Ignite Creation Date:
2025-12-24 @ 9:47 PM
Ignite Modification Date:
2026-01-01 @ 8:01 PM
Study NCT ID:
NCT05261932
Status:
UNKNOWN
Last Update Posted:
2022-03-02
First Post:
2022-02-17
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
Research on Endoscopic Precision Biopsy.
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}}, 'protocolSection': {'designModule': {'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'PROSPECTIVE', 'observationalModel': 'CASE_CONTROL'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 40}, 'patientRegistry': False}, 'statusModule': {'overallStatus': 'UNKNOWN', 'lastKnownStatus': 'RECRUITING', 'startDateStruct': {'date': '2021-11-26', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2022-02', 'completionDateStruct': {'date': '2023-11-30', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2022-02-28', 'studyFirstSubmitDate': '2022-02-17', 'studyFirstSubmitQcDate': '2022-02-28', 'lastUpdatePostDateStruct': {'date': '2022-03-02', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2022-03-02', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2023-06-01', 'type': 'ESTIMATED'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'The accuracy of AI', 'timeFrame': 'June 2023', 'description': 'Concordance rate between biopsy and postoperative pathology'}, {'measure': 'The accuracy of expert with or without AI', 'timeFrame': 'June 2023', 'description': 'Concordance rate between expert experience and postoperative pathology'}, {'measure': 'The accuracy of non-expert with or without AI', 'timeFrame': 'June 2023', 'description': 'Concordance rate between non-expert experience and postoperative pathology'}]}, 'oversightModule': {'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'keywords': ['AI system', 'Colonoscope', 'Guided Biopsy'], 'conditions': ['Colorectal Adenoma']}, 'descriptionModule': {'briefSummary': 'Colorectal adenoma is a common disease and frequently-occurring disease in gastroenterology. With the continuous progress of colonoscopy equipment and the gradual improvement of endoscopic accessories, especially the development of chromo-endoscopy and magnifying endoscopy. The observation of the surface structure and capillary morphology of colorectal adenomas can realize optical biopsy. Currently, most clinical endoscopic diagnosis of colorectal diseases is biopsy under colonoscopy, and further treatment options are determined based on the pathological results of the biopsy. The problem is that the pathological diagnosis of some preoperative biopsy is not completely consistent with the pathological diagnosis of postoperative large specimens. Previous studies have found that the pathological diagnosis accuracy rate of preoperative biopsy is only 66-75%, so there is a certain degree of subjectivity in relying solely on colonoscopy white light biopsy. Based on the previous work, the research team has initially established an intelligent recognition model for colorectal adenoma classification (low-grade intraepithelial neoplasia, high-grade intraepithelial neoplasia), and formed a colorectal adenoma of a certain size with annotated endoscopic image data set. Using the YOLO-V4 algorithm, under the Darknet framework, to train an artificial intelligence (AI) system which specifically for adenoma recognition and diagnosis, its accuracy rate has reached more than 90%. This study intends to increase the sample size based on the previous work, and further improve the accuracy of the classification and diagnosis of the AI system, so as to guide the endoscopist to perform targeted biopsy and improve the accuracy of preoperative biopsy.'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'maximumAge': '75 Years', 'minimumAge': '30 Years', 'samplingMethod': 'PROBABILITY_SAMPLE', 'studyPopulation': 'This study has been reviewed by the hospital ethics committee. The enrolled subjects were found to have advanced colorectal adenomas during colonoscopy, and already had the pathological results of AI-assisted guided biopsy. The patients were hospitalized for complete resection with EMR and ESD and were willing to participate in this study.', 'healthyVolunteers': False, 'eligibilityCriteria': 'Inclusion Criteria:\n\n* Age between 30-75;\n* Those who have no mental abnormality and can conduct questionnaire surveys;\n* BBPS ≥ 6;\n* Colorectal advanced adenoma, and admitted for complete resection with EMR and ESD;\n* Provide the relevant information required by this study and sign the informed consent.\n\nExclusion Criteria:\n\n* Those who cannot provide the relevant information required by this research;\n* Patients with inflammatory bowel disease;\n* Those with a history of liver cirrhosis, uncontrolled hypertension, history of myocardial infarction, cardiac insufficiency, renal insufficiency, respiratory failure, diabetic ketosis and electrolyte imbalance and other serious diseases;\n* Those who cannot stop antiplatelet drugs or anticoagulant drugs;\n* Those who have not completed full colonoscopy;\n* Pregnant women.'}, 'identificationModule': {'nctId': 'NCT05261932', 'acronym': 'REPB', 'briefTitle': 'Research on Endoscopic Precision Biopsy.', 'organization': {'class': 'OTHER', 'fullName': 'Beijing Tsinghua Chang Gung Hospital'}, 'officialTitle': 'Research on Endoscopic Precision Biopsy Guided by AI System', 'orgStudyIdInfo': {'id': '12021C1011'}}, 'armsInterventionsModule': {'armGroups': [{'label': 'The accuracy of expert with or with-out AI', 'interventionNames': ['Procedure: AI-assisted guided biopsy']}, {'label': 'The accuracy non-expert with or with-out AI', 'interventionNames': ['Procedure: AI-assisted guided biopsy']}], 'interventions': [{'name': 'AI-assisted guided biopsy', 'type': 'PROCEDURE', 'description': 'The surface of the adenoma was classified and identified by the AI system, and different areas of the adenoma were marked by distribution to guide the endoscopist for biopsy to obtain the poorly differentiated portion of the lesion.', 'armGroupLabels': ['The accuracy non-expert with or with-out AI', 'The accuracy of expert with or with-out AI']}]}, 'contactsLocationsModule': {'locations': [{'zip': '102218', 'city': 'Beijing', 'state': 'Beijing Municipality', 'status': 'RECRUITING', 'country': 'China', 'contacts': [{'name': 'Ruigang Wang', 'role': 'CONTACT', 'email': 'wrga02147@btch.edu.cn'}], 'facility': 'Beijing Tsinghua Changgung Hospital', 'geoPoint': {'lat': 39.9075, 'lon': 116.39723}}], 'centralContacts': [{'name': 'Ruigang Wang', 'role': 'CONTACT', 'email': 'wrga02147@btch.edu.cn'}, {'name': 'Xuan Jiang', 'role': 'CONTACT', 'email': 'jxa01998@btch.edu.cn', 'phone': '+86 (010)56119096'}], 'overallOfficials': [{'name': 'Ruigang Wang', 'role': 'STUDY_CHAIR', 'affiliation': 'Beijing Tsinghua Changgeng Hospital'}]}, 'ipdSharingStatementModule': {'ipdSharing': 'UNDECIDED', 'description': 'After completing the pre-experiment.'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Beijing Tsinghua Chang Gung Hospital', 'class': 'OTHER'}, 'responsibleParty': {'type': 'SPONSOR'}}}}