Whole-slide Image and CT Radiomics Based Deep Learning System for Prognostication Prediction in Bladder Cancer

November 5, 2026 updated by: Mingzhao Xiao

Bladder cancer (BLCA), with its diverse histopathological features and varying patient outcomes, poses significant challenges in diagnosis and prognosis. Postoperative survival stratification based on radiomics feature and whole slide image feature may be useful for treatment decisions to improve prognosis. In this research, we aim to develop a deep learning-based prognostic-stratification system for automatic prediction of overall and cancer-specific survival in patients with BLCA.This observational study evaluates a deep learning system that combines CT images and digitized pathology slides to predict outcomes in patients with bladder cancer. It includes a retrospective cohort and a prospective cohort of newly enrolled patients. The prospective cohort will assess the system in patients enrolled during (2024-2026), with follow-up for survival and recurrence.

Study Overview

Status

Recruiting

Conditions

Intervention / Treatment

Detailed Description

Study structure The study includes an initial retrospective phase and a subsequent prospective observational phase involving newly enrolled patients. The two phases are analyzed separately according to the specified analysis plan.

Retrospective phase The retrospective phase included patients from [2012-2024] and was used for model construction. Results from this phase were reported in npj series.

Prospective phase Enrollment of newly recruited patients began on [2024]. The prospective phase follows protocol, with ethics approval from the first affliated hospital of chongqing medical, university. Recruitment is consecutive, as specified in the protocol.

Model evaluation The prospective cohort is used for validation. The model version, preprocessing procedures and prediction thresholds used in this phase. Any model updating using this cohort is documented separately.

Registry update This update distinguishes the retrospective phase from the subsequently enrolled cohort. The prospective phase enrollment dates and the dates of protocol development and ethics approval are reported separately.

Study Type

Observational

Enrollment (Estimated)

2000

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 Contact Backup

Study Locations

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

Genders Eligible for Study

All

Sampling Method

Non-Probability Sample

Study Population

We included patients who had surgery only or who had neoadjuvant chemotherapy before surgery. We excluded patients with a postoperative diagnosis of non-urothelial carcinoma.

Description

Inclusion Criteria:

  • patients with bladder cancer who had surgery like radical cystectomy or transurethral resection of bladder tumour (TURBT)
  • contrast-CT scan less than two weeks before surgery
  • complete CT image data and clinical data
  • complete whole slide image data

Exclusion Criteria:

  • patients with a postoperative diagnosis of non-urothelial carcinoma
  • poor quality of CT images

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

  • Observational Models: COHORT
  • Time Perspectives: OTHER

Cohorts and Interventions

Group / Cohort
Intervention / Treatment
Retrospective cohort

Patients with bladder cancer included in the retrospective phase of the study, using clinical records, CT images, and whole-slide images obtained during [2012-2024]. This cohort was used for model development and/or validation and was reported in our npj publications.

develop and validate a deep learning system for prognostication prediction in bladder cancer based on CT radiomics and whole slide images.

Prospective cohort of newly enrolled patients

Newly enrolled patients with bladder cancer recruited at four centers between 2024 and2026. Eligibility is assessed at enrollment, and participants are followed according to protocol version and date for the study outcomes. This cohort is distinct from the previously reported retrospective cohort.

develop and validate a deep learning system for prognostication prediction in bladder cancer based on CT radiomics and whole slide images.

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Overall survival
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

Secondary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Recurrence free survival
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
progression-free survival
Time Frame: up to 10 years

The length of time during and after the treatment of a disease, such as cancer, that a patient lives with the disease but it does not get worse. In a clinical trial, measuring the progression-free survival is one way to see how well a new treatment works. Also called PFS.

up to 10 years

Collaborators and Investigators

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

Sponsor

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)

January 1, 2024

Primary Completion (Estimated)

December 1, 2027

Study Completion (Estimated)

December 1, 2027

Study Registration Dates

First Submitted

April 25, 2024

First Submitted That Met QC Criteria

April 26, 2024

First Posted (Actual)

April 29, 2024

Study Record Updates

Last Update Posted (Actual)

November 9, 2026

Last Update Submitted That Met QC Criteria

November 5, 2026

Last Verified

November 1, 2026

More Information

Terms related to this study

Other Study ID Numbers

  • BLCA_CMUFH
  • K2024-187-01 (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

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.