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Not yet recruiting NCT07543263

An Artificial Intelligence System for Multimodal, Multi-class Diagnosis of Pancreatic Cystic Lesions Based on Endoscopic Ultrasonography

— Observational Pancreatic Cystic Lesion (PCL)

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-PCL(intelligent endoscopic ultrasound system- pancreatic cystic lesion).
Who it may be relevant to
Registry conditions: Pancreatic Cystic Lesion (PCL). 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 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.

Interventions

  • Device 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.

Primary outcome measures

  • The accuracy of iEUS-PCL for pancreatic cystic lesions [Time frame: During procedure]
  • The sensitivity of iEUS-PCL for pancreatic cystic lesions [Time frame: During procedure]
  • The specificicy of iEUS-PCL for pancreatic cystic lesions [Time frame: During procedure]
  • The postive predictive value of iEUS-PCL for pancreatic cystic lesions [Time frame: During procedure]
  • The negative predictive value of iEUS-PCL for pancreatic cystic lesions [Time frame: During procedure]
Secondary outcome measures (5)
  • 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]

Eligibility criteria

Inclusion criteria

\- 1. 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.

2\. Patients with no prior history of treatment for pancreatic lesions.

Exclusion criteria

\- 1. Patients with absolute contraindications to EUS examination. 2. Pregnancy or lactating. 3. Uncorrectable coagulopathy(PTT>50 seconds or INR>1.5) and/or uncorrectable thrombocytopenia(platelet count<50×109/L). 4. Upper gastrointestinal obstruction. 5. 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.

6\. Patients who have undergone biliary/pancreatic duct stent placement. 7. 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: No

Study design

Observational model
Cohort

Study locations

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

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. PMID 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/JS9.0 PMID 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. PMID 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. PMID 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. PMID 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. PMID 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. PMID 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. PMID 38813054

Identifiers

NCT: NCT07543263 · 2026-SDU-QILU-2

Primary sources (government registries)

View this study on ClinicalTrials.gov ↗