AI-powered Early Detection for Pancreatic Cancer Via Non-contrast CT in Opportunistic Screening Cohort (AI-PANC)

March 14, 2025 updated by: Changhai Hospital

Artificial Intelligence-based Health Information Management System and Key Technology Study of Early Screening and Hierarchical Diagnosis and Treatment of Pancreatic Cancer

Pancreatic ductal adenocarcinoma (PDAC) remains a therapeutic challenge with 5-year survival rates of 13%, primarily attributable to advanced-stage diagnosis (AJCC Stage III/IV in >80% of cases). This prospective, observational, multi-center study will evaluate the performance of an AI-powered opportunistic screening system utilizing non-contrast computed tomography (NCCT) acquired during routine clinical encounters or health check-ups. The proposed AI model will perform automated detection of pancreatic parenchymal abnormalities, including PDAC and precursor lesions (intraductal papillary mucinous neoplasms [IPMN], mucinous cystic neoplasms [MCN]). Algorithm-positive cases will be independently reviewed by two radiologists. Highly suspected individuals will undergo further diagnostic verification, including serological tests and multimodal imaging confirmation. Patients with confirmed positive diagnosis will receive multidisciplinary consultation and specialized treatment, whereas those with negative results will undergo at least one-year clinical follow-up. This study will quantitatively evaluate the AI system's performance, and aims to advance PDAC early detection, improve patient outcomes, and make it accessible in underserved populations.

Study Overview

Status

Recruiting

Conditions

Intervention / Treatment

Detailed Description

PDAC is projected to become the second-leading cause of cancer mortality by 2030, with stage-specific survival disparities reaching 83.7% for stage IA versus 2.9% for stage IV disease. This dramatic survival gradient highlights the transformative potential of stage migration through early detection.

Screening-based early detection has demonstrated improved prognosis for PDAC patients; however, implementation faces dual challenges. he low incidence of PDAC renders population-wide screening cost-ineffective, while current screening methods are hampered by high false-positive rates and overdiagnosis risks. In this context, opportunistic screening has garnered attention for its unique implementation advantages. By leveraging existing imaging resources from routine clinical encounters or health check-ups, this approach obviates the need for additional screening infrastructure, potentially reducing healthcare resource consumption while effectively increasing screening coverage among high-risk populations.

Non-contrast computed tomography (NCCT), despite its widespread clinical application and operational convenience, is limited by suboptimal soft tissue resolution, resulting in insufficient sensitivity for early pancreatic lesions (≤2 cm), thus significantly constraining its utility in opportunistic screening. Recent advancements in AI technology have significantly impacted the field of medical image analysis. These techniques have enabled the automation of the detection of subtle pancreatic lesion features in large-scale imaging data, with the potential to enhance the accuracy and efficiency of early pancreatic cancer detection. In preliminary research, a deep learning-based model for pancreatic cancer detection was developed by our team. This model demonstrated the ability to accurately detect and classify pancreatic lesions on NCCT images, with excellent performance in multicenter validation studies. The model also exhibited strong generalizability when applied to chest CT scans. Therefore, AI-powered NCCT shows significant potential for application in hospital-based opportunistic screening programs and may become an effective tool for early pancreatic cancer detection. However, further research is required to fully explore and realize this potential.

This prospective, observational, multi-center study will evaluate the performance of an AI-powered opportunistic screening system utilizing NCCT acquired during routine clinical encounters or health check-ups. The deep learning-based detection system will perform automated identification of pancreatic lesions, including PDAC and precursor entities (intraductal papillary mucinous neoplasms [IPMN], mucinous cystic neoplasms [MCN]). Algorithm-positive cases will be independently reviewed by two radiologists. Individuals with high suspicion after radiologists review will undergo further validation via serological tests (e.g., CA19-9, CEA) and imaging studies (e.g., contrast-enhanced CT, contrast-enhanced MRI, EUS-FNA). Participants with a confirmed positive diagnosis will undergo multidisciplinary consultation and specialized treatment, while those with a negative diagnosis will be followed clinically for at least one year.

The AI system's performance will be evaluated through three primary metrics: (1) Detection rate of PDAC and high-risk precursor lesions, defined as the proportion of histologically confirmed PDAC and precursor lesions (IPMN/MCN) meeting Sendai criteria among all participants undergoing CT screening. (2) Recall rate, defined as the proportion of individuals recalled for confirmatory testing after AI-positive screening and radiologist review among all participants undergoing CT screening. (3) Positive predictive value (PPV) defined as the proportion of histologically confirmed PDAC and high-risk precursor lesions among all AI-positive screening cases.

Institutional Collaboration: Led by Shanghai Changhai Hospital (PI: Gang Jin, MD) with five regional centers (Yinzhou Hospital, Jiaxing University Hospital, Lishui Central Hospital, Jingning County Hospital) and Alibaba DAMO Academy (technical support).

Study Type

Observational

Enrollment (Estimated)

5000

Phase

  • Not Applicable

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Study Contact Backup

  • Name: Guo Shi Wei, M.D.
  • Phone Number: 86-18621500666
  • Email: gestwa@163.com

Study Locations

  • China
    • Shanghai
      • Shanghai, Shanghai, China, 200433
        • Recruiting
        • Shanghai Changhai Hospital
        • Contact:
        • Contact:
          • Jin Gang, M.D.
        • Contact:
          • Wang Bei Lei, M.D.
    • Zhejiang
      • Jiaxing, Zhejiang, China, 314000
        • Recruiting
        • Second Affiliated Hospital of Jiaxing University
        • Contact:
        • Contact:
          • Shen Yi Jue, M.D.
      • Ningbo, Zhejiang, China, 315100
        • Recruiting
        • Yinzhou Hospital Affiliated to Medical School of Ningbo University
        • Contact:
        • Contact:
          • Zhu Ke Lei, M.D.

