- 30 Dec 2028 (estimated)
- First posted
- 5 Nov 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.
Pancreatic mucinous cystic neoplasms (MCNs), serous cystic neoplasms (SCNs), and pancreatic pseudocysts (PPCs) often exhibit overlapping imaging features on endoscopic ultrasound (EUS). However, these conditions require different clinical management strategies. Accurate differentiation is therefore important for appropriate treatment planning.
This study aims to develop and validate an artificial intelligence (AI) model that integrates EUS images and clinical features to differentiate MCNs, SCNs, and PPCs. The study is designed as an ambispective observational diagnostic study consisting of retrospective model development, prospective temporal validation, and a reader study involving EUS physicians.
Approximately 400 adult patients with confirmed MCN, SCN, or PPC will be included. Retrospective clinical data and EUS images will be used for model development and internal validation. After model development is completed, newly diagnosed patients will be prospectively enrolled to independently evaluate the diagnostic performance of the model.
The primary outcome is the patient-level macro-averaged area under the receiver operating characteristic curve (macro-AUROC) for three-class classification in the prospective validation cohort. Secondary outcomes include diagnostic accuracy, sensitivity, specificity, calibration, clinical net benefit, and the performance of EUS physicians with and without AI assistance.
This study is expected to provide evidence for the potential clinical value of AI-assisted differentiation of pancreatic cystic lesions. No additional invasive procedures or study-specific treatments will be performed.
Detailed description
This study is an ambispective observational diagnostic study designed to develop and independently validate a multimodal artificial intelligence (AI) model for differentiating pancreatic mucinous cystic neoplasms (MCNs), serous cystic neoplasms (SCNs), and pancreatic pseudocysts (PPCs) using endoscopic ultrasound (EUS) images and clinical features.
Study Design
Approximately 400 adult patients with a final diagnosis of MCN, SCN, or PPC will be included. The study consists of two stages: retrospective model development and prospective temporal validation. A reader study will also be conducted to evaluate the potential benefits of AI assistance for EUS physicians.
Stage 1: Retrospective Model Development
Approximately 310 patients will be retrospectively identified from existing clinical databases. EUS images, clinical characteristics available before EUS examination, and final diagnostic information will be collected.
MCN and SCN diagnoses will be established using surgical or histopathological findings. PPC will be diagnosed according to a predefined composite reference standard based on clinical history, imaging findings, and other relevant diagnostic evidence.
Separate models will be developed using EUS images alone, clinical features alone, and the combination of EUS images and clinical features. Multiple EUS images from the same patient will be aggregated to generate a single patient-level prediction. Patient-level stratified cross-validation will be used for internal validation, with measures implemented to prevent data leakage.
Stage 2: Prospective Temporal Validation
After the AI model and analysis procedures have been finalized, approximately 80-100 consecutive eligible patients will be prospectively enrolled for independent temporal validation. These patients will not be used for model training, feature selection, or parameter optimization.
The primary outcome will be the patient-level macro-averaged area under the receiver operating characteristic curve (macro-AUROC) for three-class classification of MCN, SCN, and PPC. Each disease category will be evaluated using a one-vs-rest approach, and the three AUROC values will be averaged. A 95% confidence interval will be estimated using bootstrap resampling.
Secondary evaluations will include overall accuracy, macro-F1 score, weighted F1 score, class-specific sensitivity, specificity, positive predictive value, negative predictive value, model calibration, and clinical net benefit.
Reader Study
EUS physicians with different levels of experience will independently evaluate selected cases. Diagnostic performance will be compared among physicians without AI assistance, the AI model alone, and physicians with AI assistance. Diagnostic confidence, interobserver agreement, and interpretation time will also be assessed.
Model Interpretability
Gradient-weighted class activation mapping (Grad-CAM) or attention-based visualization will be used to identify image regions contributing to model predictions. Shapley additive explanations (SHAP) will be used to evaluate the contributions of clinical variables.
Exploratory analyses will investigate model performance across predefined patient subgroups, the potential incremental value of EUS videos, and differences among pathological subtypes of MCN.
Clinical Procedures and Patient Safety
All EUS examinations, pathological assessments, and clinical treatments will be performed according to routine clinical practice. No additional invasive procedures or study-specific therapeutic interventions will be required. AI predictions during prospective validation will not be used to guide clinical decisions.
