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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 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.
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.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| 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. |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| iEUS-SPL(intelligent endoscopic ultrasound system-pancreatic solid lesion) | Device | 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. |
| Measure | Description | Time Frame |
|---|---|---|
| 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). |
| Measure | Description | Time Frame |
|---|---|---|
| Comparison of the accuracy between iEUS-SPL and endosonographers | The secondary outcome of the study is to comparing the accuracy between iEUS-SPL and different-level endosonographers in identifying the solid pancreatic lesions (including pancreatic cancer, pancreatic neuroendocrine tumor, solid pseudopapillary tumor, autoimmune pancreatitis, chronic pancreatitis). | During procedure |
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Inclusion Criteria:
Exclusion Criteria:
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Adult patients with suspected solid pancreatic lesions undergoing EUS.
| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Zhen Li | Contact | 18560086106 | qilulizhen@sdu.edu.cn |
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| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Qilu Hospital of Shandong University | Recruiting | Jinan | Shandong | 250012 | China |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 40953587 | Background | 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. | |
| 39028670 |
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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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Endoscopic ultrasound images, endoscopic ultrasound features, clinical data and imaging features from patients who underwent endoscopic ultrasound.
|
| 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 |
| Comparison of the sensitivity between iEUS-SPL and endosonographers | The secondary outcome of the study is to comparing the sensitivity between iEUS-SPL and different-level endosonographers in identifying the solid pancreatic lesions (including pancreatic cancer, pancreatic neuroendocrine tumor, solid pseudopapillary tumor, autoimmune pancreatitis, chronic pancreatitis). | During procedure |
| Comparison of the specificity between iEUS-SPL and endosonographers | The secondary outcome of the study is to comparing the specificity between iEUS-SPL and different-level endosonographers in identifying the solid pancreatic lesions (including pancreatic cancer, pancreatic neuroendocrine tumor, solid pseudopapillary tumor, autoimmune pancreatitis, chronic pancreatitis). | During procedure |
| Comparison of the postive predictive value between iEUS-SPL and endosonographers | The secondary outcome of the study is to comparing the postive predictive value between iEUS-SPL and different-level endosonographers in identifying the solid pancreatic lesions (including pancreatic cancer, pancreatic neuroendocrine tumor, solid pseudopapillary tumor, autoimmune pancreatitis, chronic pancreatitis). | During procedure |
| Comparison of the negative predictive value between iEUS-SPL and endosonographers | The secondary outcome of the study is to comparing the negative predictive value between iEUS-SPL and different-level endosonographers in identifying the solid pancreatic lesions (including pancreatic cancer, pancreatic neuroendocrine tumor, solid pseudopapillary tumor, autoimmune pancreatitis, chronic pancreatitis). | During procedure |
| 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. |
| 37775472 | Background | 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. |
| 32387499 | Background | 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. |
| 34369001 | Background | 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. |
| 36556092 | Background | 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. |
| 33003602 | Background | 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. |
| 37455599 | Background | 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. |
| 38079604 | Background | 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. |
| 35009788 | Background | 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. |
| 11677484 | Background | 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. |
| 38434146 | Background | 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. |
| 37800594 | Background | 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. |
| 18179797 | Background | 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. |
| 35688454 | Background | 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. |
| 36276094 | Background | 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 | Background | 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 | Background | 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. |