Deep Learning Radiomics Model for Predicting Post-cystectomy Outcome in Muscle Invasive Bladder Cancer
NCT ID: NCT06092450
Last Updated: 2025-05-31
Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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RECRUITING
500 participants
OBSERVATIONAL
2023-08-01
2025-06-01
Brief Summary
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Detailed Description
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Conditions
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Study Design
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COHORT
RETROSPECTIVE
Study Groups
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MIBC
patients with pathologically confirmed MIBC after radical cystectomy
develop and validate a deep learning radiomics model based on preoperative enhanced CT image
develop and validate a deep learning radiomics model based on preoperative enhanced CT to predict postoperative survival in MIBC
Interventions
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develop and validate a deep learning radiomics model based on preoperative enhanced CT image
develop and validate a deep learning radiomics model based on preoperative enhanced CT to predict postoperative survival in MIBC
Eligibility Criteria
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Inclusion Criteria
* contrast-CT scan less than two weeks before surgery;
* complete CT image data and clinical data.
Exclusion Criteria
* non-urothelial carcinoma;
* poor quality of CT images;
* incomplete clinical and follow-up data.
ALL
No
Sponsors
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First Affiliated Hospital of Chongqing Medical University
OTHER
Responsible Party
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Mingzhao Xiao
Professor
Locations
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Department of Urology, The First Affiliated Hospital of Chongqing Medical University
Chongqing, Chongqing Municipality, China
Countries
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Central Contacts
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Facility Contacts
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References
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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.
Other Identifiers
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2022-K508
Identifier Type: OTHER
Identifier Source: secondary_id
AI-BLCA
Identifier Type: -
Identifier Source: org_study_id
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