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Recruiting NCT07439757

AI-Powered Precision Decision-Making for Pancreatic Diseases

— Observational Pancreatic Cancer Diagnose Disease IPMN, Pancreatic Pancreatic Cystic Lesions

For patients and families

In plain language

An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.

What is being studied
The protocol lists: Diagnosis by Artificial Intelligence model.
Who it may be relevant to
Registry conditions: Pancreatic Cancer, Diagnose Disease, IPMN, Pancreatic, Pancreatic Cystic Lesions. Basic parameters: 18 years — 80 years · All.
What needs checking
Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
Where it takes place
China
Next step
Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →

Unsure about the terms? Read our patient guide →

Official title

A Multicenter Clinical Study on AI-Powered Precision Decision-Making Management for Pancreatic Diseases Using Contrast-Enhanced CT

Overview

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.

Detailed description

MEHTOD： This multicenter clinical trial evaluates the reliability and effectiveness of an AI system for patients with pancreatic diseases in a real-world clinical environment. The study calculates the AI system's classification accuracy using pathological diagnosis (biopsy/surgery results) or long-term follow-up as the "gold standard" for comparison. Additionally, the safety and clinical utility of the management strategies recommended by the AI are assessed by measuring the risk of missing malignant lesions, the rate of unnecessary surgeries for pancreatic diseases, and the level of agreement with traditional clinical decisions.

STUDY DESIGN

All contrast-enhanced CT images from patients with pancreatic diseases are analyzed by the AI system to generate a classification result (Intervention, Intensive Surveillance, or Routine Surveillance). Simultaneously, clinical doctors review the same data and categorize patients into these three groups to determine their actual care plan:

1. INTERVENTION: Patients assessed by doctors as needing "Intervention" are recommended for further surgical evaluation or treatment. 2. INTENSIVE SURVEILLANCE: Patients assessed by doctors as needing "Intensive Surveillance" receive a personalized, high-frequency follow-up plan until the study endpoint. 3. ROUTINE SURVEILLANCE: Patients assessed by doctors as needing "Routine Surveillance" undergo follow-up for at least one year. If abnormalities arise during this period, the patient is transferred to the appropriate "Intervention" or "Intensive Surveillance" protocol.

OUTCOMES： The study compares the performance of the AI system against clinical doctors regarding classification accuracy, the risk of missed diagnoses, unnecessary surgery rates, and decision consistency. These metrics are used to validate the AI system's value, safety, and utility in the clinical management of pancreatic diseases.

Interventions

  • Diagnostic test Diagnosis by Artificial Intelligence model
    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.

Primary outcome measures

  • Classification accuracy [Time frame: From date of contrast-enhanced CT scan to 1 year]
Secondary outcome measures (3)
  • Agreement rate with clinical decisions [Time frame: From date of contrast-enhanced CT scan to 1 year]
  • Percentage decrease in unnecessary surgical procedures [Time frame: From date of contrast-enhanced CT scan to 1 year]
  • Malignancy miss rate [Time frame: From date of contrast-enhanced CT scan to 1 year]

Eligibility criteria

Inclusion criteria

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

Exclusion criteria

  • 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.

Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.

Healthy volunteers: No

Study design

Observational model
Cohort

Study locations

China · 1 center
  • Changhai Hospital — Shanghai

Identifiers

NCT: NCT07439757 · Prospective PRISM

Primary sources (government registries)

View this study on ClinicalTrials.gov ↗