Bladder CancerNCT06092450
Deep Learning Radiomics Model for Predicting Post-cystectomy Outcome in Muscle Invasive Bladder Cancer
An observational study of Bladder Cancer, run by First Affiliated Hospital of Chongqing Medical University at 1 site in China.
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. Read more about this study
- Who is it for?
- Any age
- Conditions the study is for
- Bladder Cancer
- What is being tested?
- develop and validate a deep learning radiomics model based on preoperative enhanced CT image
- How many people
- 500 participants
- Where
- 1 siteChina
- Study dates
- 1 Aug 2023to 1 Jun 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: patients with pathologically confirmed MIBC who underwent radical cystectomy
Who may be able to join
- patients with pathologically confirmed MIBC after radical cystectomy;
- contrast-CT scan less than two weeks before surgery;
- complete CT image data and clinical data.
Who cannot join
- patients who received neoadjuvant therapy;
- 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
Zongjie Wei, 023-89012557, wzj9846@163.com
About this study
Official title: 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.
- Conditions
- Bladder Cancer
- Being tested
- develop and validate a deep learning radiomics model based on preoperative enhanced CT image
- Keywords
- Tomography, X-ray computed, Muscle-invasive bladder cancer, Radiomics, Deep Learning
- 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.
develop and validate a deep learning radiomics model based on preoperative enhanced CT imageOther
develop and validate a deep learning radiomics model based on preoperative enhanced CT to predict postoperative survival in MIBC
| Study group | Role | What they receive |
|---|---|---|
| MIBC | patients with pathologically confirmed MIBC after radical cystectomy Receives: Other: develop and validate a deep learning radiomics model based on preoperative enhanced CT image |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: 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.
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 posted23 Oct 2023
- Study start1 Aug 2023
- Primary completion1 Jun 2025 (estimated)
- Study completion1 Jun 2025 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (1)Papers the sponsor has linked to this study.
- 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
- Cohort
- Time perspective
- Retrospective
- Enrolment
- 500 participants (estimated)
Sponsor and investigators
- Lead sponsor
- First Affiliated Hospital of Chongqing Medical University
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Mingzhao Xiao, First Affiliated Hospital of Chongqing Medical University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06092450
- Sponsor's protocol ID
- AI-BLCA
- Other IDs
- 2022-K508
- Status last verified
- May 2025
- First submitted
- 12 Oct 2023
- Last updated
- 31 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.
Publications
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.
PubMed 38349205
Study at a glance
From the registry record, last updated by the sponsor on 31 May 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Deep Learning Radiomics Model for Predicting Post-cystectomy Outcome From Preoperative CT in Muscle Invasive Bladder Cancer
- ClinicalTrials.gov ID
- NCT06092450
- Acronym / sponsor ID
- AI-BLCA
- Conditions
- Bladder Cancer
- Keywords
- Tomography, X-ray computed, Muscle-invasive bladder cancer, Radiomics, Deep Learning
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 500 participants (estimated)
- Lead sponsor
- First Affiliated Hospital of Chongqing Medical UniversityUniversity, hospital or other
- Study start
- 1 Aug 2023 (actual)
- Primary completion
- 1 Jun 2025 (estimated)
- Study completion
- 1 Jun 2025 (estimated)
- First posted
- 23 Oct 2023 (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.
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 design
- Observational model
- Cohort
- Time perspective
- Retrospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| MIBC | – | Other: develop and validate a deep learning radiomics model based on preoperative enhanced CT imagepatients with pathologically confirmed MIBC after radical cystectomy |
Who may qualify
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
- Study population
- patients with pathologically confirmed MIBC who underwent radical cystectomy
Key inclusion criteria (3 listed)
- patients with pathologically confirmed MIBC after radical cystectomy;
- contrast-CT scan less than two weeks before surgery;
- complete CT image data and clinical data.
Key exclusion criteria (4 listed)
- patients who received neoadjuvant therapy;
- 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 |
|---|---|---|
| develop and validate a deep learning radiomics model based on preoperative enhanced CT imagedevelop and validate a deep learning radiomics model based on preoperative enhanced CT to predict postoperative survival in MIBC | Other | MIBC |
Primary outcome
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.
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.
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 31 May 2025.
- Derived from this study: PubMed 38349205Wei 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.
- 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 First Affiliated Hospital of Chongqing Medical University 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 3 inclusion and 4 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 31 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 Zongjie Wei (023-89012557, wzj9846@163.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Overall survival（OS） (time frame: up to 10 years). The study lists 2 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT06092450, which also lists 1 linked publication.
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 31 May 2025.