Pancreatic Cystic Lesion (PCL)NCT07543263
An Artificial Intelligence System for Multimodal, Multi-class Diagnosis of Pancreatic Cystic Lesions Based on Endoscopic Ultrasonography
An observational study of Pancreatic Cystic Lesion (PCL), run by Qilu Hospital of Shandong University at 1 site in China.
Trial summary
The aim of this study is to develop and validate an artificial intelligence system named iEUS-PCL (intelligent endoscopic ultrasound system-pancreatic cystic lesions） for detecting and multimodal, multi-class diagnosing pancreatic cystic lesions (PCL) during endoscopic ultrasound (EUS) examination. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Pancreatic Cystic Lesion (PCL)
- What is being tested?
- iEUS-PCL(intelligent endoscopic ultrasound system- pancreatic cystic lesion)
- Type of treatment
- Medical device
- How many people
- 176 participants
- Where
- 1 siteChina
- Study dates
- 20 Apr 2026to 30 Jun 2028
- 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 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: Adult patients with suspected pancreatic cystic lesions undergoing EUS.
Who may be able to join
- Patients aged ≥18 years scheduled for EUS with suspected pancreatic cystic lesions based on clinical symptoms, medical history, laboratory tests or radiological examinations, and who agree to participate in the research and voluntarily sign the informed consent.
- Patients with no prior history of treatment for pancreatic lesions.
Who cannot join
- Patients with absolute contraindications to EUS examination.
- Pregnancy or lactating.
- Uncorrectable coagulopathy (PTT>50 seconds or INR>1.5) and/or uncorrectable thrombocytopenia(platelet count<50×109/L).
- Upper gastrointestinal obstruction.
- Patients who underwent surgical treatment or anatomical alterations of the pancreas due to lesions in other thoracic and/or abdominal organs, as well as patients with congenital anatomical abnormalities.
- Patients who have undergone biliary/pancreatic duct stent placement.
- Patients who refuse to sign the informed consent.
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
China1 site, 1 recruiting
- Recruiting
Qilu Hospital of Shandong University
Jinan, Shandong, 250012
Zhen Li, doctor, 86+18560086106, qilulizhen@sdu.edu.cn
About this study
The aim of this study is to develop and validate an artificial intelligence system named iEUS-PCL (intelligent endoscopic ultrasound system-pancreatic cystic lesions） for detecting and multimodal, multi-class diagnosing pancreatic cystic lesions (PCL) during endoscopic ultrasound (EUS) examination.
Read the detailed description
This multicenter, prospective cohort study aims to develop and validate a multimodal artificial intelligence system named iEUS-PCL for the detection and differential diagnosis of PCL. The model was developed based on retrospectively collected EUS images, EUS features, clinical data and radiological imaging features of patients who underwent EUS examination. The diagnostic performance of iEUS-PCL will be evaluated prospectively in real-time EUS videos and compared with endosonographers' performance.
- Conditions
- Pancreatic Cystic Lesion (PCL)
- Being tested
- iEUS-PCL(intelligent endoscopic ultrasound system- pancreatic cystic lesion)
- Keywords
- endoscopic ultrasound, pancreatic cystic lesion, machine learning
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
iEUS-PCL(intelligent endoscopic ultrasound system- pancreatic cystic lesion)Device
The iEUS-PCL will automatically detect pancreatic cystic lesions and integrate the patients' EUS images, EUS features, clinical data and radiological imaging features to perform three classification tasks:
- binary classification: benign/malignant lesions;
- binary classification: mucinous/non-mucinous lesions;
- four-category classification: intraductal papillary mucinous neoplasm/ mucinous cystic neoplasm/ serous cyst neoplasm/ pancreatic cyst.
| Study group | Role | What they receive |
|---|---|---|
| Patients undergoing EUS | Patients aged ≥18 years scheduled for EUS with suspected pancreatic cystic lesions based on clinical symptoms, medical history, laboratory tests or radiological examinations are eligible upon agreement to participate in the research and voluntary signing of the informed consent. Receives: Device: iEUS-PCL(intelligent endoscopic ultrasound system- pancreatic cystic lesion) |
What is being measured
The primary outcome is the main question the study is designed to answer.
