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.

Recruiting

Looking for participants now. Contact the study team or a site below to ask about taking part.

Sites and contacts

Official record

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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.

Zhen Li contact

86+18560086106

qilulizhen@sdu.edu.cn

China1 site, 1 recruiting

Qilu Hospital of Shandong University
Jinan, Shandong, 250012
Zhen Li, doctor, 86+18560086106, qilulizhen@sdu.edu.cn
Recruiting

Other trials in China

About this study

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:
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.

Study groups in NCT07543263
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.

20 Apr 202630 Jun 2028

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.
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)

IDs, dates and publications

ClinicalTrials.gov ID: NCT07543263
Sponsor's protocol ID: 2026-SDU-QILU-2
Status last verified: Sep 2026
First submitted: 15 Apr 2026
Last updated: 3 Sep 2026
Results posted: No
FDA-regulated drug: No
FDA-regulated device: No
Individual data sharing: No

Publications

Wu HL, Yao LW, Shi HY, Wu LL, Li X, Zhang CX, Chen BR, Zhang J, Tan W, Cui N, Zhou W, Zhang JX, Xiao B, Gong RR, Ding Z, Yu HG. Validation of a real-time biliopancreatic endoscopic ultrasonography analytical device in China: a prospective, single-centre, randomised, controlled trial. Lancet Digit Health. 2023 Nov;5(11):e812-e820. doi: 10.1016/S2589-7500(23)00160-7. Epub 2023 Sep 27.
PubMed 37775472
Tian S, Shi H, Chen W, Li S, Han C, Du F, Wang W, Wen H, Lei Y, Deng L, Tang J, Zhang J, Lin J, Shi L, Ning B, Zhao K, Miao J, Wang G, Hou H, Huang X, Kong W, Jin X, Ding Z, Lin R. Artificial intelligence-based diagnosis of standard endoscopic ultrasonography scanning sites in the biliopancreatic system: a multicenter retrospective study. Int J Surg. 2024 Mar 1;110(3):1637-1644. doi: 10.1097/JS9.0000000000000995.
PubMed 38079604
Lipkova J, Chen RJ, Chen B, Lu MY, Barbieri M, Shao D, Vaidya AJ, Chen C, Zhuang L, Williamson DFK, Shaban M, Chen TY, Mahmood F. Artificial intelligence for multimodal data integration in oncology. Cancer Cell. 2022 Oct 10;40(10):1095-1110. doi: 10.1016/j.ccell.2022.09.012.
PubMed 36220072
Schulz D, Heilmaier M, Phillip V, Treiber M, Mayr U, Lahmer T, Mueller J, Demir IE, Friess H, Reichert M, Schmid RM, Abdelhafez M. Accurate prediction of histological grading of intraductal papillary mucinous neoplasia using deep learning. Endoscopy. 2023 May;55(5):415-422. doi: 10.1055/a-1971-1274. Epub 2022 Nov 2.
PubMed 36323331
Kuwahara T, Hara K, Mizuno N, Okuno N, Matsumoto S, Obata M, Kurita Y, Koda H, Toriyama K, Onishi S, Ishihara M, Tanaka T, Tajika M, Niwa Y. Usefulness of Deep Learning Analysis for the Diagnosis of Malignancy in Intraductal Papillary Mucinous Neoplasms of the Pancreas. Clin Transl Gastroenterol. 2019 May 22;10(5):1-8. doi: 10.14309/ctg.0000000000000045.
PubMed 31117111
Nguon 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
Vilas-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 36140443
Gheorghiu 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 38813054
Huang 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 40355497
Hwang 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 28779397
Jiang 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 37887627
Oh 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 35009788
Rangwani 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 35735595
European 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 29574408
Elta 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 29485131

Study at a glance

From the registry record, last updated by the sponsor on 3 Sep 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.

Detailed description

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 3 Sep 2026.
Results publication: PubMed 37775472Wu HL, Yao LW, Shi HY, Wu LL, Li X, Zhang CX, Chen BR, Zhang J, Tan W, Cui N, Zhou W, Zhang JX, Xiao B, Gong RR, Ding Z, Yu HG. Validation of a real-time biliopancreatic endoscopic ultrasonography analytical device in China: a prospective, single-centre, randomised, controlled trial. Lancet Digit Health. 2023 Nov;5(11):e812-e820. doi: 10.1016/S2589-7500(23)00160-7. Epub 2023 Sep 27.
Results publication: PubMed 38079604Tian S, Shi H, Chen W, Li S, Han C, Du F, Wang W, Wen H, Lei Y, Deng L, Tang J, Zhang J, Lin J, Shi L, Ning B, Zhao K, Miao J, Wang G, Hou H, Huang X, Kong W, Jin X, Ding Z, Lin R. Artificial intelligence-based diagnosis of standard endoscopic ultrasonography scanning sites in the biliopancreatic system: a multicenter retrospective study. Int J Surg. 2024 Mar 1;110(3):1637-1644. doi: 10.1097/JS9.0000000000000995.
Results publication: PubMed 36220072Lipkova J, Chen RJ, Chen B, Lu MY, Barbieri M, Shao D, Vaidya AJ, Chen C, Zhuang L, Williamson DFK, Shaban M, Chen TY, Mahmood F. Artificial intelligence for multimodal data integration in oncology. Cancer Cell. 2022 Oct 10;40(10):1095-1110. doi: 10.1016/j.ccell.2022.09.012.
Results publication: PubMed 36323331Schulz D, Heilmaier M, Phillip V, Treiber M, Mayr U, Lahmer T, Mueller J, Demir IE, Friess H, Reichert M, Schmid RM, Abdelhafez M. Accurate prediction of histological grading of intraductal papillary mucinous neoplasia using deep learning. Endoscopy. 2023 May;55(5):415-422. doi: 10.1055/a-1971-1274. Epub 2022 Nov 2.
Results publication: PubMed 31117111Kuwahara T, Hara K, Mizuno N, Okuno N, Matsumoto S, Obata M, Kurita Y, Koda H, Toriyama K, Onishi S, Ishihara M, Tanaka T, Tajika M, Niwa Y. Usefulness of Deep Learning Analysis for the Diagnosis of Malignancy in Intraductal Papillary Mucinous Neoplasms of the Pancreas. Clin Transl Gastroenterol. 2019 May 22;10(5):1-8. doi: 10.14309/ctg.0000000000000045.
Results publication: PubMed 34201066Nguon 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.
Results publication: PubMed 36140443Vilas-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.
Results publication: PubMed 38813054Gheorghiu 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.
Results publication: 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.
Results publication: 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.
Results publication: 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.
Results publication: 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.
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.

Zhen Li contact

86+18560086106

qilulizhen@sdu.edu.cn

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 3 Sep 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 3 Sep 2026.