NCT07381192

Brief Summary

The aim of this study is to validate an artificial intelligence system named iEUS-SPL(intelligent endoscopic ultrasound system-solid pancreatic lesion) for detecting and multimodal, multi-class diagnosing solid pancreatic lesions during endoscopic ultrasound(EUS) examination.

Trial Health

77
On Track

Trial Health Score

Automated assessment based on enrollment pace, timeline, and geographic reach

Enrollment
383

participants targeted

Target at P75+ for all trials

Timeline
26mo left

Started Sep 2025

Typical duration for all trials

Geographic Reach
1 country

1 active site

Status
recruiting

Health score is calculated from publicly available data and should be used for screening purposes only.

Trial Relationships

Click on a node to explore related trials.

Study Timeline

Key milestones and dates

Study Progress22%
Sep 2025Jun 2028

Study Start

First participant enrolled

September 1, 2025

Completed
4 months until next milestone

First Submitted

Initial submission to the registry

December 24, 2025

Completed
1 month until next milestone

First Posted

Study publicly available on registry

February 2, 2026

Completed
2.4 years until next milestone

Primary Completion

Last participant's last visit for primary outcome

June 30, 2028

Expected
Same day until next milestone

Study Completion

Last participant's last visit for all outcomes

June 30, 2028

Last Updated

February 2, 2026

Status Verified

September 1, 2025

Enrollment Period

2.8 years

First QC Date

December 24, 2025

Last Update Submit

January 23, 2026

Conditions

Keywords

endoscopic ultrasoundsolid pancreatic lesion

Outcome Measures

Primary Outcomes (6)

  • The accuracy of iEUS-SPL for solid pancreatic lesions

    The primary outcome of the study is to evaluate the accuracy of the iEUS-SPL in identifying the solid pancreatic lesions (including pancreatic cancer, pancreatic neuroendocrine tumor, solid pseudopapillary tumor, autoimmune pancreatitis, chronic pancreatitis).

    During procedure

  • The sensitivity of iEUS-SPL for solid pancreatic lesions

    The primary outcome of the study is to evaluate the sensitivity of the iEUS-SPL in identifying the solid pancreatic lesions (including pancreatic cancer, pancreatic neuroendocrine tumor, solid pseudopapillary tumor, autoimmune pancreatitis, chronic pancreatitis).

    During procedure

  • The specificicy of iEUS-SPL for solid pancreatic lesions

    The primary outcome of the study is to evaluate the specificity of the iEUS-SPL in identifying the solid pancreatic lesions (including pancreatic cancer, pancreatic neuroendocrine tumor, solid pseudopapillary tumor, autoimmune pancreatitis, chronic pancreatitis).

    During procedure

  • The postive predictive value of iEUS-SPL for solid pancreatic lesions

    The primary outcome of the study is to evaluate the postive predictive value of the iEUS-SPL in identifying the solid pancreatic lesions (including pancreatic cancer, pancreatic neuroendocrine tumor, solid pseudopapillary tumor, autoimmune pancreatitis, chronic pancreatitis).

    During procedure

  • The negative predictive value of iEUS-SPL for solid pancreatic lesions

    The primary outcome of the study is to evaluate the negative predictive value of the iEUS-SPL in identifying the solid pancreatic lesions (including pancreatic cancer, pancreatic neuroendocrine tumor, solid pseudopapillary tumor, autoimmune pancreatitis, chronic pancreatitis).

    During procedure

  • the lesion detection rate of iEUS-SPL for detecting solid pancreatic lesions

    The primary outcome of the study is to evaluate the lesion detection rate of the iEUS-SPL in identifying the solid pancreatic lesions(defined as the number of detected lesions divided by the total number of lesions).

    During procedure

Secondary Outcomes (5)

  • Comparison of the accuracy between iEUS-SPL and endosonographers

    During procedure

  • Comparison of the sensitivity between iEUS-SPL and endosonographers

    During procedure

  • Comparison of the specificity between iEUS-SPL and endosonographers

    During procedure

  • Comparison of the postive predictive value between iEUS-SPL and endosonographers

    During procedure

  • Comparison of the negative predictive value between iEUS-SPL and endosonographers

    During procedure

Study Arms (1)

Patients undergoing EUS

Patients aged ≥18 years scheduled for EUS with suspected solid pancreatic lesions based on clinical symptoms, medical history, laboratory tests or radiological examinations agree to participate in the research and be able to sign informed consent.

