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Recruiting NCT07381192

An Artificial Intelligence System for Multimodal, Multi-class Diagnosing Solid Pancreatic Lesions Based on Endoscopic Ultrasound

— Observational Endoscopic Ultrasound (EUS) Solid Pancreatic Lesion

For patients and families

In plain language

An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.

What is being studied
The protocol lists: iEUS-SPL(intelligent endoscopic ultrasound system-pancreatic solid lesion).
Who it may be relevant to
Registry conditions: Endoscopic Ultrasound (EUS), Solid Pancreatic Lesion. Basic parameters: from 18 years · All.
What needs checking
Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
Where it takes place
China
Next step
Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →

Unsure about the terms? Read our patient guide →

Overview

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.

Detailed description

This is an observational study with a prospective, cohort design. We have developed an artificial intelligence system named iEUS-SPL for multimodal, multi-class diagnosing solid pancreatic lesions using endoscopic ultrasound images, endoscopic ultrasound features, clinical data and imaging features from retrospectively collected patients who underwent EUS. The lesion detection rate and diagnostic performance of iEUS-SPL in identifying solid pancreatic lesions will be evaluated in real-time EUS scanning videos over prospective enrolled cases.

Interventions

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

Primary outcome measures

  • The accuracy of iEUS-SPL for solid pancreatic lesions [Time frame: During procedure]
  • The sensitivity of iEUS-SPL for solid pancreatic lesions [Time frame: During procedure]
  • The specificicy of iEUS-SPL for solid pancreatic lesions [Time frame: During procedure]
  • The postive predictive value of iEUS-SPL for solid pancreatic lesions [Time frame: During procedure]
  • The negative predictive value of iEUS-SPL for solid pancreatic lesions [Time frame: During procedure]
  • the lesion detection rate of iEUS-SPL for detecting solid pancreatic lesions [Time frame: During procedure]
Secondary outcome measures (5)
  • Comparison of the accuracy between iEUS-SPL and endosonographers [Time frame: During procedure]
  • Comparison of the sensitivity between iEUS-SPL and endosonographers [Time frame: During procedure]
  • Comparison of the specificity between iEUS-SPL and endosonographers [Time frame: During procedure]
  • Comparison of the postive predictive value between iEUS-SPL and endosonographers [Time frame: During procedure]
  • Comparison of the negative predictive value between iEUS-SPL and endosonographers [Time frame: During procedure]

Eligibility criteria

Inclusion criteria

  • 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.

Exclusion criteria

  • 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.

Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.

Healthy volunteers: Yes

Study design

Observational model
Cohort

Study locations

China · 1 center
  • Qilu Hospital of Shandong University — Jinan

Publications

  • 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 40953587
  • 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 39028670
  • 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 37775472
  • 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 32387499
  • 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 34369001
  • 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 36556092
  • 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 33003602
  • 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 37455599

Identifiers

NCT: NCT07381192 · 2025-SDU-QILU-6

Primary sources (government registries)

View this study on ClinicalTrials.gov ↗