AI-Powered Precision Decision-Making for Pancreatic Diseases

NCT07439757 | Recruiting DMC

• 1 other identifier: Prospective PRISM
• Study Type: observational
• Target: 2,000
• Locations: 1 country
• Sites: 1

Brief Summary

This multicenter clinical trial evaluates an artificial intelligence (AI) system designed to assist in the diagnosis and management of pancreatic diseases. Using contrast-enhanced CT scans, the study compares the AI's recommendations against the decisions of experienced clinicians to verify the system's accuracy and safety in a real-world setting. Patients are categorized into three management groups: Intervention (surgery/treatment), Intensive Surveillance (close monitoring), or Routine Surveillance (standard follow-up). The primary goal is to determine if the AI system can reliably classify patients, reduce the risk of missing malignant lesions, and prevent unnecessary surgeries, thereby improving clinical decision-making for pancreatic conditions.

Conditions

Pancreatic Cancer; Diagnose Disease; IPMN, Pancreatic; Pancreatic Cystic Lesions; Chronic Pancreatitis; Pancreatic Neuroendocrine Tumor; Acute Pancreatitis (AP)

Keywords

Artificial Intelligence (AI), Deep Learning, Contrast-Enhanced CT, Multicenter Clinical Trial, Real-World Study

Outcome Measures

Primary Outcomes (1)

• Classification accuracy

  The percentage of cases correctly classified by AI out of the total number of cases.

  From date of contrast-enhanced CT scan to 1 year

Secondary Outcomes (3)

• Agreement rate with clinical decisions

  From date of contrast-enhanced CT scan to 1 year

• Percentage decrease in unnecessary surgical procedures

  From date of contrast-enhanced CT scan to 1 year

• Malignancy miss rate

  From date of contrast-enhanced CT scan to 1 year

Study Arms (2)

AI group

Diagnosis by Artificial Intelligence model

Diagnostic Test: Diagnosis by Artificial Intelligence model

Clinicians group

Diagnosis by clinicians

Interventions

Diagnosis by Artificial Intelligence model DIAGNOSTIC_TEST

To develop an artificial intelligence-based classification management system for pancreatic diseases, achieving automated and precise classification. Contrast-enhanced CT images from all study subjects will be analyzed by the AI system to generate classification results, categorizing patients into three groups: INTERVENTIOM, INTENSIVE SURVEILLANCE or ROUTINE SURVEILLANCE.

AI group

Eligibility Criteria

• Age: 18 Years - 80 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

The study enrolls patients with clinically suspected pancreatic disease who have available contrast-enhanced CT scans and provide informed consent. Patients are excluded if they have a history of pancreatic surgery, contraindications to contrast media, suboptimal image quality, or other conditions deemed unsuitable by the investigator (e.g., pregnancy, cognitive impairment, or concurrent trial participation).

You may qualify if:

• Clinically suspected pancreatic disease.
• Scheduled to undergo contrast-enhanced CT.
• Signed informed consent form indicating agreement to participate.

You may not qualify if:

• History of pancreatic surgery.
• Contraindications to contrast-enhanced CT, including known hypersensitivity to iodinated contrast media or severe renal/hepatic dysfunction.
• Suboptimal image quality affecting diagnosis.
• Concurrent participation in another interventional clinical trial.
• Unsuitability for participation as determined by the investigator, including but not limited to: pregnancy or lactation, severe psychiatric disorders or cognitive impairment, significant comorbidities that may interfere with study results or patient safety.

Sponsors & Collaborators

• Shanghai Changzheng Hospital: collaborator
• Xinhua Hospital, Shanghai Jiao Tong University School of Medicine: collaborator
• Shengjing Hospital: collaborator
• Changhai Hospital: lead
• The First Affiliated Hospital with Nanjing Medical University: collaborator
• The Affiliated People's Hospital of Ningbo University: collaborator
• The Second Affiliated Hospital of Jiaxing University: collaborator
• Shanghai Fourth People's Hospital Tongji University: collaborator
• The First Affiliated Hospital of Medical School of Zhejiang University: collaborator
• Shanghai Fudan University Cancer Center: collaborator

Study Sites (1)

Changhai Hospital

Shanghai, 200433, China

RECRUITING

MeSH Terms

Conditions

Pancreatic Neoplasms; Pancreatic Intraductal Neoplasms; Pancreatitis, Chronic; Adenoma, Islet Cell; Pancreatitis

Condition Hierarchy (Ancestors)

Digestive System Neoplasms; Neoplasms by Site; Neoplasms; Endocrine Gland Neoplasms; Digestive System Diseases; Pancreatic Diseases; Endocrine System Diseases; Neoplasms, Ductal, Lobular, and Medullary; Neoplasms, Glandular and Epithelial; Neoplasms by Histologic Type; Chronic Disease; Disease Attributes; Pathologic Processes; Pathological Conditions, Signs and Symptoms; Adenoma

Central Study Contacts

Beilei Wang, Doctor

CONTACT

+86 13774238083; lilly_wang@126.com

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Target Duration: 1 Year
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: February 23, 2026
• First Posted: February 27, 2026
• Study Start: March 1, 2026
• Primary Completion (Estimated): October 31, 2029
• Study Completion (Estimated): October 31, 2029
• Last Updated: February 27, 2026

Record last verified: 2026-02

Data Sharing

• IPD Sharing: Will not share

Locations

CN (1)