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Adult
  • Older Adult

Accepts Healthy Volunteers

Yes

Sampling Method

Non-Probability Sample

Study Population

The study population included adults aged 18 years or older undergoing routine non-contrast chest and/or abdominal CT scans for non-pancreatic indications, while exclusion criteria comprised a history of pancreatic cancer, thoracic or abdominal surgery, acute pancreatitis within the past 6 months, or referral for evaluation of suspected or confirmed pancreatic cancer.

Description

Inclusion Criteria

1. Individuals undergoing routine non-contrast chest and/or abdominal CT scans for non-pancreatic indications.

Exclusion Criteria

  1. History of pancreatic cancer;
  2. History of thoracic or abdominal surgery;
  3. Acute pancreatitis within 6 months;
  4. Patients referred for evaluation of suspected or confirmed pancreatic cancer.

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

Cohorts and Interventions

Group / Cohort
Intervention / Treatment
AIgorithm-classified PDAC Group
Participants who underwent non-contrast abdominal and/or chest CT scans and were preliminarily classified by the aIgorithm as PDAC.
Diagnostic test: PDAC
Participants with algorithm-identified PDAC will be independently reviewed by two radiologists. Those highly suspected will be recalled for further diagnostic evaluation, including serological tests (e.g., CA19-9, CEA) and imaging (e.g., contrast-enhanced CT/MRI, EUS-FNA). Participants with a confirmed positive diagnosis will undergo multidisciplinary consultation and specialized treatment, while those with a negative diagnosis will be followed clinically for at least one year.
AIgorithm-classified Pancreatic Precursor Lesions Group
Participants who underwent non-contrast abdominal and/or chest CT scans and were preliminarily classified by the aIgorithm as pancreatic precursor lesions.
Diagnostic test: Pancreatic precursor lesions
Participants with algorithm-identified pancreatic precursor lesions will be independently reviewed by two radiologists. Those highly suspected will be recalled for further diagnostic evaluation, including serological tests (e.g., CA19-9, CEA) and imaging (e.g., contrast-enhanced CT/MRI, EUS-FNA). Participants with a confirmed positive diagnosis will undergo multidisciplinary consultation and specialized treatment, while those with a negative diagnosis will be followed clinically for at least one year.

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Detection rate of PDAC
Time Frame: 3 years
Defined as the proportion of histologically confirmed PDAC among all participants undergoing CT screening.
3 years
Detection rate of high-risk precursor lesions
Time Frame: 3 years
Defined as the proportion of histologically confirmed precursor lesions (IPMN/MCN) meeting Sendai criteria among all participants undergoing CT screening.
3 years
PPV
Time Frame: 3 years
Defined as the proportion of histologically confirmed PDAC and high-risk precursor lesions among all AI-positive screening cases.
3 years
Recall rate
Time Frame: 3 years
Defined as the proportion of individuals recalled for further validation via serological and imaging tests after AI-positive screening and radiologist review among all participants undergoing CT screening.
3 years

Secondary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Early-stage PDAC Proportion
Time Frame: 3 years
Defined as the proportion of histologically confirmed early-stage PDAC among all PDAC cases detected through CT screening.
3 years
Survival time
Time Frame: 5 years
Defined as the survival time of patients with PDAC or precursor lesions detected through screening.
5 years

Other Outcome Measures

Outcome Measure
Measure Description
Time Frame
Potential harms associated with screening procedures and treatments
Time Frame: 3 years
Defined as the potential adverse effects associated with screening procedures (e.g., contrast-enhanced CT/MRI, EUS-FNA) and treatments (e.g., postoperative complications).
3 years

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Changhai Hospital

Collaborators

The Second Affiliated Hospital of Jiaxing University
Yinzhou Hospital Affiliated to Medical School of Ningbo University
Jingning County People's Hospital
Alibaba DAMO Academy
Central Hospital of Lishui City

Investigators

  • Study Chair: Jin Gang, M.D., Changhai Hospital
  • Study Director: Wang Bei Lei, M.D., Changhai Hospital

Publications and helpful links

The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.

General Publications

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

August 3, 2024

Primary Completion (Estimated)

December 31, 2029

Study Completion (Estimated)

December 31, 2030

Study Registration Dates

First Submitted

October 9, 2024

First Submitted That Met QC Criteria

October 9, 2024

First Posted (Actual)

October 15, 2024

Study Record Updates

Last Update Posted (Actual)

March 25, 2025

Last Update Submitted That Met QC Criteria

March 14, 2025

Last Verified

March 1, 2025

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • AI-PANC-1
  • 202401063 (Other Grant/Funding Number: Shanghai Municipal Bureau of Data)
  • 202440208 (Other Grant/Funding Number: Shanghai Municipal Health Commission)
  • 20511101200 (Other Grant/Funding Number: Shanghai Municipal Science and Technology Commission)

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

NO

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

product manufactured in and exported from the U.S.

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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