The accuracy of iEUS-PCL for pancreatic cystic lesions
The primary outcome of the study is to evaluate the accuracy of the iEUS-PCL in identifying the pancreatic cystic lesions (benign/malignant; mucinous/non-mucinous; 4-category).
Measured: During procedure
The sensitivity of iEUS-PCL for pancreatic cystic lesions
The primary outcome of the study is to evaluate the sensitivity of the iEUS-PCL in identifying the pancreatic cystic lesions (benign/malignant; mucinous/non-mucinous; 4-category).
Measured: During procedure
The specificicy of iEUS-PCL for pancreatic cystic lesions
The primary outcome of the study is to evaluate the specificity of the iEUS-PCL in identifying the pancreatic cystic lesions (benign/malignant; mucinous/non-mucinous; 4-category).
Measured: During procedure
The postive predictive value of iEUS-PCL for pancreatic cystic lesions
The primary outcome of the study is to evaluate the postive predictive value of the iEUS-PCL in identifying the pancreatic cystic lesions (benign/malignant; mucinous/non-mucinous; 4-category).
Measured: During procedure
The negative predictive value of iEUS-PCL for pancreatic cystic lesions
The primary outcome of the study is to evaluate the negative predictive value of the iEUS-PCL in identifying the pancreatic cystic lesions (benign/malignant; mucinous/non-mucinous; 4-category).
Measured: During procedure
Secondary outcomes (5)
Comparison of the accuracy between iEUS-PCL and endosonographers
The secondary outcome of the study is to comparing the accuracy between iEUS-PCL and different-level endosonographers in identifying the pancreatic cystic lesions (benign/malignant; mucinous/non-mucinous; 4-category).
Measured: During procedure
Comparison of the sensitivity between iEUS-PCL and endosonographers
The secondary outcome of the study is to comparing the sensitivity between iEUS-PCL and different-level endosonographers in identifying in identifying the pancreatic cystic lesions (benign/malignant; mucinous/non-mucinous; 4-category).
Measured: During procedure
Comparison of the specificity between iEUS-PCL and endosonographers
The secondary outcome of the study is to comparing the specificity between iEUS-PCL and different-level endosonographers in identifying in identifying the pancreatic cystic lesions (benign/malignant; mucinous/non-mucinous; 4-category).
Measured: During procedure
Comparison of the postive predictive value between iEUS-PCL and endosonographers
The secondary outcome of the study is to comparing the postive predictive value between iEUS-PCL and different level endosonographers in identifying in identifying the pancreatic cystic lesions (benign/malignant; mucinous/non-mucinous; 4-category).
Measured: During procedure
Comparison of the negative predictive value between iEUS-PCL and endosonographers
The secondary outcome of the study is to comparing the negative predictive value between iEUS-PCL and different-level endosonographers in identifying in identifying the pancreatic cystic lesions (benign/malignant; mucinous/non-mucinous; 4-category).
Measured: During procedure
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted21 Apr 2026
- Study start20 Apr 2026
- Primary completion30 Jun 2028 (estimated)
- Study completion30 Jun 2028 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (20)Papers the sponsor has linked to this study.
- Other trials for Pancreatic Cystic Lesion (PCL)Studies of the same condition, open and under way.
- More trials run by Qilu Hospital of Shandong UniversityThe sponsor's other registered studies.
- 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
- 176 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Qilu Hospital of Shandong University
- Sponsor type
- University, hospital or other
- Collaborators
- Liaocheng People's Hospital, Taian City Central Hospital, Qilu Hospital of Shandong University (Qingdao), Binzhou People's Hospital, Shandong Provincial Hospital, The Affiliated Hospital of Qingdao University, Qianfoshan Hospital, Shengli Oilfield Hospital, Binzhou Medical University
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07543263
- Sponsor's protocol ID
- 2026-SDU-QILU-2
- Status last verified
- Oct 2026
- First submitted
- 15 Apr 2026
- Last updated
- 5 Nov 2026
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
Goyal H, Sherazi SAA, Gupta S, Perisetti A, Achebe I, Ali A, Tharian B, Thosani N, Sharma NR. Application of artificial intelligence in diagnosis of pancreatic malignancies by endoscopic ultrasound: a systemic review. Ther Adv Gastroenterol. 2022 Apr 29;15:17562848221093873. doi: 10.1177/17562848221093873. eCollection 2022.
