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

NCT06092450 | Recruiting

• 2 other identifiers: AI-BLCA2022-K508
• Study Type: observational
• Target: 500
• Locations: 1 country
• Sites: 1

Brief 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.

Conditions

Bladder Cancer

Keywords

Tomography, X-ray computed; Muscle-invasive bladder cancer; Radiomics; Deep Learning

Outcome Measures

Primary Outcomes (2)

• 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

• 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

Study Arms (1)

MIBC

patients with pathologically confirmed MIBC after radical cystectomy

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

Interventions

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

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

MIBC

Eligibility Criteria

• Sex: all
• Healthy Volunteers: No
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

patients with pathologically confirmed MIBC who underwent radical cystectomy

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.

Sponsors & Collaborators

• First Affiliated Hospital of Chongqing Medical University: lead

Study Sites (1)

Department of Urology, The First Affiliated Hospital of Chongqing Medical University

Chongqing, Chongqing Municipality, 400016, China

RECRUITING

Related Publications (1)

• Wei Z, Xv Y, Liu H, Li Y, Yin S, Xie Y, Chen Y, Lv F, Jiang Q, Li F, Xiao M. A CT-based deep learning model predicts overall survival in patients with muscle invasive bladder cancer after radical cystectomy: a multicenter retrospective cohort study. Int J Surg. 2024 May 1;110(5):2922-2932. doi: 10.1097/JS9.0000000000001194.

  PMID: 38349205 DERIVED

MeSH Terms

Conditions

Urinary Bladder Neoplasms

Condition Hierarchy (Ancestors)

Urologic Neoplasms; Urogenital Neoplasms; Neoplasms by Site; Neoplasms; Female Urogenital Diseases; Female Urogenital Diseases and Pregnancy Complications; Urogenital Diseases; Urinary Bladder Diseases; Urologic Diseases; Male Urogenital Diseases

Central Study Contacts

Zongjie Wei

CONTACT

023-89012557; wzj9846@163.com

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Professor

Study Record Dates

• First Submitted: October 12, 2023
• First Posted: October 23, 2023
• Study Start: August 1, 2023
• Primary Completion: June 1, 2025
• Study Completion: June 1, 2025
• Last Updated: May 31, 2025

Record last verified: 2025-05

Data Sharing

• IPD Sharing: Will not share

Locations

CN (1)