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
First Affiliated Hospital of Chongqing Medical University
500 participants
Aug 1, 2023
OBSERVATIONAL
Conditions
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.
Eligibility
Inclusion Criteria3
- 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 Criteria4
- patients who received neoadjuvant therapy;
- non-urothelial carcinoma;
- poor quality of CT images;
- incomplete clinical and follow-up data.
Interventions
develop and validate a deep learning radiomics model based on preoperative enhanced CT to predict postoperative survival in MIBC
Locations(1)
View Full Details on ClinicalTrials.gov
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NCT06092450