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

Bladder Cancer Clinical Trial

Official title:

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

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT06092450
• Other study ID #: AI-BLCA
• Secondary ID: 2022-K508
• Status: Recruiting
• Start date: August 1, 2023
• Est. completion date: June 1, 2024

Study information

• Verified date: October 2023
• Source: First Affiliated Hospital of Chongqing Medical University
• Contact: Zongjie Wei
• Phone: 023-89012557
• Email: wzj9846[@]163.com
• Is FDA regulated: No
• Study type: Observational

Clinical Trial 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.

Recruitment information / eligibility

• Status: Recruiting
• Enrollment: 500
• Est. completion date: June 1, 2024
• Est. primary completion date: December 1, 2023
• Accepts healthy volunteers: No
• Gender: All
• Age group: N/A and older

Eligibility

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.

Related Conditions & MeSH terms

• Bladder Cancer
• Urinary Bladder Neoplasms

Intervention

Other:
• develop and validate a deep learning radiomics model based on preoperative enhanced CT image
develop and validate a deep learning radiomics model based on preoperative enhanced CT to predict postoperative survival in MIBC

Locations

Country	Name	City	State
China	Department of Urology, The First Affiliated Hospital of Chongqing Medical University	Chongqing	Chongqing

Sponsors (1)

Lead Sponsor	Collaborator
First Affiliated Hospital of Chongqing Medical University

Country where clinical trial is conducted

China, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Overall survival(OS)	the time from the date of surgery to death from any cause or the date of last contact (censored observation) at the date of data cut-off.	up to 10 years
Primary	Recurrence free survival(RFS)	the time from the date of surgery to the date of first documented disease recurrence. Patients without recurrence at the time of analysis will be censored.	up to 10 years