Pancreatic CancerAI-LDCT-PCNCT07117045
Artificial Intelligence-powered Low-Dose Computed Tomography for Screening of Pancreatic Cancer
An observational study of Pancreatic Cancer and Intraductal Papillary Mucinous Neoplasm, run by Changhai Hospital at 5 sites in China.
Trial 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. Read more about this study
- Who is it for?
- Ages 50 years and olderHealthy volunteers can take part
- Conditions the study is for
- Pancreatic Cancer, Intraductal Papillary Mucinous Neoplasm, High-grade Pancreatic Intraepithelial Neoplasia
- What is being tested?
- Diagnostic Evaluation for Positive AI Findings
- How many people
- 400,000 participants
- Where
- 5 sitesChina
- Study dates
- 15 Aug 2025to 30 Dec 2032
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 50 years and older
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
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.
Who may be able to join
- Age 50 years and above.
- Voluntary signing of informed consent.
- Completion of LDCT examination.
Who cannot join
- 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.
Trial locations and contacts
5 sites in 1 country. Contact the study team or a site near you to ask about taking part.
5 sites in 1 country
China5 sites, 5 recruiting
- Recruiting
Jiaxing University Affiliated Second Hospital
Jiaxing, Zhejiang, 314000
Shen Yi Jue, M.D., 13605835645, dr.syj@163.com
- Recruiting
Ningbo University Affiliated People's Hospital
Ningbo, Zhejiang, 315100
Zhu Ke Lei, M.D., 13566636272, dr.zkl@163.com
- Recruiting
Meinian Onehealth Healthcare Holdings Co., Ltd
Shanghai, Shanghai Municipality, 200072
Qin Jianzeng Dr, M.D., 13602746909, qinjianzeng@126.com
- Recruiting
Ruici Medical Examination Institution
Shanghai, Shanghai Municipality, 200126
Wang Liucheng Dr, M.D., 18601790221, wangliucheng@126.com
- Recruiting
Changhai Hospital
Shanghai, Shanghai Municipality, 200433
Wang Bei Lei, M.D., 13774238083, lilly_wang@126.com
Guo Shi Wei, M.D., 18621500666, gestwa@163.com
About this study
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.
Read the detailed description
Pancreatic ductal adenocarcinoma (PDAC) is an extremely aggressive cancer with a dismal 5-year survival rate of just 13%. The key to improving outcomes lies in early detection, as patients diagnosed at an early stage (IA) can achieve an 80% 5-year survival. However, current screening methods are limited, focusing only on high-risk populations and lacking effectiveness for the general public due to the cancer's relatively low incidence and high false-positive risks.
Contrast-enhanced CT (CE-CT), the primary imaging modality, faces barriers for widespread implementation due to its invasiveness, high costs, and need for contrast agents. In this context, low-dose CT (LDCT) emerges as a promising alternative, having demonstrated success in lung cancer screening by reducing radiation exposure. Retrospective analysis revealed that one-third of pancreatic abnormalities were missed during routine LDCT interpretations, suggesting the untapped potential of LDCT-based pancreatic lesion screening.
Breakthroughs in AI have transformed medical imaging. Our PANDA (pancreatic cancer detection with artifcial intelligence) system excels at pancreatic cancer detection, utilizing innovative registration techniques and a cascaded deep learning framework (UNet+Max-Deeplab) for comprehensive lesion analysis. Validated across 10 centers (6,239 patients), PANDA outperformed radiologists. Real-world testing (20,530 cases) demonstrated remarkable accuracy: 92.9% sensitivity and 99.9% specificity, maintaining 92.2% sensitivity even for small T1 tumors. On LDCT, PANDA achieved 0.979 AUC without protocol modifications, confirming "LDCT+AI" as a viable screening approach.
China's health check-up environment presents three key advantages: First, LDCT delivers just 1/4-1/5 the radiation of standard abdominal CT, staying within ICRP safety guidelines (<3mSv). Second, LDCT offers superior cost-effectiveness compared to CE-CT by eliminating contrast agent expenses. Third, China's extensive annual health check-up infrastructure provides an unparalleled foundation for widespread implementation.
In the study, we will conduct a prospective, multicenter, observational cohort design targeting a health check-up population, utilizing the PANDA system to enhance LDCT for pancreatic cancer screening. Initially, PANDA analyzes the LDCT images of participants and provides interpretation results. Subsequently, a multidisciplinary team (MDT) will re-evaluate the cases with positive AI findings (including PDAC, pancreatic precursor lesions and benign lesion) and determine whether to recall the individuals: (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). The primary outcome measures include pancreatic cancer detection rate, positive predictive value, consensus rate, and recall rate. Secondary outcome measures include the proportion of early-stage pancreatic cancers and resectable tumors. Safety indicators include the false positive rate, the proportion of unnecessary invasive procedures, and the proportion of unnecessary surgeries.
This study aims to evaluate the effectiveness and safety of AI-powered LDCT in detecting pancreatic cancer within a health check-up population, offering a practical solution to improve public health and early diagnosis for pancreatic cancer.
