Research of the Application of Pancreatic Cancer Screening Artificial Intelligence Model "PANDAPro"

NCT06643715 | Recruiting

• 1 other identifier: ZY-PANDAPro
• Study Type: interventional
• Target: 100,000
• Locations: 1 country
• Sites: 1

Brief Summary

The purpose of this study is to build upon the previously developed deep learning-based non-contrast CT pancreatic cancer screening model, PANDA. The model will first undergo training and enhancement, followed by external validation across multiple centers. Subsequently, a large-scale real-world validation will be conducted at Zhejiang University's First Affiliated Hospital , the study will be divided into two rounds. In the first round, the performance of the PANDA model and the upgraded PANDA Pro model will be compared on consecutive retrospective real-world CT scans. In the second round, physicians will record the PANDA Pro results in real time to identify potential pancreatic lesions that may have been clinically missed. By leveraging clinical big data across different scenarios at Zhejiang University's First Affiliated Hospital, the study aims to validate the model's role in prompting and supplementing the diagnosis of PDAC in clinical practice, thereby laying the foundation for large-scale opportunistic screening of PDAC.

Conditions

Pancreatic Cancer

Keywords

Artificial Intelligence; Pancreatic Cancer; PANDA PLUS; PANDA

Outcome Measures

Primary Outcomes (1)

• Detection efficiency of doctors in pancreatic cancer assisted by PANDA Pro

  Sensitivity、Specificity、PPV、NPV

  Complete the statistics within six months after the patient is fully enrolled, and it is expected to take 2 years from the start of the study

Secondary Outcomes (2)

• TNM stage

  1 day (evaluate through CT imaging before surgery)

• Resectability grading

  1 day (evaluate through CT imaging before surgery)

Study Arms (1)

PANDA Pro

EXPERIMENTAL

recall of clinically missed but PANDA Pro detected pancreatic lesions

Device: PANDA Pro

Interventions

PANDA Pro DEVICE

Patients with PANDA Pro-reported PDAC positivity but no pancreatic lesions indicated in the imaging report, or those with positive pancreatic findings in the imaging report but no subsequent clinical intervention, will be identified as requiring follow-up. These patients will be recalled to the First Affiliated Hospital of Zhejiang University for further examination and diagnosis.

PANDA Pro

Eligibility Criteria

• Age: 18 Years - 90 Years
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• Subjects who have undergone chest and/or abdominal CT scans at outpatient clinics, inpatient departments, or physical examination centers;
• Age at the time of the scan between 18-90 years old, with no restriction on gender;

You may not qualify if:

• Chest CT scans that do not cover the pancreas;
• Non-contrast CT scans performed in emergency settings;
• Patients who have undergone thoracic/abdominal surgeries affecting or altering the anatomical display of the pancreas (e.g., post-esophageal, gastric, pancreatic, vascular surgeries, or post-ERCP);
• Non-standard scans (e.g., hands placed on either side of the body or abdomen, severe respiratory motion artifacts, perfusion contamination, etc.);
• CT scans ordered by hepatobiliary and pancreatic surgeons or oncologists;
• Patients referred to a higher-level hospital due to a pancreatic mass found during local hospital examination;
• Patients who, for personal reasons, did not follow up with pancreatic cancer diagnosis or treatment at the hospital, or were lost to follow-up midway;
• Patients with concurrent malignancies in other locations or those undergoing comprehensive cancer treatment for malignant tumors;
• Imaging reports made by radiologists without referring to AI during the image interpretation;
• Patients who underwent enhanced CT, MRI, or PET-CT examinations concurrently.

Sponsors & Collaborators

• Zhejiang University: lead
• Lishui Municipal Central Hospital: collaborator
• Second Affiliated Hospital of Nanchang University: collaborator

Study Sites (1)

the First Affiliated Hospital, School of Medicine, Zhejiang University

Hangzhou, Zhejiang, 310000, China

RECRUITING

MeSH Terms

Conditions

Pancreatic Neoplasms

Condition Hierarchy (Ancestors)

Digestive System Neoplasms; Neoplasms by Site; Neoplasms; Endocrine Gland Neoplasms; Digestive System Diseases; Pancreatic Diseases; Endocrine System Diseases

Central Study Contacts

Qi Zhang

CONTACT

13819137113; qi.zhang@zju.edu.cn

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: NA
• Masking: NONE
• Purpose: DIAGNOSTIC
• Intervention Model: SINGLE GROUP
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Professor

Model Details: The PANDA (Pancreatic Cancer Detection with Artificial Intelligence) early screening model was developed by Alibaba DAMO Academy.It employs a fully automated pancreatic segmentation model using the VoxelMorph algorithm, which enhances registration speed and accuracy. By building a self-learning framework, the model efficiently obtains precise annotations of non-contrast CT images. The core network is based on Transformer technology, combined with the MaskFormer model to improve diagnostic accuracy. The upgraded PANDA Pro model has improved upon the original PANDA model by enhancing its ability to differentiate between pancreatitis, pancreatic cystic lesions, and eliminating interference from adjacent organs such as the common bile duct and duodenum. These advancements have effectively increased the model's clinical utility, making it more reliable for real-world applications in diagnosing pancreatic conditions.

Study Record Dates

• First Submitted: October 10, 2024
• First Posted: October 16, 2024
• Study Start: November 18, 2025
• Primary Completion (Estimated): August 1, 2027
• Study Completion (Estimated): August 1, 2027
• Last Updated: March 10, 2026

Record last verified: 2026-02

Data Sharing

• IPD Sharing: Will not share

Locations

CN (1)