Artificial Intelligence-powered Low-Dose Computed Tomography for Screening of Pancreatic Cancer

NCT07117045 | Recruiting DMC

• 4 other identifiers: AI-LDCT-PC2024010632024402082025AAA031146
• Study Type: observational
• Target: 400,000
• Locations: 1 country
• Sites: 5

Brief Summary

Pancreatic ductal adenocarcinoma (PDAC) has a poor prognosis, with early diagnosis crucial for improving survival. Due to the absence of effective screening methods, most patients are diagnosed at advanced stages. The population undergoing low-dose computed tomography (LDCT) screening significantly overlaps with those at high risk for PDAC; however, traditional imaging methods have limited sensitivity for detecting pancreatic lesions. This study utilizes the Pancreatic Cancer Detection with Artificial Intelligence (PANDA) system to enhance LDCT for pancreatic cancer screening in a prospective, multicenter, observational cohort. PANDA will analyze LDCT images, followed by a multidisciplinary team (MDT) reassessment of abnormal interpretations. Based on MDT evaluation, individuals will be recalled for further examination, placed under a personalized follow-up plan, or monitored for at least one year. The primary outcomes include pancreatic cancer detection rate, positive predictive value, consensus rate, and recall rate, while secondary outcomes focus on early-stage cancers, resectable tumors, and safety indicators such as false positive rates and unnecessary procedures. This study aims to assess the effectiveness and safety of AI-assisted LDCT for PDAC detection, providing a practical solution for improving public health and enhancing early diagnostic capabilities.

Conditions

Pancreatic Cancer; Intraductal Papillary Mucinous Neoplasm; High-grade Pancreatic Intraepithelial Neoplasia; PDAC - Pancreatic Ductal Adenocarcinoma; Mucinous Cystic Neoplasm

Keywords

Screening; Early Diagnosis; Pancreatic Cancer; Artificial Intelligence; Computed Tomography

Outcome Measures

Primary Outcomes (4)

• Pancreatic cancer detection rate

  The proportion of individuals with abnormal AI assessment confirmed as pancreatic cancer or precancerous lesions among the total screened population

  2 years

• Positive predictive value

  The proportion of individuals with abnormal AI assessment confirmed as pancreatic cancer or precancerous lesions among all individuals with abnormal AI assessment

  2 years

• Consensus rate

  The proportion of individuals with abnormal AI assessment deemed suspicious for pancreatic cancer or precancerous lesions by MDT requiring recall among the total screened population.

  2 years

• Recall rate

  The proportion of individuals actually recalled among the total screened population.

  2 years

Secondary Outcomes (2)

• Early-stage pancreatic cancer proportion

  2 years

• Resectable pancreatic cancer proportion

  2 years

Other Outcomes (4)

• False positive rate

  2 years

• Unnecessary invasive examination proportion

  2 years

• Unnecessary surgery proportion

  2 years

Study Arms (1)

AI-powered LDCT (LDCT+AI)

Participants will undergo annual screening with the LDCT+AI system.

Diagnostic Test: Diagnostic Evaluation for Positive AI Findings

Interventions

Diagnostic Evaluation for Positive AI Findings DIAGNOSTIC_TEST

MDT will review positive AI findings (including PDAC, pancreatic precursor lesions and benign lesion) cases to determine next steps: (1) Suspected PDAC and pancreatic precursor lesions are referred for hospital examination with diagnostic results collected; (2) Benign lesion cases receive personalized monitoring until endpoint events or study end; (3) Cases with positive AI findings but MDT-confirmed normal pancreatic issues receive at least one year of follow-up. If any abnormal results arise, management will transition to either plan (1) or (2).

AI-powered LDCT (LDCT+AI)

Eligibility Criteria

• Age: 50 Years+
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

The study population includes asymptomatic individuals aged 50 years and above who have undergone routine low-dose chest CT (LDCT) scans at health check-up centers. Eligible participants must provide written informed consent and be willing to attend all scheduled follow-up visits. Exclusion criteria include a previous history of pancreatic cancer, abdominal inflammation or diagnosis of acute pancreatitis within 6 months, poor image quality due to ascites, pancreatic trauma, thoracic/abdominal surgery, radiotherapy or chemotherapy, and research subjects unable to complete follow-up due to physical or other reasons.

You may qualify if:

• Age 50 years and above.
• Voluntary signing of informed consent.
• Completion of LDCT examination.

You may not qualify if:

• Previous history of pancreatic cancer.
• Abdominal inflammation or diagnosis of acute pancreatitis within 6 months.
• Poor image quality due to ascites, pancreatic trauma, thoracic/abdominal surgery, radiotherapy or chemotherapy.
• Research subjects unable to complete follow-up due to physical or other reasons.