- Conditions
- Pancreatic Cancer, Intraductal Papillary Mucinous Neoplasm, High-grade Pancreatic Intraepithelial Neoplasia, PDAC - Pancreatic Ductal Adenocarcinoma, Mucinous Cystic Neoplasm
- Being tested
- Diagnostic Evaluation for Positive AI Findings
- Keywords
- Screening, Early Diagnosis, Pancreatic Cancer, Artificial Intelligence, Computed Tomography
- Medical subject headings
- Pancreatic Neoplasms, Disease
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Diagnostic Evaluation for Positive AI FindingsDiagnostic 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).
| Study group | Role | What they receive |
|---|---|---|
| AI-powered LDCT (LDCT+AI) | Participants will undergo annual screening with the LDCT+AI system. Receives: Diagnostic Test: Diagnostic Evaluation for Positive AI Findings |
What is being measured
The primary outcome is the main question the study is designed to answer.
Pancreatic cancer detection rate
The proportion of individuals with abnormal AI assessment confirmed as pancreatic cancer or precancerous lesions among the total screened population
Measured: 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
Measured: 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.
Measured: 2 years
Recall rate
The proportion of individuals actually recalled among the total screened population.
Measured: 2 years
Secondary outcomes (2)
Early-stage pancreatic cancer proportion
The proportion of individuals with abnormal AI assessment confirmed as early-stage pancreatic cancer among the total number of confirmed pancreatic cancer cases.
Measured: 2 years
Resectable pancreatic cancer proportion
The proportion of individuals with surgically resectable pancreatic cancer among the total number of confirmed pancreatic cancer cases.
Measured: 2 years
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted12 Aug 2025
- Study start15 Aug 2025 (estimated)
- Primary completion30 Dec 2030 (estimated)
- Study completion30 Dec 2032 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (9)Papers the sponsor has linked to this study.
- Other trials for Pancreatic CancerStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 400,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Changhai Hospital
- Sponsor type
- University, hospital or other
- Collaborators
- The Affiliated People's Hospital of Ningbo University, The Second Affiliated Hospital of Jiaxing University, Meinian Onehealth Healthcare Holdings Co., Ltd, Ruici Medical Examination Institution
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07117045
- Sponsor's protocol ID
- AI-LDCT-PC
- Other IDs
- 202401063, 202440208, 2025AAA031146
- Acronym
- AI-LDCT-PC
- Status last verified
- Jan 2025
- First submitted
- 5 Aug 2025
- Last updated
- 12 Aug 2025
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
Cao 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.
PubMed 37985692Mizrahi 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.
PubMed 32593337Pereira 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.
PubMed 32135127Attiyeh 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.
PubMed 29380093Ardila 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.
PubMed 31110349Wood 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.
PubMed 35398344Singhi 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.
PubMed 30721664Chu 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.
PubMed 31492412Gros 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.
PubMed 39617764
Study at a glance
From the registry record, last updated by the sponsor on 12 Aug 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Artificial Intelligence-powered Low-Dose Computed Tomography for Screening of Pancreatic Cancer
- ClinicalTrials.gov ID
- NCT07117045
- Acronym / sponsor ID
- AI-LDCT-PC / AI-LDCT-PC
- 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
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 400,000 participants (estimated)
- Lead sponsor
- Changhai HospitalUniversity, hospital or other
- Collaborators
- The Affiliated People's Hospital of Ningbo University, The Second Affiliated Hospital of Jiaxing University, Meinian Onehealth Healthcare Holdings Co., Ltd, Ruici Medical Examination Institution
- Study start
- 15 Aug 2025 (estimated)
- Primary completion
- 30 Dec 2030 (estimated)
- Study completion
- 30 Dec 2032 (estimated)
- First posted
- 12 Aug 2025 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Detailed description
Pancreatic ductal adenocarcinoma (PDAC) is an extremely aggressive cancer with a dismal 5-year survival rate of just 13%. The key to improving outcomes lies in early detection, as patients diagnosed at an early stage (IA) can achieve an 80% 5-year survival. However, current screening methods are limited, focusing only on high-risk populations and lacking effectiveness for the general public due to the cancer's relatively low incidence and high false-positive risks.
Contrast-enhanced CT (CE-CT), the primary imaging modality, faces barriers for widespread implementation due to its invasiveness, high costs, and need for contrast agents. In this context, low-dose CT (LDCT) emerges as a promising alternative, having demonstrated success in lung cancer screening by reducing radiation exposure. Retrospective analysis revealed that one-third of pancreatic abnormalities were missed during routine LDCT interpretations, suggesting the untapped potential of LDCT-based pancreatic lesion screening.
Breakthroughs in AI have transformed medical imaging. Our PANDA (pancreatic cancer detection with artifcial intelligence) system excels at pancreatic cancer detection, utilizing innovative registration techniques and a cascaded deep learning framework (UNet+Max-Deeplab) for comprehensive lesion analysis. Validated across 10 centers (6,239 patients), PANDA outperformed radiologists. Real-world testing (20,530 cases) demonstrated remarkable accuracy: 92.9% sensitivity and 99.9% specificity, maintaining 92.2% sensitivity even for small T1 tumors. On LDCT, PANDA achieved 0.979 AUC without protocol modifications, confirming "LDCT+AI" as a viable screening approach.
