Actively Recruiting
Whole-slide Image and CT Radiomics Based Deep Learning System for Prognostication Prediction in Bladder Cancer
Led by Mingzhao Xiao · Updated on 2025-05-28
1000
Participants Needed
1
Research Sites
91 weeks
Total Duration
On this page
AI-Summary
What this Trial Is About
Bladder cancer (BLCA), with its diverse histopathological features and varying patient outcomes, poses significant challenges in diagnosis and prognosis. Postoperative survival stratification based on radiomics feature and whole slide image feature may be useful for treatment decisions to improve prognosis. In this research, we aim to develop a deep learning-based prognostic-stratification system for automatic prediction of overall and cancer-specific survival in patients with BLCA.
CONDITIONS
Official Title
Whole-slide Image and CT Radiomics Based Deep Learning System for Prognostication Prediction in Bladder Cancer
Who Can Participate
Eligibility Criteria
You may qualify if you...
- Patients with bladder cancer who had surgery like radical cystectomy or transurethral resection of bladder tumour (TURBT)
- Contrast-CT scan less than two weeks before surgery
- Complete CT image data and clinical data
- Complete whole slide image data
You will not qualify if you...
- Patients with a postoperative diagnosis of non-urothelial carcinoma
- Poor quality of CT images
- Incomplete clinical and follow-up data
AI-Screening
AI-Powered Screening
Complete this quick 3-step screening to check your eligibility
Trial Site Locations
Total: 1 location
1
Department of Urology, The First Affiliated Hospital of Chongqing Medical University
Chongqing, Chongqing Municipality, China, 400016
Actively Recruiting
Research Team
Q
QuanHao He
CONTACT
M
Mingzhao Xiao, PHD
CONTACT
How is the study designed?
Study Type
OBSERVATIONAL
Masking
N/A
Allocation
N/A
Model
N/A
Primary Purpose
N/A
Number of Arms
1
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