Sponsors & Collaborators

• Changhai Hospital: lead
• Ningbo University Affiliated People's Hospital: collaborator
• Jiaxing University Affiliated Second Hospital: collaborator
• Meinian Onehealth Healthcare Holdings Co., Ltd: collaborator
• Ruici Medical Examination Institution: collaborator

Study Sites (5)

Meinian Onehealth Healthcare Holdings Co., Ltd

Shanghai, Shanghai Municipality, 200072, China

RECRUITING

Ruici Medical Examination Institution

Shanghai, Shanghai Municipality, 200126, China

RECRUITING

Changhai Hospital

Shanghai, Shanghai Municipality, 200433, China

RECRUITING

Jiaxing University Affiliated Second Hospital

Jiaxing, Zhejiang, 314000, China

RECRUITING

Ningbo University Affiliated People's Hospital

Ningbo, Zhejiang, 315100, China

RECRUITING

Related Publications (9)

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  PMID: 37985692 BACKGROUND

• Mizrahi JD, Surana R, Valle JW, Shroff RT. Pancreatic cancer. Lancet. 2020 Jun 27;395(10242):2008-2020. doi: 10.1016/S0140-6736(20)30974-0.

  PMID: 32593337 BACKGROUND

• Pereira SP, Oldfield L, Ney A, Hart PA, Keane MG, Pandol SJ, Li D, Greenhalf W, Jeon CY, Koay EJ, Almario CV, Halloran C, Lennon AM, Costello E. Early detection of pancreatic cancer. Lancet Gastroenterol Hepatol. 2020 Jul;5(7):698-710. doi: 10.1016/S2468-1253(19)30416-9. Epub 2020 Mar 2.

  PMID: 32135127 BACKGROUND

• Attiyeh MA, Chakraborty J, Doussot A, Langdon-Embry L, Mainarich S, Gonen M, Balachandran VP, D'Angelica MI, DeMatteo RP, Jarnagin WR, Kingham TP, Allen PJ, Simpson AL, Do RK. Survival Prediction in Pancreatic Ductal Adenocarcinoma by Quantitative Computed Tomography Image Analysis. Ann Surg Oncol. 2018 Apr;25(4):1034-1042. doi: 10.1245/s10434-017-6323-3. Epub 2018 Jan 29.

  PMID: 29380093 BACKGROUND

• Ardila D, Kiraly AP, Bharadwaj S, Choi B, Reicher JJ, Peng L, Tse D, Etemadi M, Ye W, Corrado G, Naidich DP, Shetty S. End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography. Nat Med. 2019 Jun;25(6):954-961. doi: 10.1038/s41591-019-0447-x. Epub 2019 May 20.

  PMID: 31110349 BACKGROUND

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  PMID: 35398344 BACKGROUND

• Singhi AD, Koay EJ, Chari ST, Maitra A. Early Detection of Pancreatic Cancer: Opportunities and Challenges. Gastroenterology. 2019 May;156(7):2024-2040. doi: 10.1053/j.gastro.2019.01.259. Epub 2019 Feb 2.

  PMID: 30721664 BACKGROUND

• Chu LC, Park S, Kawamoto S, Wang Y, Zhou Y, Shen W, Zhu Z, Xia Y, Xie L, Liu F, Yu Q, Fouladi DF, Shayesteh S, Zinreich E, Graves JS, Horton KM, Yuille AL, Hruban RH, Kinzler KW, Vogelstein B, Fishman EK. Application of Deep Learning to Pancreatic Cancer Detection: Lessons Learned From Our Initial Experience. J Am Coll Radiol. 2019 Sep;16(9 Pt B):1338-1342. doi: 10.1016/j.jacr.2019.05.034. No abstract available.

  PMID: 31492412 BACKGROUND

• Gros L, Yip R, Zhu Y, Li P, Paksashvili N, Sun Q, Yankelevitz DF, Henschke CI. GI cancer mortality in participants in low dose CT screening for lung cancer with a focus on pancreatic cancer. Sci Rep. 2024 Dec 2;14(1):29851. doi: 10.1038/s41598-024-76322-z.

  PMID: 39617764 BACKGROUND

Biospecimen

Retention: SAMPLES WITH DNA

Participants will have the option to donate blood samples for biobanking. Approximately 20ml blood and 2ml serum will be collected. Additional samples collected for diagnostic purposes may be banked. If consented, biological samples (blood, tissue, saliva) will be used for identifying potential biomarkers from de-identified samples.

MeSH Terms

Conditions

Pancreatic Neoplasms; Disease

Condition Hierarchy (Ancestors)

Digestive System Neoplasms; Neoplasms by Site; Neoplasms; Endocrine Gland Neoplasms; Digestive System Diseases; Pancreatic Diseases; Endocrine System Diseases; Pathologic Processes; Pathological Conditions, Signs and Symptoms

Central Study Contacts

Wang Bei Lei, M.D.

CONTACT

13774238083; lilly_wang@126.com

Guo Shi Wei, M.D.

CONTACT

18621500666; gestwa@163.com

Study Design

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

Study Record Dates

• First Submitted: August 5, 2025
• First Posted: August 12, 2025
• Study Start: August 15, 2025
• Primary Completion (Estimated): December 30, 2030
• Study Completion (Estimated): December 30, 2032
• Last Updated: August 12, 2025

Record last verified: 2025-01

Data Sharing

• IPD Sharing: Will not share

Locations

CN (5)