China's health check-up environment presents three key advantages: First, LDCT delivers just 1/4-1/5 the radiation of standard abdominal CT, staying within ICRP safety guidelines (<3mSv). Second, LDCT offers superior cost-effectiveness compared to CE-CT by eliminating contrast agent expenses. Third, China's extensive annual health check-up infrastructure provides an unparalleled foundation for widespread implementation.
In the study, we will conduct a prospective, multicenter, observational cohort design targeting a health check-up population, utilizing the PANDA system to enhance LDCT for pancreatic cancer screening. Initially, PANDA analyzes the LDCT images of participants and provides interpretation results. Subsequently, a multidisciplinary team (MDT) will re-evaluate the cases with positive AI findings (including PDAC, pancreatic precursor lesions and benign lesion) and determine whether to recall the individuals: (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). The primary outcome measures include pancreatic cancer detection rate, positive predictive value, consensus rate, and recall rate. Secondary outcome measures include the proportion of early-stage pancreatic cancers and resectable tumors. Safety indicators include the false positive rate, the proportion of unnecessary invasive procedures, and the proportion of unnecessary surgeries.
This study aims to evaluate the effectiveness and safety of AI-powered LDCT in detecting pancreatic cancer within a health check-up population, offering a practical solution to improve public health and early diagnosis for pancreatic cancer.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| AI-powered LDCT (LDCT+AI) | – | Diagnostic Test: Diagnostic Evaluation for Positive AI FindingsParticipants will undergo annual screening with the LDCT+AI system. |
Who may qualify
- Ages
- 50 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- 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.
Key inclusion criteria (3 listed)
- Age 50 years and above.
- Voluntary signing of informed consent.
- Completion of LDCT examination.
Key exclusion criteria (4 listed)
- 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.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Diagnostic Evaluation for Positive AI FindingsMDT 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). | Diagnostic test | AI-powered LDCT (LDCT+AI) |
Primary outcome
Pancreatic cancer detection rate
Time frame: 2 years
The proportion of individuals with abnormal AI assessment confirmed as pancreatic cancer or precancerous lesions among the total screened population
Positive predictive value
Time frame: 2 years
The proportion of individuals with abnormal AI assessment confirmed as pancreatic cancer or precancerous lesions among all individuals with abnormal AI assessment
Consensus rate
Time frame: 2 years
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.
Recall rate
Time frame: 2 years
The proportion of individuals actually recalled among the total screened population.
2 secondary outcomes
Early-stage pancreatic cancer proportion
Time frame: 2 years
Resectable pancreatic cancer proportion
Time frame: 2 years
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 12 Aug 2025.
- Background: PubMed 37985692Cao 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.
- Background: PubMed 32593337Mizrahi 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.
- Background: PubMed 32135127Pereira 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.
- Background: PubMed 29380093Attiyeh 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.
- Background: PubMed 31110349Ardila 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.
- Background: PubMed 35398344Wood 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.
- Background: PubMed 30721664Singhi 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.
- Background: PubMed 31492412Chu 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.
- Background: PubMed 39617764Gros 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.
- Individual participant dataSharing planned: No
Related trials for Pancreatic Cancer
RecruitingPhase 3
HRS 4642 Injection Combined With AG Versus Placebo Combined With AG Therapy in First-Line Advanced or Metastatic Pancreatic Cancer
KRAS G12D-Mutant Advanced or Metastatic Pancreatic Cancer in the First-line Setting
RecruitingObservational study
Obtaining Solid Tumor Tissue From People Having Biopsy or Surgery for Certain Types of Cancer
Colorectal Neoplasms, Gastric Neoplasms
RecruitingPhase 1
A Study to Learn About Study Medicine ALTA3263 in Adults With Advanced Solid Tumors With KRAS Mutations
Cancer, PDAC - Pancreatic Ductal Adenocarcinoma
RecruitingObservational study
Solid Tumor Analysis for HLA Loss of Heterozygosity (LOH) and Apheresis for CAR T- Cell Manufacturing
Solid Tumor, Adult, Colorectal Cancer
Active, not recruitingPhase 3
Testing Cabozantinib in Patients With Advanced Pancreatic Neuroendocrine and Carcinoid Tumors
Functioning Pancreatic Neuroendocrine Tumor, Intermediate Grade Lung Neuroendocrine Neoplasm
Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
5 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Pancreatic Cancer and Intraductal Papillary Mucinous Neoplasm, run by Changhai Hospital at 5 sites in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 4 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: looking for participants now, as of the sponsor's last update on 12 Aug 2025. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 5 sites in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Wang Bei Lei, M.D. (13774238083, lilly_wang@126.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Pancreatic cancer detection rate (time frame: 2 years). The study lists 4 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT07117045, which also lists 9 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 12 Aug 2025.