Deep Learning Radiomics Model for Predicting Post-cystectomy Outcome in Muscle Invasive Bladder Cancer

Recruiting now

Conditions studied: Bladder Cancer

In brief

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.

Key facts

Study ID

NCT06092450

Run by

First Affiliated Hospital of Chongqing Medical University

People needed

500

Starts

2023-08-01

Expected to finish

2025-06-01

Last updated by the study team

2025-05-31

Who can join

Age: any. Sex: any. Healthy volunteers: not accepted.

You may qualify if…

• patients with pathologically confirmed MIBC after radical cystectomy;
• contrast-CT scan less than two weeks before surgery;
• complete CT image data and clinical data.

You may not qualify if…

• patients who received neoadjuvant therapy;
• non-urothelial carcinoma;
• poor quality of CT images;
• incomplete clinical and follow-up data.

Where it is running

• Department of Urology, The First Affiliated Hospital of Chongqing Medical University — Chongqing, Chongqing Municipality, China (enrolling)

Full record on ClinicalTrials.gov

Trial information comes from ClinicalTrials.gov and is refreshed daily. TrialsForMe does not provide medical care and does not run the studies it lists.