Using CT Scans and Deep Learning to Predict Bladder Cancer Outcomes
Tests a computer model using pre-surgery CT scans to predict survival in muscle-invasive bladder cancer.
Plain-language title generated from the registry record
Registry title: Deep Learning Radiomics Model for Predicting Post-cystectomy Outcome in Muscle Invasive Bladder Cancer
Key facts
- Where
- Chongqing, ChinaSee the site
Where in China
- Department of Urology, The First Affiliated Hospital of Chongqing Medical UniversityChongqing, ChinaTaking people
Questions to ask the study team
- How often do I need to come in, and for how long each time?
- Are travel costs paid?
- Can I keep my own doctor?
- What happens if I want to stop?
Trial details
- Who runs it
- First Affiliated Hospital of Chongqing Medical University
- Trial code
- NCT06092450
- Record updated
- 31 May 2025
- Original description
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.Read full original descriptionShow less
Deep Learning Radiomics Model for Predicting Post-cystectomy Outcome From Preoperative CT in Muscle Invasive Bladder Cancer
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.
Inclusion: patients with pathologically confirmed MIBC after radical cystectomy; contrast-CT scan less than two weeks before surgery; complete CT image data and clinical data.
Exclusion: patients who received neoadjuvant therapy; non-urothelial carcinoma; poor quality of CT images; incomplete clinical and follow-up data.