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

Trial statusRecruiting
Trial phaseNot listed
Trial typeObservational
Biological sexAll
AgeNot listed
SponsorFirst Affiliated Hospital of Chongqing Medical University

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.

Eligibility criteria

Qualifiers

patients with pathologically confirmed MIBC after radical cystectomy;

contrast-CT scan less than two weeks before surgery;

complete CT image data and clinical data.

Disqualifiers

patients who received neoadjuvant therapy;

non-urothelial carcinoma;

poor quality of CT images;

incomplete clinical and follow-up data.

Trial design

Treatments tested in this trial

  • develop and validate a deep learning radiomics model based on preoperative enhanced CT image

Treatment groups

500 Participants
are divided into 1 treatment group