Ignite Creation Date:
2025-12-24 @ 3:33 PM
Ignite Modification Date:
2025-12-24 @ 3:33 PM
Study NCT ID:
NCT04328792
Status:
UNKNOWN
Last Update Posted:
2020-04-02
First Post:
2019-12-02
Is Possible Gene Therapy:
False
Has Adverse Events:
False
Brief Title:
Prediction Model of CP-EBUS in the Diagnosis of Lymph Nodes
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}}, 'protocolSection': {'designModule': {'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'OTHER', 'observationalModel': 'COHORT'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 1300}, 'patientRegistry': False}, 'statusModule': {'overallStatus': 'UNKNOWN', 'lastKnownStatus': 'RECRUITING', 'startDateStruct': {'date': '2018-07-01', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2020-03', 'completionDateStruct': {'date': '2020-12-31', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2020-03-31', 'studyFirstSubmitDate': '2019-12-02', 'studyFirstSubmitQcDate': '2020-03-29', 'lastUpdatePostDateStruct': {'date': '2020-04-02', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2020-03-31', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2020-06-30', 'type': 'ESTIMATED'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'Diagnostic efficacy of EBUS multimodal artificial intelligence prediction model based on videos', 'timeFrame': '6 months post-procedure', 'description': 'Diagnostic efficacy includes sensitivity, specificity, positive predictive value, negative predictive value and diagnostic accuracy.'}], 'secondaryOutcomes': [{'measure': 'Diagnostic efficacy of traditional qualitative and quantitative methods', 'timeFrame': '6 months post-procedure', 'description': 'Diagnostic efficacy includes sensitivity, specificity, positive predictive value, negative predictive value and diagnostic accuracy.'}, {'measure': 'Diagnostic efficacy of multimodal deep learning model based on images', 'timeFrame': '6 months post-procedure', 'description': 'Diagnostic efficacy includes sensitivity, specificity, positive predictive value, negative predictive value and diagnostic accuracy.'}, {'measure': 'Comparion of prediction model based on deeping learning with traditional qualitative and quantitative methods', 'timeFrame': '6 months post-procedure', 'description': 'Diagnostic efficacy includes sensitivity, specificity, positive predictive value, negative predictive value and diagnostic accuracy.'}]}, 'oversightModule': {'oversightHasDmc': True, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'keywords': ['EBUS-TBNA', 'Intrathoracic lymph node', 'Multimodal image', 'Deep learning', 'Prediction model'], 'conditions': ['Lymph Node Disease']}, 'referencesModule': {'references': [{'pmid': '20521347', 'type': 'BACKGROUND', 'citation': 'Steinfort DP, Conron M, Tsui A, Pasricha SR, Renwick WE, Antippa P, Irving LB. Endobronchial ultrasound-guided transbronchial needle aspiration for the evaluation of suspected lymphoma. J Thorac Oncol. 2010 Jun;5(6):804-9. doi: 10.1097/jto.0b013e3181d873be.'}, {'pmid': '24035300', 'type': 'BACKGROUND', 'citation': 'Sun J, Teng J, Yang H, Li Z, Zhang J, Zhao H, Garfield DH, Han B. Endobronchial ultrasound-guided transbronchial needle aspiration in diagnosing intrathoracic tuberculosis. Ann Thorac Surg. 2013 Dec;96(6):2021-7. doi: 10.1016/j.athoracsur.2013.07.005. Epub 2013 Sep 12.'}, {'pmid': '20382710', 'type': 'BACKGROUND', 'citation': 'Fujiwara T, Yasufuku K, Nakajima T, Chiyo M, Yoshida S, Suzuki M, Shibuya K, Hiroshima K, Nakatani Y, Yoshino I. The utility of sonographic features during endobronchial ultrasound-guided transbronchial needle aspiration for lymph node staging in patients with lung cancer: a standard endobronchial ultrasound image classification system. Chest. 2010 Sep;138(3):641-7. doi: 10.1378/chest.09-2006. Epub 2010 Apr 9.'}, {'pmid': '22525556', 'type': 'BACKGROUND', 'citation': 'Nakajima T, Anayama T, Shingyoji M, Kimura H, Yoshino I, Yasufuku K. Vascular image patterns of lymph nodes for the prediction of metastatic disease during EBUS-TBNA for mediastinal staging of lung cancer. J Thorac Oncol. 2012 Jun;7(6):1009-14. doi: 10.1097/JTO.0b013e31824cbafa.'}, {'pmid': '26228606', 'type': 'BACKGROUND', 'citation': 'Wang L, Wu W, Hu Y, Teng J, Zhong R, Han B, Sun J. Sonographic Features of Endobronchial Ultrasonography Predict Intrathoracic Lymph Node Metastasis in Lung Cancer Patients. Ann Thorac Surg. 2015 Oct;100(4):1203-9. doi: 10.1016/j.athoracsur.2015.04.143. Epub 2015 Jul 28.'}, {'pmid': '25121724', 'type': 'BACKGROUND', 'citation': 'Izumo T, Sasada S, Chavez C, Matsumoto Y, Tsuchida T. Endobronchial ultrasound elastography in the diagnosis of mediastinal and hilar lymph nodes. Jpn J Clin Oncol. 2014 Oct;44(10):956-62. doi: 10.1093/jjco/hyu105. Epub 2014 Aug 13.'}, {'pmid': '21437851', 'type': 'BACKGROUND', 'citation': 'Saftoiu A, Vilmann P, Gorunescu F, Janssen J, Hocke M, Larsen M, Iglesias-Garcia J, Arcidiacono P, Will U, Giovannini M, Dietrich C, Havre R, Gheorghe C, McKay C, Gheonea DI, Ciurea T; European EUS Elastography Multicentric Study Group. Accuracy of endoscopic ultrasound elastography used for differential diagnosis of focal pancreatic masses: a multicenter study. Endoscopy. 