Device: iEUS-SPL(intelligent endoscopic ultrasound system-pancreatic solid lesion)

Interventions

The iEUS-SPL will automaticly detect solid pancreatic lesions and integrate the patients' endoscopic ultrasound images, endoscopic ultrasound features, clinical data and imaging features to perform a five-category classification for the lesions, categorizing them as pancreatic cancer, pancreatic neuroendocrine tumor, solid pseudopapillary tumor, autoimmune pancreatitis and chronic pancreatitis.

Patients undergoing EUS

Eligibility Criteria

Age18 Years+
Sexall
Healthy VolunteersYes
Age GroupsAdult (18-64), Older Adult (65+)
Sampling MethodNon-Probability Sample
Study Population

Adult patients with suspected solid pancreatic lesions undergoing EUS.

You may qualify if:

  • Patients aged ≥18 years scheduled for EUS with suspected solid pancreatic lesions based on clinical symptoms, medical history, laboratory tests or radiological examinations agree to participate in the research and be able to sign informed consent.
  • Patients with no prior history of treatment for pancreatic lesions.

You may not qualify if:

  • 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\<50Ă—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.

Contact the study team to confirm eligibility.

Sponsors & Collaborators

Study Sites (1)

Qilu Hospital of Shandong University

Jinan, Shandong, 250012, China

RECRUITING

Related Publications (18)

  • Bang JY, Saftoiu A, Udristoiu A, Gruionu L, Codruta Gheorghe E, Gruionu G, Ramesh J, Wilcox CM, Varadarajulu S. Prospective clinical validation of a novel artificial intelligence system for real-time detection of solid pancreatic masses during endoscopic ultrasonography. Endoscopy. 2026 Mar;58(3):223-232. doi: 10.1055/a-2701-6530. Epub 2025 Sep 15.

    PMID: 40953587BACKGROUND
  • Cui H, Zhao Y, Xiong S, Feng Y, Li P, Lv Y, Chen Q, Wang R, Xie P, Luo Z, Cheng S, Wang W, Li X, Xiong D, Cao X, Bai S, Yang A, Cheng B. Diagnosing Solid Lesions in the Pancreas With Multimodal Artificial Intelligence: A Randomized Crossover Trial. JAMA Netw Open. 2024 Jul 1;7(7):e2422454. doi: 10.1001/jamanetworkopen.2024.22454.

    PMID: 39028670BACKGROUND
  • 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.

    PMID: 37775472BACKGROUND
  • Zhang J, Zhu L, Yao L, Ding X, Chen D, Wu H, Lu Z, Zhou W, Zhang L, An P, Xu B, Tan W, Hu S, Cheng F, Yu H. Deep learning-based pancreas segmentation and station recognition system in EUS: development and validation of a useful training tool (with video). Gastrointest Endosc. 2020 Oct;92(4):874-885.e3. doi: 10.1016/j.gie.2020.04.071. Epub 2020 May 6.

    PMID: 32387499BACKGROUND
  • Oh CK, Kim T, Cho YK, Cheung DY, Lee BI, Cho YS, Kim JI, Choi MG, Lee HH, Lee S. Convolutional neural network-based object detection model to identify gastrointestinal stromal tumors in endoscopic ultrasound images. J Gastroenterol Hepatol. 2021 Dec;36(12):3387-3394. doi: 10.1111/jgh.15653. Epub 2021 Aug 16.

    PMID: 34369001BACKGROUND
  • Dahiya DS, Al-Haddad M, Chandan S, Gangwani MK, Aziz M, Mohan BP, Ramai D, Canakis A, Bapaye J, Sharma N. Artificial Intelligence in Endoscopic Ultrasound for Pancreatic Cancer: Where Are We Now and What Does the Future Entail? J Clin Med. 2022 Dec 16;11(24):7476. doi: 10.3390/jcm11247476.

    PMID: 36556092BACKGROUND
  • Kim YH, Kim GH, Kim KB, Lee MW, Lee BE, Baek DH, Kim DH, Park JC. Application of A Convolutional Neural Network in The Diagnosis of Gastric Mesenchymal Tumors on Endoscopic Ultrasonography Images. J Clin Med. 2020 Sep 29;9(10):3162. doi: 10.3390/jcm9103162.

