Deep Learning Radiomics Model for Predicting Post-cystectomy Outcome in Muscle Invasive Bl
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 preoperat
| Condition(s) | Bladder Cancer |
|---|---|
| Status | Recruiting |
| Study type | Observational |
| Summary | 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. |
| Who can participate | Inclusion Criteria: * 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 Criteria: * patients who received neoadjuvant therapy; * non-urothelial carcinoma; * poor quality of CT images; * incomplete clinical and follow-up data. |
| Sex | All |
| Lead sponsor | First Affiliated Hospital of Chongqing Medical University |
| Locations | Chongqing, Chongqing Municipality, China |
| Start date | 2023-08-01 |
| NCT ID | NCT06092450 |
| Official listing | https://clinicaltrials.gov/study/NCT06092450 |