Deep Learning Radiomics Model for Predicting Post-cystectomy Outcome From Preoperative CT in Muscle Invasive Bladder Cancer
Status: Recruiting
Location: See location...
Intervention Type: Other
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.
Eligibility
Participation Requirements
Sex: All
Healthy Volunteers: f
View:
• patients with pathologically confirmed MIBC after radical cystectomy;
• contrast-CT scan less than two weeks before surgery;
• complete CT image data and clinical data.
Locations
Other Locations
China
Department of Urology, The First Affiliated Hospital of Chongqing Medical University
RECRUITING
Chongqing
Contact Information
Primary
Zongjie Wei
wzj9846@163.com
023-89012557
Time Frame
Start Date: 2023-08-01
Estimated Completion Date: 2025-06-01
Participants
Target number of participants: 500
Treatments
MIBC
patients with pathologically confirmed MIBC after radical cystectomy
Related Therapeutic Areas
Sponsors
Leads: First Affiliated Hospital of Chongqing Medical University