Whole-slide Image and CT Radiomics Based Deep Learning System for Prognostication Prediction in Bladder Cancer
NCT ID: NCT06389019
Last Updated: 2025-05-28
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
1000 participants
OBSERVATIONAL
2024-01-01
2025-10-01
Brief Summary
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Detailed Description
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Conditions
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Study Design
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OTHER
RETROSPECTIVE
Study Groups
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BLCA
patients with bladder cancer who had surgery like radical cystectomy or transurethral resection of bladder tumour (TURBT).
Deep learning system for prognostication prediction in bladder cancer
develop and validate a deep learning system for prognostication prediction in bladder cancer based on CT radiomics and whole slide images.
Interventions
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Deep learning system for prognostication prediction in bladder cancer
develop and validate a deep learning system for prognostication prediction in bladder cancer based on CT radiomics and whole slide images.
Eligibility Criteria
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Inclusion Criteria
* contrast-CT scan less than two weeks before surgery
* complete CT image data and clinical data
* complete whole slide image data
Exclusion Criteria
* poor quality of CT images
* incomplete clinical and follow-up data
ALL
No
Sponsors
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Mingzhao Xiao
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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Other Identifiers
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K2024-187-01
Identifier Type: OTHER
Identifier Source: secondary_id
BLCA_CMUFH
Identifier Type: -
Identifier Source: org_study_id
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