PubMed 35509425Qin X, Ran T, Chen Y, Zhang Y, Wang D, Zhou C, Zou D. Artificial Intelligence in Endoscopic Ultrasonography-Guided Fine-Needle Aspiration/Biopsy (EUS-FNA/B) for Solid Pancreatic Lesions: Opportunities and Challenges. Diagnostics (Basel). 2023 Sep 26;13(19):3054. doi: 10.3390/diagnostics13193054.
PubMed 37835797Tian G, Xu D, He Y, Chai W, Deng Z, Cheng C, Jin X, Wei G, Zhao Q, Jiang T. Deep learning for real-time auxiliary diagnosis of pancreatic cancer in endoscopic ultrasonography. Front Oncol. 2022 Oct 7;12:973652. doi: 10.3389/fonc.2022.973652. eCollection 2022.
PubMed 36276094Kloth C, Haggenmuller B, Beck A, Wagner M, Kornmann M, Steinacker JP, Steinacker-Stanescu N, Vogele D, Beer M, Juchems MS, Schmidt SA. Diagnostic, Structured Classification and Therapeutic Approach in Cystic Pancreatic Lesions: Systematic Findings with Regard to the European Guidelines. Diagnostics (Basel). 2023 Jan 26;13(3):454. doi: 10.3390/diagnostics13030454.
PubMed 36766560Vilela A, Quingalahua E, Vargas A, Hawa F, Shannon C, Carpenter ES, Shi J, Krishna SG, Lee UJ, Chalhoub JM, Machicado JD. Global Prevalence of Pancreatic Cystic Lesions in the General Population on Magnetic Resonance Imaging: A Systematic Review and Meta-analysis. Clin Gastroenterol Hepatol. 2024 Sep;22(9):1798-1809.e6. doi: 10.1016/j.cgh.2024.02.018. Epub 2024 Feb 28.
PubMed 38423346Elta GH, Enestvedt BK, Sauer BG, Lennon AM. ACG Clinical Guideline: Diagnosis and Management of Pancreatic Cysts. Am J Gastroenterol. 2018 Apr;113(4):464-479. doi: 10.1038/ajg.2018.14. Epub 2018 Feb 27.
PubMed 29485131European Study Group on Cystic Tumours of the Pancreas. European evidence-based guidelines on pancreatic cystic neoplasms. Gut. 2018 May;67(5):789-804. doi: 10.1136/gutjnl-2018-316027. Epub 2018 Mar 24.
PubMed 29574408Rangwani S, Ardeshna DR, Rodgers B, Melnychuk J, Turner R, Culp S, Chao WL, Krishna SG. Application of Artificial Intelligence in the Management of Pancreatic Cystic Lesions. Biomimetics (Basel). 2022 Jun 14;7(2):79. doi: 10.3390/biomimetics7020079.
PubMed 35735595Oh S, Kim YJ, Park YT, Kim KG. Automatic Pancreatic Cyst Lesion Segmentation on EUS Images Using a Deep-Learning Approach. Sensors (Basel). 2021 Dec 30;22(1):245. doi: 10.3390/s22010245.
PubMed 35009788Jiang J, Chao WL, Cao T, Culp S, Napoleon B, El-Dika S, Machicado JD, Pannala R, Mok S, Luthra AK, Akshintala VS, Muniraj T, Krishna SG. Improving Pancreatic Cyst Management: Artificial Intelligence-Powered Prediction of Advanced Neoplasms through Endoscopic Ultrasound-Guided Confocal Endomicroscopy. Biomimetics (Basel). 2023 Oct 19;8(6):496. doi: 10.3390/biomimetics8060496.
PubMed 37887627Hwang J, Kim YK, Min JH, Jeong WK, Hong SS, Kim HJ. Comparison between MRI with MR cholangiopancreatography and endoscopic ultrasonography for differentiating malignant from benign mucinous neoplasms of the pancreas. Eur Radiol. 2018 Jan;28(1):179-187. doi: 10.1007/s00330-017-4926-5. Epub 2017 Aug 4.
