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| Name | Class |
|---|---|
| Liaocheng People's Hospital | OTHER |
| Taian City Central Hospital | OTHER |
| Qilu Hospital of Shandong University (Qingdao) | OTHER |
| Binzhou People's Hospital |
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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.
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.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| 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. |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| 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:
|
| Measure | Description | Time Frame |
|---|---|---|
| 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). | 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). | 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). | 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). | 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). | During procedure |
| Measure | Description | Time Frame |
|---|---|---|
| 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). | During procedure |
| Comparison of the sensitivity between iEUS-PCL and endosonographers |
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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.
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Adult patients with suspected pancreatic cystic lesions undergoing EUS.
| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Zhen Li | Contact | 86+18560086106 | qilulizhen@sdu.edu.cn |
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| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Qilu Hospital of Shandong University | Jinan | Shandong | 250012 | China |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 37775472 | Result | 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. | |
| 38079604 |
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| OTHER |
| Shandong Provincial Hospital | OTHER_GOV |
| The Affiliated Hospital of Qingdao University | OTHER |
| Qianfoshan Hospital | OTHER |
| Shengli Oilfield Hospital | OTHER |
| Binzhou Medical University | OTHER |
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EUS images, EUS features, clinical data and radiological imaging features from patients who underwent EUS.
|
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). |
| 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). | 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). | 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). | During procedure |
| Result |
| 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. |
| 36220072 | Result | 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. |
| 36323331 | Result | 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. |
| 31117111 | Result | 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. |
| 34201066 | Result | 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. |
| 36140443 | Result | 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. |
| 38813054 | Result | 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. |
| 40355497 | Result | 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. |
| 28779397 | Result | 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. |
| 37887627 | Result | 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. |
| 35009788 | Result | 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. |
| 35735595 | Result | 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. |
| 29574408 | Result | 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. |
| 29485131 | Result | 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. |
| 38423346 | Result | Vilela 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. |
| 36766560 | Result | Kloth 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. |
| 36276094 | Result | 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. |
| 37835797 | Result | 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. |
| 35509425 | Result | 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. |