Ignite Creation Date:
2025-12-24 @ 11:36 PM
Ignite Modification Date:
2026-01-01 @ 4:27 PM
Study NCT ID:
NCT05193656
Status:
RECRUITING
Last Update Posted:
2024-01-30
First Post:
2021-11-17
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
Bladder Cancer Detection Using Convolutional Neural Networks
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}, 'conditionBrowseModule': {'meshes': [{'id': 'D001749', 'term': 'Urinary Bladder Neoplasms'}], 'ancestors': [{'id': 'D014571', 'term': 'Urologic Neoplasms'}, {'id': 'D014565', 'term': 'Urogenital Neoplasms'}, {'id': 'D009371', 'term': 'Neoplasms by Site'}, {'id': 'D009369', 'term': 'Neoplasms'}, {'id': 'D052776', 'term': 'Female Urogenital Diseases'}, {'id': 'D005261', 'term': 'Female Urogenital Diseases and Pregnancy Complications'}, {'id': 'D000091642', 'term': 'Urogenital Diseases'}, {'id': 'D001745', 'term': 'Urinary Bladder Diseases'}, {'id': 'D014570', 'term': 'Urologic Diseases'}, {'id': 'D052801', 'term': 'Male Urogenital Diseases'}]}}, 'protocolSection': {'designModule': {'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'PROSPECTIVE', 'observationalModel': 'COHORT'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 5000}, 'patientRegistry': False}, 'statusModule': {'overallStatus': 'RECRUITING', 'startDateStruct': {'date': '2021-06-01', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2024-01', 'completionDateStruct': {'date': '2026-06-01', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2024-01-28', 'studyFirstSubmitDate': '2021-11-17', 'studyFirstSubmitQcDate': '2022-01-14', 'lastUpdatePostDateStruct': {'date': '2024-01-30', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2022-01-18', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2026-06-01', 'type': 'ESTIMATED'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'Comparing standard technique to Machine Learning', 'timeFrame': '5 years', 'description': 'The accuracy of Machine learning to detect bladder cancer compared to standard cystoscopy'}], 'secondaryOutcomes': [{'measure': 'Detecting accuracy of subtypes of bladder cancer', 'timeFrame': '5 years', 'description': 'The abelity of Machine Learning to identify high grad bladder cancer from low grad bladder cancer'}]}, 'oversightModule': {'oversightHasDmc': False, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'keywords': ['Machine learning', 'Artificial intelligence'], 'conditions': ['Bladder Cancer']}, 'descriptionModule': {'briefSummary': 'The investigators aim to experiment and implement various deep learning architectures to achieve human-level accuracy in Computer-aided diagnosis (CAD) systems. In particular, the investigators are interested in detecting bladder tumors from CT urography scans and cystoscopies of the bladder in this project.', 'detailedDescription': 'The investigators aim to experiment and implement various deep learning architectures to achieve human-level accuracy in Computer-aided diagnosis (CAD) systems. In particular, the investigators are interested in detecting bladder tumors from CT urography scans and cystoscopies of the bladder in this project. The investigators want to classify bladder tumors as cancer, non cancer, high grade and low grade, invasive and non-invasive, with high sensitivity and low false positive rate using various convolutional neural networks (CNN). This task can be considered as the first step in building CAD systems for bladder cancer diagnosis. Moreover, by automating this task, the investigator scan significantly reduce the time for the radiologists to create large-scale labeled datasets of CT-urography scans and reduce the false-negative and positive that can happen due to human evaluation cystoscopies.'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['CHILD', 'ADULT', 'OLDER_ADULT'], 'samplingMethod': 'PROBABILITY_SAMPLE', 'studyPopulation': 'Patients with micro or macroscopic hematuria', 'healthyVolunteers': False, 'eligibilityCriteria': 'Inclusion Criteria:\n\n* Patients with first time hematuria\n* Patients with the control program for previous bladder cancer\n\nExclusion Criteria:\n\n* Patients with control cystoscope for noncancer suspected disease'}, 'identificationModule': {'nctId': 'NCT05193656', 'acronym': 'BLAInostic', 'briefTitle': 'Bladder Cancer Detection Using Convolutional Neural Networks', 'organization': {'class': 'OTHER', 'fullName': 'Zealand University Hospital'}, 'officialTitle': 'Bladder Cancer Detection Using Convolutional Neural Networks', 'orgStudyIdInfo': {'id': 'SJ-905'}}, 'armsInterventionsModule': {'armGroups': [{'label': 'Detecting bladder tumor', 'description': 'Patients with hematuria, or previous bladder tumor', 'interventionNames': ['Diagnostic Test: Al_bladder']}], 'interventions': [{'name': 'Al_bladder', 'type': 'DIAGNOSTIC_TEST', 'description': 'Detection of bladder tumor with help of Artificial intelligence', 'armGroupLabels': ['Detecting bladder tumor']}]}, 'contactsLocationsModule': {'locations': [{'zip': '4000', 'city': 'Roskilde', 'status': 'RECRUITING', 'country': 'Denmark', 'contacts': [{'name': 'Nessn H. Azawi, M.D.', 'role': 'CONTACT', 'email': 'nesa@regionsjaelland.dk', 'phone': '004526393034'}, {'name': 'Nessn Azawi, Ph.D', 'role': 'PRINCIPAL_INVESTIGATOR'}], 'facility': 'Zealand University Hospital', 'geoPoint': {'lat': 55.64152, 'lon': 12.08035}}], 'centralContacts': [{'name': 'Nessn Azawi, phd', 'role': 'CONTACT', 'email': 'nesa@regionsjaelland.dk', 'phone': '26393034'}], 'overallOfficials': [{'name': 'Nessn Azawi, phd', 'role': 'PRINCIPAL_INVESTIGATOR', 'affiliation': 'Zealand University Hospital'}]}, 'ipdSharingStatementModule': {'ipdSharing': 'NO'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Zealand University Hospital', 'class': 'OTHER'}, 'responsibleParty': {'type': 'SPONSOR'}}}}