PubMed 28779397Huang W, Xu Y, Li Z, Li J, Chen Q, Huang Q, Wu Y, Chen H. Enhancing noninvasive pancreatic cystic neoplasm diagnosis with multimodal machine learning. Sci Rep. 2025 May 12;15(1):16398. doi: 10.1038/s41598-025-01502-4.
PubMed 40355497Gheorghiu MI, Seicean A, Pojoga C, Hagiu C, Seicean R, Sparchez Z. Contrast-enhanced guided endoscopic ultrasound procedures. World J Gastroenterol. 2024 May 7;30(17):2311-2320. doi: 10.3748/wjg.v30.i17.2311.
PubMed 38813054Vilas-Boas F, Ribeiro T, Afonso J, Cardoso H, Lopes S, Moutinho-Ribeiro P, Ferreira J, Mascarenhas-Saraiva M, Macedo G. Deep Learning for Automatic Differentiation of Mucinous versus Non-Mucinous Pancreatic Cystic Lesions: A Pilot Study. Diagnostics (Basel). 2022 Aug 24;12(9):2041. doi: 10.3390/diagnostics12092041.
PubMed 36140443Nguon LS, Seo K, Lim JH, Song TJ, Cho SH, Park JS, Park S. Deep Learning-Based Differentiation between Mucinous Cystic Neoplasm and Serous Cystic Neoplasm in the Pancreas Using Endoscopic Ultrasonography. Diagnostics (Basel). 2021 Jun 8;11(6):1052. doi: 10.3390/diagnostics11061052.
PubMed 34201066
Study at a glance
From the registry record, last updated by the sponsor on 5 Nov 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- An Artificial Intelligence System for Multimodal, Multi-class Diagnosis of Pancreatic Cystic Lesions Based on Endoscopic Ultrasonography
- ClinicalTrials.gov ID
- NCT07543263
- Acronym / sponsor ID
- 2026-SDU-QILU-2
- Conditions
- Pancreatic Cystic Lesion (PCL)
- Keywords
- endoscopic ultrasound, pancreatic cystic lesion, machine learning
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 176 participants (estimated)
- Lead sponsor
- Qilu Hospital of Shandong UniversityUniversity, hospital or other
- Collaborators
- Liaocheng People's Hospital, Taian City Central Hospital, Qilu Hospital of Shandong University (Qingdao), Binzhou People's Hospital, Shandong Provincial Hospital, The Affiliated Hospital of Qingdao University, Qianfoshan Hospital, Shengli Oilfield Hospital, Binzhou Medical University
- Study start
- 20 Apr 2026 (actual)
- Primary completion
- 30 Jun 2028 (estimated)
- Study completion
- 30 Jun 2028 (estimated)
- First posted
- 21 Apr 2026 (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.
The aim of this study is to develop and validate an artificial intelligence system named iEUS-PCL (intelligent endoscopic ultrasound system-pancreatic cystic lesions） for detecting and multimodal, multi-class diagnosing pancreatic cystic lesions (PCL) during endoscopic ultrasound (EUS) examination.
Detailed description
This multicenter, prospective cohort study aims to develop and validate a multimodal artificial intelligence system named iEUS-PCL for the detection and differential diagnosis of PCL. The model was developed based on retrospectively collected EUS images, EUS features, clinical data and radiological imaging features of patients who underwent EUS examination. The diagnostic performance of iEUS-PCL will be evaluated prospectively in real-time EUS videos and compared with endosonographers' performance.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Patients undergoing EUS | – | Device: iEUS-PCL(intelligent endoscopic ultrasound system- pancreatic cystic lesion)Patients aged ≥18 years scheduled for EUS with suspected pancreatic cystic lesions based on clinical symptoms, medical history, laboratory tests or radiological examinations are eligible upon agreement to participate in the research and voluntary signing of the informed consent. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Adult patients with suspected pancreatic cystic lesions undergoing EUS.
Key inclusion criteria (2 listed)
- Patients aged ≥18 years scheduled for EUS with suspected pancreatic cystic lesions based on clinical symptoms, medical history, laboratory tests or radiological examinations, and who agree to participate in the research and voluntarily sign the informed consent.
- Patients with no prior history of treatment for pancreatic lesions.
