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

May 27, 2025 updated by: Mingzhao Xiao, First Affiliated Hospital of Chongqing Medical University

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

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.

Study Overview

Status

Recruiting

Conditions

Intervention / Treatment

Study Type

Observational

Enrollment (Estimated)

500

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Study Locations

  • China
    • Chongqing
      • Chongqing, Chongqing, China, 400016
        • Recruiting
        • Department of Urology, The First Affiliated Hospital of Chongqing Medical University
        • Contact:

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Child
  • Adult
  • Older Adult

Accepts Healthy Volunteers

No

Sampling Method

Non-Probability Sample

Study Population

patients with pathologically confirmed MIBC who underwent radical cystectomy

Description

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.

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

Cohorts and Interventions

Group / Cohort
Intervention / Treatment
MIBC
patients with pathologically confirmed MIBC after radical cystectomy
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

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Overall survival（OS）
Time Frame: up to 10 years
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)
Time Frame: up to 10 years
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

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

First Affiliated Hospital of Chongqing Medical University

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

August 1, 2023

Primary Completion (Estimated)

June 1, 2025

Study Completion (Estimated)

June 1, 2025

Study Registration Dates

First Submitted

October 12, 2023

First Submitted That Met QC Criteria

October 18, 2023

First Posted (Actual)

October 23, 2023

Study Record Updates

Last Update Posted (Actual)

May 31, 2025

Last Update Submitted That Met QC Criteria

May 27, 2025

Last Verified

May 1, 2025

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • AI-BLCA
  • 2022-K508 (Other Identifier: The First Affiliated Hospital of Chongqing Medical University)

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

NO

IPD Plan Description

The datasets analyzed during the current study are not publicly available due to the privacy of patients but are available from the corresponding author on reasonable request.

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

product manufactured in and exported from the U.S.

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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