Ignite Creation Date:
2025-12-24 @ 4:49 PM
Ignite Modification Date:
2025-12-26 @ 4:26 AM
Study NCT ID:
NCT06092450
Status:
RECRUITING
Last Update Posted:
2025-05-31
First Post:
2023-10-12
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
Deep Learning Radiomics Model for Predicting Post-cystectomy Outcome in Muscle Invasive Bladder Cancer
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': 'RETROSPECTIVE', 'observationalModel': 'COHORT'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 500}, 'patientRegistry': False}, 'statusModule': {'overallStatus': 'RECRUITING', 'startDateStruct': {'date': '2023-08-01', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2025-05', 'completionDateStruct': {'date': '2025-06-01', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2025-05-27', 'studyFirstSubmitDate': '2023-10-12', 'studyFirstSubmitQcDate': '2023-10-18', 'lastUpdatePostDateStruct': {'date': '2025-05-31', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2023-10-23', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2025-06-01', 'type': 'ESTIMATED'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'Overall survival（OS）', 'timeFrame': 'up to 10 years', 'description': 'the time from the date of surgery to death from any cause or the date of last contact (censored observation) at the date of data cut-off.'}, {'measure': 'Recurrence free survival（RFS)', 'timeFrame': 'up to 10 years', 'description': 'the time from the date of surgery to the date of first documented disease recurrence. Patients without recurrence at the time of analysis will be censored.'}]}, 'oversightModule': {'isUsExport': False, 'oversightHasDmc': False, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'keywords': ['Tomography, X-ray computed', 'Muscle-invasive bladder cancer', 'Radiomics', 'Deep Learning'], 'conditions': ['Bladder Cancer']}, 'referencesModule': {'references': [{'pmid': '38349205', 'type': 'DERIVED', 'citation': 'Wei Z, Xv Y, Liu H, Li Y, Yin S, Xie Y, Chen Y, Lv F, Jiang Q, Li F, Xiao M. A CT-based deep learning model predicts overall survival in patients with muscle invasive bladder cancer after radical cystectomy: a multicenter retrospective cohort study. Int J Surg. 2024 May 1;110(5):2922-2932. doi: 10.1097/JS9.0000000000001194.'}]}, 'descriptionModule': {'briefSummary': 'Muscle invasive bladder cancer (MIBC) has a poor prognosis even after radical cystectomy. Postoperative survival stratification based on radiomics and deep learning may be useful for treatment decisions to improve prognosis. This study was aimed to develop and validate a deep learning radiomics model based on preoperative enhanced CT to predict postoperative survival in MIBC.'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['CHILD', 'ADULT', 'OLDER_ADULT'], 'samplingMethod': 'NON_PROBABILITY_SAMPLE', 'studyPopulation': 'patients with pathologically confirmed MIBC who underwent radical cystectomy', 'healthyVolunteers': False, 'eligibilityCriteria': 'Inclusion Criteria:\n\n* patients with pathologically confirmed MIBC after radical cystectomy;\n* contrast-CT scan less than two weeks before surgery;\n* complete CT image data and clinical data.\n\nExclusion Criteria:\n\n* patients who received neoadjuvant therapy;\n* non-urothelial carcinoma;\n* poor quality of CT images;\n* incomplete clinical and follow-up data.'}, 'identificationModule': {'nctId': 'NCT06092450', 'briefTitle': 'Deep Learning Radiomics Model for Predicting Post-cystectomy Outcome in Muscle Invasive Bladder Cancer', 'organization': {'class': 'OTHER', 'fullName': 'First Affiliated Hospital of Chongqing Medical University'}, 'officialTitle': 'Deep Learning Radiomics Model for Predicting Post-cystectomy Outcome From Preoperative CT in Muscle Invasive Bladder Cancer', 'orgStudyIdInfo': {'id': 'AI-BLCA'}, 'secondaryIdInfos': [{'id': '2022-K508', 'type': 'OTHER', 'domain': 'The First Affiliated Hospital of Chongqing Medical University'}]}, 'armsInterventionsModule': {'armGroups': [{'label': 'MIBC', 'description': 'patients with pathologically confirmed MIBC after radical cystectomy', 'interventionNames': ['Other: develop and validate a deep learning radiomics model based on preoperative enhanced CT image']}], 'interventions': [{'name': 'develop and validate a deep learning radiomics model based on preoperative enhanced CT image', 'type': 'OTHER', 'description': 'develop and validate a deep learning radiomics model based on preoperative enhanced CT to predict postoperative survival in MIBC', 'armGroupLabels': ['MIBC']}]}, 'contactsLocationsModule': {'locations': [{'zip': '400016', 'city': 'Chongqing', 'state': 'Chongqing Municipality', 'status': 'RECRUITING', 'country': 'China', 'contacts': [{'name': 'Zongjie Wei', 'role': 'CONTACT', 'email': 'wzj9846@163.com', 'phone': '023-89012557'}], 'facility': 'Department of Urology, The First Affiliated Hospital of Chongqing Medical University', 'geoPoint': {'lat': 29.56026, 'lon': 106.55771}}], 'centralContacts': [{'name': 'Zongjie Wei', 'role': 'CONTACT', 'email': 'wzj9846@163.com', 'phone': '023-89012557'}]}, 'ipdSharingStatementModule': {'ipdSharing': 'NO', 'description': 'The datasets analyzed during the current study are not publicly available due to the privacy of patients but are available from the corresponding author on reasonable request.'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'First Affiliated Hospital of Chongqing Medical University', 'class': 'OTHER'}, 'responsibleParty': {'type': 'PRINCIPAL_INVESTIGATOR', 'investigatorTitle': 'Professor', 'investigatorFullName': 'Mingzhao Xiao', 'investigatorAffiliation': 'First Affiliated Hospital of Chongqing Medical University'}}}}