Key exclusion criteria (7 listed)
- Patients with absolute contraindications to EUS examination.
- Pregnancy or lactating.
- Uncorrectable coagulopathy (PTT>50 seconds or INR>1.5) and/or uncorrectable thrombocytopenia(platelet count<50×109/L).
- Upper gastrointestinal obstruction.
- Patients who underwent surgical treatment or anatomical alterations of the pancreas due to lesions in other thoracic and/or abdominal organs, as well as patients with congenital anatomical abnormalities.
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 |
|---|---|---|
| iEUS-PCL(intelligent endoscopic ultrasound system- pancreatic cystic lesion)The iEUS-PCL will automatically detect pancreatic cystic lesions and integrate the patients' EUS images, EUS features, clinical data and radiological imaging features to perform three classification tasks: 1. binary classification: benign/malignant lesions; 2. binary classification: mucinous/non-mucinous lesions; 3. four-category classification: intraductal papillary mucinous neoplasm/ mucinous cystic neoplasm/ serous cyst neoplasm/ pancreatic cyst. | Device | Patients undergoing EUS |
Primary outcome
The accuracy of iEUS-PCL for pancreatic cystic lesions
Time frame: During procedure
The primary outcome of the study is to evaluate the accuracy of the iEUS-PCL in identifying the pancreatic cystic lesions (benign/malignant; mucinous/non-mucinous; 4-category).
The sensitivity of iEUS-PCL for pancreatic cystic lesions
Time frame: During procedure
The primary outcome of the study is to evaluate the sensitivity of the iEUS-PCL in identifying the pancreatic cystic lesions (benign/malignant; mucinous/non-mucinous; 4-category).
The specificicy of iEUS-PCL for pancreatic cystic lesions
Time frame: During procedure
The primary outcome of the study is to evaluate the specificity of the iEUS-PCL in identifying the pancreatic cystic lesions (benign/malignant; mucinous/non-mucinous; 4-category).
The postive predictive value of iEUS-PCL for pancreatic cystic lesions
Time frame: During procedure
The primary outcome of the study is to evaluate the postive predictive value of the iEUS-PCL in identifying the pancreatic cystic lesions (benign/malignant; mucinous/non-mucinous; 4-category).
The negative predictive value of iEUS-PCL for pancreatic cystic lesions
Time frame: During procedure
The primary outcome of the study is to evaluate the negative predictive value of the iEUS-PCL in identifying the pancreatic cystic lesions (benign/malignant; mucinous/non-mucinous; 4-category).
5 secondary outcomes
Comparison of the accuracy between iEUS-PCL and endosonographers
Time frame: During procedure
Comparison of the sensitivity between iEUS-PCL and endosonographers
Time frame: During procedure
Comparison of the specificity between iEUS-PCL and endosonographers
Time frame: During procedure
Comparison of the postive predictive value between iEUS-PCL and endosonographers
Time frame: During procedure
Comparison of the negative predictive value between iEUS-PCL and endosonographers
Time frame: During procedure
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 5 Nov 2026.
- Background: PubMed 35509425Goyal H, Sherazi SAA, Gupta S, Perisetti A, Achebe I, Ali A, Tharian B, Thosani N, Sharma NR. Application of artificial intelligence in diagnosis of pancreatic malignancies by endoscopic ultrasound: a systemic review. Ther Adv Gastroenterol. 2022 Apr 29;15:17562848221093873. doi: 10.1177/17562848221093873. eCollection 2022.
- Background: PubMed 37835797Qin X, Ran T, Chen Y, Zhang Y, Wang D, Zhou C, Zou D. Artificial Intelligence in Endoscopic Ultrasonography-Guided Fine-Needle Aspiration/Biopsy (EUS-FNA/B) for Solid Pancreatic Lesions: Opportunities and Challenges. Diagnostics (Basel). 2023 Sep 26;13(19):3054. doi: 10.3390/diagnostics13193054.
- Background: PubMed 36276094Tian G, Xu D, He Y, Chai W, Deng Z, Cheng C, Jin X, Wei G, Zhao Q, Jiang T. Deep learning for real-time auxiliary diagnosis of pancreatic cancer in endoscopic ultrasonography. Front Oncol. 2022 Oct 7;12:973652. doi: 10.3389/fonc.2022.973652. eCollection 2022.