2011 Jul;43(7):596-603. doi: 10.1055/s-0030-1256314. Epub 2011 Mar 24.'}, {'pmid': '27898976', 'type': 'BACKGROUND', 'citation': 'Gulshan V, Peng L, Coram M, Stumpe MC, Wu D, Narayanaswamy A, Venugopalan S, Widner K, Madams T, Cuadros J, Kim R, Raman R, Nelson PC, Mega JL, Webster DR. Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs. JAMA. 2016 Dec 13;316(22):2402-2410. doi: 10.1001/jama.2016.17216.'}]}, 'descriptionModule': {'briefSummary': 'Endobronchial ultrasound (EBUS) multimodal image including grey scale, blood flow doppler and elastography, can be used as non-invasive diagnosis and supplement the pathological result, which has important clinical application value. In this study, EBUS multimodal image database of 1000 inthoracic benign and malignant lymph nodes (LNs) will be constructed to train deep learning neural networks, which can automatically select representative images and diagnose LNs. Investigators will establish an artificial intelligence prediction model based on deep learning of intrathoracic LNs, and verify the model in other 300 LNs.', 'detailedDescription': 'Intrathoracic LNs enlargement has a wide range of diseases, among which intrathoracic LNs metastasis of lung cancer is the most common malignant disease. Benign lesions, including inflammation, tuberculosis and sarcoidosis, also need to be differentiated for targeted treatment.\n\nEBUS multimodal image including grey scale, blood flow doppler and elastography, can be used as non-invasive diagnosis and supplement the pathological result, which has important clinical application value. This study includes two parts: retrospectively construction of EBUS artificial intelligence prediction model and multi-center prospectively validation of the prediction model. A total of 1300 LNs will be enrolled in the study.\n\nDuring the retention of videos, target LNs and peripheral vessels are examined using ultrasound hosts (EU-ME2, Olympus or Hi-vision Avius, Hitachi) equipped with elastography and doppler functions and ultrasound bronchoscopy (BF-UC260FW, Olympus or EB1970UK, Pentax). Multimodal image data of target LNs are collected.\n\nInvestigators will construct artificial intelligence prediction model based on deep learning using images from 1000 LNs firstly, and verify the model in other 300 LNs. This model will be compared with traditional qualitative and quantitative evaluation methods to verify the diagnostic efficacy.'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'minimumAge': '18 Years', 'samplingMethod': 'NON_PROBABILITY_SAMPLE', 'studyPopulation': 'Patients with enlarged intrathoracic LNs that need to be diagnosed by EBUS-TBNA are enrolled in this study.', 'eligibilityCriteria': 'Inclusion Criteria:\n\n1. Chest CT shows enlarged intrathoracic LNs (short diameter \\> 1 cm) or PET / CT shows patients with increased FDG uptake (SUV ≧ 2.0) in intrathoracic LNs;\n2. Operating physician considered EBUS-TBNA should be performed on LNs for diagnosis or preoperative staging of lung cancer;\n3. Patients agree to undergo EBUS-TBNA, sign informed consent, and have no contraindications.\n\nExclusion Criteria:\n\n\\- Patients having other situations that are not suitable for EBUS-TBNA.'}, 'identificationModule': {'nctId': 'NCT04328792', 'briefTitle': 'Prediction Model of CP-EBUS in the Diagnosis of Lymph Nodes', 'organization': {'class': 'OTHER', 'fullName': 'Shanghai Chest Hospital'}, 'officialTitle': 'Prediction Model Based on Deep Learning of CP-EBUS Multimodal Image in the Diagnosis of Benign and Malignant Lymph Nodes', 'orgStudyIdInfo': {'id': 'SHCHE201906'}}, 'armsInterventionsModule': {'armGroups': [{'label': 'Prospectively validation group', 'description': 'Two diagnosis methods will be used in the prospective validation section, one is traditional qualitative and quantitative method, the other is artificial intelligence prediction model based on videos to compare the diagnostic efficacy.'}]}, 'contactsLocationsModule': {'locations': [{'zip': '200030', 'city': 'Shanghai', 'state': 'Shanghai Municipality', 'status': 'RECRUITING', 'country': 'China', 'contacts': [{'name': 'Jiayuan Sun, PhD', 'role': 'CONTACT', 'email': 'jysun1976@163.com', 'phone': '86-21-22200000', 'phoneExt': '1511'}], 'facility': 'Shanghai Chest Hospital', 'geoPoint': {'lat': 31.22222, 'lon': 121.45806}}], 'centralContacts': [{'name': 'Jiayuan Sun, MD, PhD', 'role': 'CONTACT', 'email': 'jysun1976@163.com', 'phone': '86-21-22200000', 'phoneExt': '1511'}], 'overallOfficials': [{'name': 'Jiayuan Sun, MD, PhD', 'role': 'STUDY_DIRECTOR', 'affiliation': 'Shanghai Chest Hospital'}]}, 'ipdSharingStatementModule': {'ipdSharing': 'UNDECIDED', 'description': 'Investigators may release the database after the study, but no decision has been made yet.'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Shanghai Chest Hospital', 'class': 'OTHER'}, 'responsibleParty': {'type': 'PRINCIPAL_INVESTIGATOR', 'investigatorTitle': 'Director,Department of Respiratory Endoscopy ,Shanghai Chest Hospital', 'investigatorFullName': 'Jiayuan Sun', 'investigatorAffiliation': 'Shanghai Chest Hospital'}}}}