Clinical trial · Observational
Artificial Intelligence-powered Low-Dose Computed Tomography for Screening of Pancreatic Cancer
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 8, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260908-000001
Summary
Brief summary (as posted)
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
Conditions (5)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| High-grade Pancreatic Intraepithelial Neoplasia | High Grade Pancreatic Intraepithelial Neoplasia | ONTOLOGY_EXACT | 0.98 |
| Intraductal Papillary Mucinous Neoplasm | Pancreatic Intraductal Papillary Mucinous Neoplasm | ALIAS | 0.90 |
| Mucinous Cystic Neoplasm | — | UNRESOLVED | — |
| Pancreatic Cancer | Malignant Pancreatic Neoplasm | CURATED_EXACT | 0.92 |
| PDAC - Pancreatic Ductal Adenocarcinoma | — | UNRESOLVED | — |
Interventions
Interventions (1)
| Intervention | Type | Mapped drug | Match |
|---|---|---|---|
| Diagnostic Evaluation for Positive AI Findings | Diagnostic Test | — | UNRESOLVED |
Design
Arms and outcomes
Arms (1)
- label
- AI-powered LDCT (LDCT+AI)
- description
- Participants will undergo annual screening with the LDCT+AI system.
- interventionNames
- Diagnostic Test: Diagnostic Evaluation for Positive AI Findings
Primary outcomes (4)
- measure
- Pancreatic cancer detection rate
- timeFrame
- 2 years
- description
- The proportion of individuals with abnormal AI assessment confirmed as pancreatic cancer or precancerous lesions among the total screened population
- measure
- Positive predictive value
- timeFrame
- 2 years
- description
- The proportion of individuals with abnormal AI assessment confirmed as pancreatic cancer or precancerous lesions among all individuals with abnormal AI assessment
Eligibility
Eligibility (as posted)
- Sex
- All
- Minimum age
- 50 Years
Show eligibility criteria text
Inclusion Criteria: 1. Age 50 years and above. 2. Voluntary signing of informed consent. 3. Completion of LDCT examination. Exclusion Criteria: 1. Previous history of pancreatic cancer. 2. Abdominal inflammation or diagnosis of acute pancreatitis within 6 months. 3. Poor image quality due to ascites, pancreatic trauma, thoracic/abdominal surgery, radiotherapy or chemotherapy. 4. Research subjects unable to complete follow-up due to physical or other reasons.
References
Publications (9)
- BACKGROUNDCao K, Xia Y, Yao J, Han X, Lambert L, Zhang T, Tang W, Jin G, Jiang H, Fang X, Nogues I, Li X, Guo W, Wang Y, Fang W, Qiu M, Hou Y, Kovarnik T, Vocka M, Lu Y, Chen Y, Chen X, Liu Z, Zhou J, Xie C, Zhang R, Lu H, Hager GD, Yuille AL, Lu L, Shao C, Shi Y, Zhang Q, Liang T, Zhang L, Lu J. Large-scale pancreatic cancer detection via non-contrast CT and deep learning. Nat Med. 2023 Dec;29(12):3033-3043. doi: 10.1038/s41591-023-02640-w. Epub 2023 Nov 20. PMID 37985692
- BACKGROUNDMizrahi 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
- BACKGROUNDPereira 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
- BACKGROUNDAttiyeh 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
- BACKGROUNDArdila 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
- BACKGROUNDWood LD, Canto MI, Jaffee EM, Simeone DM. Pancreatic Cancer: Pathogenesis, Screening, Diagnosis, and Treatment. Gastroenterology. 2022 Aug;163(2):386-402.e1. doi: 10.1053/j.gastro.2022.03.056. Epub 2022 Apr 7.