- Background: PubMed 36766560Kloth C, Haggenmuller B, Beck A, Wagner M, Kornmann M, Steinacker JP, Steinacker-Stanescu N, Vogele D, Beer M, Juchems MS, Schmidt SA. Diagnostic, Structured Classification and Therapeutic Approach in Cystic Pancreatic Lesions: Systematic Findings with Regard to the European Guidelines. Diagnostics (Basel). 2023 Jan 26;13(3):454. doi: 10.3390/diagnostics13030454.
- Background: PubMed 38423346Vilela A, Quingalahua E, Vargas A, Hawa F, Shannon C, Carpenter ES, Shi J, Krishna SG, Lee UJ, Chalhoub JM, Machicado JD. Global Prevalence of Pancreatic Cystic Lesions in the General Population on Magnetic Resonance Imaging: A Systematic Review and Meta-analysis. Clin Gastroenterol Hepatol. 2024 Sep;22(9):1798-1809.e6. doi: 10.1016/j.cgh.2024.02.018. Epub 2024 Feb 28.
- Background: PubMed 29485131Elta GH, Enestvedt BK, Sauer BG, Lennon AM. ACG Clinical Guideline: Diagnosis and Management of Pancreatic Cysts. Am J Gastroenterol. 2018 Apr;113(4):464-479. doi: 10.1038/ajg.2018.14. Epub 2018 Feb 27.
- Background: PubMed 29574408European Study Group on Cystic Tumours of the Pancreas. European evidence-based guidelines on pancreatic cystic neoplasms. Gut. 2018 May;67(5):789-804. doi: 10.1136/gutjnl-2018-316027. Epub 2018 Mar 24.
- Background: PubMed 35735595Rangwani S, Ardeshna DR, Rodgers B, Melnychuk J, Turner R, Culp S, Chao WL, Krishna SG. Application of Artificial Intelligence in the Management of Pancreatic Cystic Lesions. Biomimetics (Basel). 2022 Jun 14;7(2):79. doi: 10.3390/biomimetics7020079.
- Background: PubMed 35009788Oh S, Kim YJ, Park YT, Kim KG. Automatic Pancreatic Cyst Lesion Segmentation on EUS Images Using a Deep-Learning Approach. Sensors (Basel). 2021 Dec 30;22(1):245. doi: 10.3390/s22010245.
- Background: PubMed 37887627Jiang J, Chao WL, Cao T, Culp S, Napoleon B, El-Dika S, Machicado JD, Pannala R, Mok S, Luthra AK, Akshintala VS, Muniraj T, Krishna SG. Improving Pancreatic Cyst Management: Artificial Intelligence-Powered Prediction of Advanced Neoplasms through Endoscopic Ultrasound-Guided Confocal Endomicroscopy. Biomimetics (Basel). 2023 Oct 19;8(6):496. doi: 10.3390/biomimetics8060496.
- Background: PubMed 28779397Hwang J, Kim YK, Min JH, Jeong WK, Hong SS, Kim HJ. Comparison between MRI with MR cholangiopancreatography and endoscopic ultrasonography for differentiating malignant from benign mucinous neoplasms of the pancreas. Eur Radiol. 2018 Jan;28(1):179-187. doi: 10.1007/s00330-017-4926-5. Epub 2017 Aug 4.
- Background: PubMed 40355497Huang W, Xu Y, Li Z, Li J, Chen Q, Huang Q, Wu Y, Chen H. Enhancing noninvasive pancreatic cystic neoplasm diagnosis with multimodal machine learning. Sci Rep. 2025 May 12;15(1):16398. doi: 10.1038/s41598-025-01502-4.
- Individual participant dataSharing planned: No
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.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Pancreatic Cystic Lesion (PCL), run by Qilu Hospital of Shandong University at 1 site 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 2 inclusion and 7 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 5 Nov 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site 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 Zhen Li (86+18560086106, qilulizhen@sdu.edu.cn), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
The accuracy of iEUS-PCL for pancreatic cystic lesions (time frame: During procedure). The study lists 5 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 NCT07543263, which also lists 20 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 5 Nov 2026.