RECRUITINGOBSERVATIONAL
Deep Learning Radiomics Model for Predicting Post-cystectomy Outcome in Muscle Invasive Bladder Cancer
Deep Learning Radiomics Model for Predicting Post-cystectomy Outcome From Preoperative CT in Muscle Invasive Bladder Cancer
About This Trial
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 May Be Eligible (Plain English)
Who May Qualify:
- patients with pathologically confirmed MIBC after radical cystectomy;
- contrast-CT scan less than two weeks before surgery;
- complete CT image data and clinical data.
Who Should NOT Join This Trial:
- patients who received neoadjuvant therapy;
- non-urothelial carcinoma;
- poor quality of CT images;
- incomplete clinical and follow-up data.
Always talk to your doctor about whether this trial is right for you.
Original Eligibility Criteria
View original clinical language
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.
Treatments Being Tested
OTHER
develop and validate a deep learning radiomics model based on preoperative enhanced CT image
develop and validate a deep learning radiomics model based on preoperative enhanced CT to predict postoperative survival in MIBC
Locations (1)
Department of Urology, The First Affiliated Hospital of Chongqing Medical University
Chongqing, Chongqing Municipality, China