Study design
- Observational model
- Cohort
- Time perspective
- Other
- Number of arms
- 3
| Arm | Type | Receives |
|---|---|---|
| Mucinous Cystic Neoplasm (MCN) | – | –Adult patients with pancreatic mucinous cystic neoplasms (MCNs) confirmed by surgical or histopathological examination. Eligible patients will have undergone endoscopic ultrasound (EUS) as part of routine clinical care. EUS images and pre-examination clinical data will be collected for AI model development and validation. Both retrospective and prospective cases will be included. |
| Serous Cystic Neoplasm (SCN) | – | –Adult patients with pancreatic serous cystic neoplasms (SCNs) confirmed by surgical or histopathological examination. Eligible patients will have undergone endoscopic ultrasound (EUS) as part of routine clinical care. EUS images and pre-examination clinical data will be collected for AI model development and validation. Both retrospective and prospective cases will be included. |
| Pancreatic Pseudocyst (PPC) | – | –Adult patients diagnosed with pancreatic pseudocysts (PPCs) according to a predefined composite reference standard incorporating clinical history, imaging findings, and relevant diagnostic evidence. Eligible patients will have undergone endoscopic ultrasound (EUS) as part of routine clinical care. EUS images and pre-examination clinical data will be collected for AI model development and validation. Both retrospective and prospective cases will be included. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- The study population will consist of approximately 400 adult patients aged 18 years or older with a final diagnosis of pancreatic mucinous cystic neoplasm (MCN), serous cystic neoplasm (SCN), or pancreatic pseudocyst (PPC) who have undergone endoscopic ultrasound (EUS) at Ruijin Hospital, Shanghai Jiao Tong University School of Medicine. Approximately 310 retrospective patients will be identified from existing clinical records for AI model development and internal validation. An additional 80-100 eligible patients will be consecutively enrolled for prospective temporal validation after model finalization. MCN and SCN diagnoses will be confirmed by pathology, whereas PPC diagnoses will be established using a predefined composite reference standard. Each patient will contribute one independent study sample regardless of the number of available EUS images.
Key inclusion criteria (10 listed)
- Inclusion Criteria:
Key exclusion criteria (10 listed)
- Severely degraded EUS image quality preventing reliable identification of the target lesion.
- Availability of only post-treatment or postoperative EUS images, without pretreatment imaging.
- Lack of reliable pathological confirmation for MCN or SCN.
- Inability to establish a clear correspondence between the EUS images and the pathologically confirmed lesion.
- Uncertain or disputed final diagnosis that remains unresolved after expert review.
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
Patient-Level Macro-AUROC of the Multimodal AI Model for Three-Class Classification of MCN, SCN, and PPC
Time frame: At completion of diagnostic assessment in the prospective temporal validation cohort, through December 2028.
The diagnostic performance of the locked multimodal artificial intelligence (AI) model will be evaluated in an independent prospective temporal validation cohort using the patient-level macro-averaged area under the receiver operating characteristic curve (macro-AUROC). One-vs-rest AUROC will be calculated separately for mucinous cystic neoplasms (MCNs), serous cystic neoplasms (SCNs), and pancreatic pseudocysts (PPCs). The three AUROC values will then be averaged with equal weighting. Each patient will contribute one final prediction, and 95% confidence intervals will be estimated using bootstrap resampling. Higher values indicate better discrimination.
8 secondary outcomes
Overall Patient-Level Classification Accuracy of the AI Model
Time frame: At completion of diagnostic assessment in the prospective temporal validation cohort, through December 2028.
Macro-Averaged F1 Score of the AI Model
Time frame: At completion of diagnostic assessment in the prospective temporal validation cohort, through December 2028.
Difference in Patient-Level Macro-AUROC Between Multimodal and Unimodal AI Models
Time frame: At completion of diagnostic assessment in the prospective temporal validation cohort, through December 2028.
Calibration Intercept of the Multimodal AI Model
Time frame: At completion of diagnostic assessment in the prospective temporal validation cohort, through December 2028.
Change in Physicians' Diagnostic Accuracy With AI Assistance
Time frame: At completion of diagnostic assessment in the prospective temporal validation cohort, through December 2028.
Difference in Patient-Level Classification Accuracy Between Multimodal and Unimodal AI Models
Time frame: At completion of diagnostic assessment in the prospective temporal validation cohort, through December 2028.
Calibration Slope of the Multimodal AI Model
Time frame: At completion of diagnostic assessment in the prospective temporal validation cohort, through December 2028.
Multiclass Brier Score of the Multimodal AI Model
Time frame: At completion of diagnostic assessment in the prospective temporal 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 5 Nov 2026.
- Individual participant dataSharing planned: No
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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.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Pancreatic Mucinous Cystic Neoplasm and Pancreatic Serous Cystic Neoplasm, run by Ruijin Hospital at 1 site in China.
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 10 inclusion and 10 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 5 Nov 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in China. Whether a site is taking part right now should be confirmed with that site. See every site.
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?
Patient-Level Macro-AUROC of the Multimodal AI Model for Three-Class Classification of MCN, SCN, and PPC (time frame: At completion of diagnostic assessment in the prospective temporal 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 5 Nov 2026.