Bladder CancerNCT06389019
Whole-slide Image and CT Radiomics Based Deep Learning System for Prognostication Prediction in Bladder Cancer
An observational study of Bladder Cancer, run by Mingzhao Xiao at 1 site in China.
Trial summary
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. Read more about this study
- Who is it for?
- Any age
- Conditions the study is for
- Bladder Cancer
- What is being tested?
- Deep learning system for prognostication prediction in bladder cancer
- How many people
- 1,000 participants
- Where
- 1 siteChina
- Study dates
- 1 Jan 2024to 1 Oct 2025
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages Any age
- All sexes
- No healthy volunteers
- Child, Adult, Older adult
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.
Who may be able to join
- 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
Who cannot join
- patients with a postoperative diagnosis of non-urothelial carcinoma
- poor quality of CT images
- incomplete clinical and follow-up data
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
China1 site, 1 recruiting
- Recruiting
Department of Urology, The First Affiliated Hospital of Chongqing Medical University
Chongqing, Chongqing Municipality, 400016
QuanHao He, PHD, 800-555-5555, 2020120460@stu.cqmu.edu.cn
Mingzhao Xiao, PHD, 023-89012557, 2023140134@stu.cqmu.edu.cn
About this study
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.
Read the detailed description
Bladder cancer can be difficult to diagnose and predict outcomes for, as the disease can vary greatly between patients. This research aims to develop a new system that uses artificial intelligence to analyze patient information, including images from surgery and scans. This system could then automatically predict a patient\'s overall survival and how likely they are to survive specifically from bladder cancer. This information could be used by doctors to make better treatment decisions for each patient.
- Conditions
- Bladder Cancer
- Being tested
- Deep learning system for prognostication prediction in bladder cancer
- Keywords
- deep learning, Radiomics, Histopathological tissue slides, Tomography
- Medical subject headings
- Urinary Bladder Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Deep learning system for prognostication prediction in bladder cancerOther
develop and validate a deep learning system for prognostication prediction in bladder cancer based on CT radiomics and whole slide images.
| Study group | Role | What they receive |
|---|---|---|
| BLCA | patients with bladder cancer who had surgery like radical cystectomy or transurethral resection of bladder tumour (TURBT). Receives: Other: Deep learning system for prognostication prediction in bladder cancer |
What is being measured
The primary outcome is the main question the study is designed to answer.
Overall survival
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.
Measured: up to 10 years
Secondary outcomes (1)
Recurrence free survival
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
Measured: up to 10 years
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted29 Apr 2024
- Study start1 Jan 2024
- Primary completion1 Jun 2025 (estimated)
- Study completion1 Oct 2025 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Bladder CancerStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Other
- Time perspective
- Retrospective
- Enrolment
- 1,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Mingzhao Xiao
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor-investigator, Mingzhao Xiao, First Affiliated Hospital of Chongqing Medical University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06389019
- Sponsor's protocol ID
- BLCA_CMUFH
- Other IDs
- K2024-187-01
- Status last verified
- May 2025
- First submitted
- 25 Apr 2024
- Last updated
- 28 May 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: 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.
Study at a glance
From the registry record, last updated by the sponsor on 28 May 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Whole-slide Image and CT Radiomics Based Deep Learning System for Prognostication Prediction in Bladder Cancer
- ClinicalTrials.gov ID
- NCT06389019
- Acronym / sponsor ID
- BLCA_CMUFH
- Conditions
- Bladder Cancer
- Keywords
- deep learning, Radiomics, Histopathological tissue slides, Tomography
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 1,000 participants (estimated)
- Lead sponsor
- Mingzhao XiaoUniversity, hospital or other
- Study start
- 1 Jan 2024 (actual)
- Primary completion
- 1 Jun 2025 (estimated)
- Study completion
- 1 Oct 2025 (estimated)
- First posted
- 29 Apr 2024 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Detailed description
Bladder cancer can be difficult to diagnose and predict outcomes for, as the disease can vary greatly between patients. This research aims to develop a new system that uses artificial intelligence to analyze patient information, including images from surgery and scans. This system could then automatically predict a patient\'s overall survival and how likely they are to survive specifically from bladder cancer. This information could be used by doctors to make better treatment decisions for each patient.
Study design
- Observational model
- Other
- Time perspective
- Retrospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| BLCA | – | Other: Deep learning system for prognostication prediction in bladder cancerpatients with bladder cancer who had surgery like radical cystectomy or transurethral resection of bladder tumour (TURBT). |
Who may qualify
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
- 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.
Key inclusion criteria (4 listed)
- 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
Key exclusion criteria (3 listed)
- patients with a postoperative diagnosis of non-urothelial carcinoma
- poor quality of CT images
- incomplete clinical and follow-up data
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Deep learning system for prognostication prediction in bladder cancerdevelop and validate a deep learning system for prognostication prediction in bladder cancer based on CT radiomics and whole slide images. | Other | BLCA |
Primary outcome
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.
1 secondary outcome
Recurrence free survival
Time frame: up to 10 years
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 28 May 2025.
- Individual participant dataSharing planned: No. 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.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Bladder Cancer, run by Mingzhao Xiao at 1 site in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 4 inclusion and 3 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: looking for participants now, as of the sponsor's last update on 28 May 2025. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact QuanHao He (800-555-5555, 2020120460@stu.cqmu.edu.cn), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Overall survival (time frame: up to 10 years). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 28 May 2025.