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

Chongqing Medical University

Status: Enrolling

Conditions: Bladder Cancer

Treatments: Other: develop and validate a deep learning radiomics model based on preoperative enhanced CT image

Study type: Observational

Funder types: Other

Identifiers

NCT06092450

2022-K508 (Other Identifier)

AI-BLCA

Details and patient eligibility

About

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.

Enrollment: 500 estimated patients

Sex: All

Volunteers: No Healthy Volunteers

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.

Trial design

500 participants in 1 patient group

MIBC

Description:

patients with pathologically confirmed MIBC after radical cystectomy

Treatment:

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