    PMID: 33003602BACKGROUND
  • Qin X, Zhang M, Zhou C, Ran T, Pan Y, Deng Y, Xie X, Zhang Y, Gong T, Zhang B, Zhang L, Wang Y, Li Q, Wang D, Gao L, Zou D. A deep learning model using hyperspectral image for EUS-FNA cytology diagnosis in pancreatic ductal adenocarcinoma. Cancer Med. 2023 Aug;12(16):17005-17017. doi: 10.1002/cam4.6335. Epub 2023 Jul 17.

    PMID: 37455599BACKGROUND
  • 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.

    PMID: 38079604BACKGROUND
  • 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.

    PMID: 35009788BACKGROUND
  • Norton ID, Zheng Y, Wiersema MS, Greenleaf J, Clain JE, Dimagno EP. Neural network analysis of EUS images to differentiate between pancreatic malignancy and pancreatitis. Gastrointest Endosc. 2001 Nov;54(5):625-9. doi: 10.1067/mge.2001.118644.

    PMID: 11677484BACKGROUND
  • Nakamura H, Fukuda M, Matsuda A, Makino N, Kimura H, Ohtaki Y, Nawa Y, Oyama S, Suzuki Y, Kobayashi T, Ishizawa T, Kakizaki Y, Ueno Y. Differentiating localized autoimmune pancreatitis and pancreatic ductal adenocarcinoma using endoscopic ultrasound images with deep learning. DEN Open. 2024 Mar 2;4(1):e344. doi: 10.1002/deo2.344. eCollection 2024 Apr.

    PMID: 38434146BACKGROUND
  • Dhali A, Kipkorir V, Srichawla BS, Kumar H, Rathna RB, Ongidi I, Chaudhry T, Morara G, Nurani K, Cheruto D, Biswas J, Chieng LR, Dhali GK. Artificial intelligence assisted endoscopic ultrasound for detection of pancreatic space-occupying lesion: a systematic review and meta-analysis. Int J Surg. 2023 Dec 1;109(12):4298-4308. doi: 10.1097/JS9.0000000000000717.

    PMID: 37800594BACKGROUND
  • Das A, Nguyen CC, Li F, Li B. Digital image analysis of EUS images accurately differentiates pancreatic cancer from chronic pancreatitis and normal tissue. Gastrointest Endosc. 2008 May;67(6):861-7. doi: 10.1016/j.gie.2007.08.036. Epub 2008 Jan 7.

    PMID: 18179797BACKGROUND
  • Kuwahara T, Hara K, Mizuno N, Haba S, Okuno N, Kuraishi Y, Fumihara D, Yanaidani T, Ishikawa S, Yasuda T, Yamada M, Onishi S, Yamada K, Tanaka T, Tajika M, Niwa Y, Yamaguchi R, Shimizu Y. Artificial intelligence using deep learning analysis of endoscopic ultrasonography images for the differential diagnosis of pancreatic masses. Endoscopy. 2023 Feb;55(2):140-149. doi: 10.1055/a-1873-7920. Epub 2022 Jun 10.

    PMID: 35688454BACKGROUND
  • Tian 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.

    PMID: 36276094BACKGROUND
  • Qin 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.

    PMID: 37835797BACKGROUND
  • 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.

    PMID: 35509425BACKGROUND

Biospecimen

Retention: SAMPLES WITHOUT DNA

Endoscopic ultrasound images, endoscopic ultrasound features, clinical data and imaging features from patients who underwent endoscopic ultrasound.

Central Study Contacts

Study Design

Study Type
observational
Observational Model
COHORT
Time Perspective
PROSPECTIVE
Target Duration
6 Months
Sponsor Type
OTHER
Responsible Party
SPONSOR

Study Record Dates

First Submitted

December 24, 2025

First Posted

February 2, 2026

Study Start

September 1, 2025

Primary Completion (Estimated)

June 30, 2028

Study Completion (Estimated)

June 30, 2028

Last Updated

February 2, 2026

Record last verified: 2025-09

Data Sharing

IPD Sharing
Will not share

Locations