Actively Recruiting
Deep Learning Radiomics Model for Predicting Post-cystectomy Outcome in Muscle Invasive Bladder Cancer
Led by First Affiliated Hospital of Chongqing Medical University · Updated on 2025-05-31
500
Participants Needed
1
Research Sites
95 weeks
Total Duration
On this page
AI-Summary
What this Trial Is About
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.
CONDITIONS
Official Title
Deep Learning Radiomics Model for Predicting Post-cystectomy Outcome in Muscle Invasive Bladder Cancer
Who Can Participate
Eligibility Criteria
You may qualify if you...
- Patients with pathologically confirmed muscle invasive bladder cancer after radical cystectomy
- Contrast-enhanced CT scan performed less than two weeks before surgery
- Complete CT image data and clinical data available
You will not qualify if you...
- Patients who received neoadjuvant therapy before surgery
- Non-urothelial carcinoma diagnosis
- 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
Z
Zongjie Wei
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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