Muscle-invasive Bladder CancerNCT06362330
Multi-parametric MRI in Patients of Bladder Cancer
An observational study of Muscle-invasive Bladder Cancer and Artificial Intelligence, run by The First Affiliated Hospital with Nanjing Medical University at 1 site in China.
Trial summary
Accurate preoperative detection of muscle-invasive bladder cancer remains a clinical challenge. The investigators aimed to develop and validate a knowledge-guided causal diagnostic network for the detection of muscle-invasive bladder cancer with multiparametric magnetic resonance imaging(MRI). Read more about this study
- Who is it for?
- Any age
- Conditions the study is for
- Muscle-invasive Bladder Cancer, Artificial Intelligence
- What is being tested?
- magnetic resonance imaging
- How many people
- 1,000 participants
- Where
- 1 siteChina
- Study dates
- 1 Jul 2021to 30 Jun 2024
- Phase
- Observational
- Are there results?
- None posted in the registry
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: Preoperative multi-parameter magnetic resonance imaging is essential to ascertain whether patients with bladder cancer exhibit muscular invasion, facilitating the selection of appropriate treatment options.
Who may be able to join
- Urothelial carcinoma of the bladder confirmed by final histopathology ②Received a standard contrast-enhanced 3.0T mpMRI before surgery ③All tumors within patients included were resected and received pathologic examination separately
Who cannot join
- ①Absence of surgical interventions
- ②With inadequate image quality or with inadequate pathology for analysis
Trial locations and contacts
1 site in 1 country.
China1 site, 1 recruiting
- Recruiting
Yu-Dong Zhang
Nanjing, 210029
Yu-Dong Zhang, MD;PHD, 15805151704, njmu_zyd@163.com
About this study
Official title: Knowledge-guided Causal Diagnostic Network for the Detection of Muscle-invasive Bladder Cancer With Single T2-weighted Imaging
Accurate preoperative detection of muscle-invasive bladder cancer remains a clinical challenge. The investigators aimed to develop and validate a knowledge-guided causal diagnostic network for the detection of muscle-invasive bladder cancer with multiparametric magnetic resonance imaging(MRI).
Read the detailed description
Patients who underwent bladder MRI were retrospectively collected at three centers between January 2013 and September 2023. The investigators first constructed a nnUNet to segment causal region where muscle-invasive bladder cancer may occur. Subsequently, the investigators explored a causal network based on a modified ResNet3d-18 by striking a fine balance between nnUNet awareness and a self-supervised learning (SSL) model, which steered model to emulate diagnostic acumen of expert in staging muscle-invasive bladder cancer at MRI. Model was trained in center 1, and independently tested in center 1, center 2 and center 3. Ablation test was performed among all 13 Ablation-Test models using either single or multi-parametric MRI. Benefit was tested in six radiologists using vesical imaging-reporting and data system (VI-RADS) versus network-adjusted VI-RADS.
- Being tested
- magnetic resonance imaging
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
magnetic resonance imagingOther
Patients of bladder cancer underwent multiparameter magnetic resonance imaging before surgery
| Study group | Role | What they receive |
|---|---|---|
| muscle-invasive bladder cancer | The postoperative pathology was muscle-invasive bladder cancer Receives: Other: magnetic resonance imaging | |
| non-muscle-invasive bladder cancer | The postoperative pathology was non-muscle-invasive bladder cancer Receives: Other: magnetic resonance imaging |
What is being measured
The primary outcome is the main question the study is designed to answer.
Muscle-invasive bladder cancer
The artificial intelligence diagnosis results, based on preoperative MRI, indicated muscle-invasive bladder cancer. Subsequently, this preoperative diagnosis was compared with the postoperative pathological diagnosis to evaluate the diagnostic performance of the artificial intelligence.
Measured: one month
Non-muscle-invasive bladder cancer
The artificial intelligence diagnosis results, based on preoperative MRI, indicated non-muscle-invasive bladder cancer. Subsequently, this preoperative diagnosis was compared with the postoperative pathological diagnosis to evaluate the diagnostic performance of the artificial intelligence.
Measured: one month
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted12 Apr 2024
- Study start1 Jul 2021
- Primary completion30 May 2024 (estimated)
- Study completion30 Jun 2024 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Muscle-invasive Bladder CancerStudies of the same condition, open and under way.
- More trials run by The First Affiliated Hospital with Nanjing Medical UniversityThe sponsor's other registered studies.
- 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
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06362330
- Sponsor's protocol ID
- 2022-SR-471
- Status last verified
- Apr 2024
- First submitted
- 6 Apr 2024
- Last updated
- 12 Apr 2024
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 12 Apr 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Knowledge-guided Causal Diagnostic Network for the Detection of Muscle-invasive Bladder Cancer With Single T2-weighted Imaging
- ClinicalTrials.gov ID
- NCT06362330
- Acronym / sponsor ID
- 2022-SR-471
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 1,000 participants (estimated)
- Lead sponsor
- The First Affiliated Hospital with Nanjing Medical UniversityUniversity, hospital or other
- Study start
- 1 Jul 2021 (actual)
- Primary completion
- 30 May 2024 (estimated)
- Study completion
- 30 Jun 2024 (estimated)
- First posted
- 12 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.
Accurate preoperative detection of muscle-invasive bladder cancer remains a clinical challenge. The investigators aimed to develop and validate a knowledge-guided causal diagnostic network for the detection of muscle-invasive bladder cancer with multiparametric magnetic resonance imaging(MRI).
Detailed description
Patients who underwent bladder MRI were retrospectively collected at three centers between January 2013 and September 2023. The investigators first constructed a nnUNet to segment causal region where muscle-invasive bladder cancer may occur. Subsequently, the investigators explored a causal network based on a modified ResNet3d-18 by striking a fine balance between nnUNet awareness and a self-supervised learning (SSL) model, which steered model to emulate diagnostic acumen of expert in staging muscle-invasive bladder cancer at MRI. Model was trained in center 1, and independently tested in center 1, center 2 and center 3. Ablation test was performed among all 13 Ablation-Test models using either single or multi-parametric MRI. Benefit was tested in six radiologists using vesical imaging-reporting and data system (VI-RADS) versus network-adjusted VI-RADS.
Study design
- Observational model
- Other
- Time perspective
- Retrospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| muscle-invasive bladder cancer | – | Other: magnetic resonance imagingThe postoperative pathology was muscle-invasive bladder cancer |
| non-muscle-invasive bladder cancer | – | Other: magnetic resonance imagingThe postoperative pathology was non-muscle-invasive bladder cancer |
Who may qualify
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
- Study population
- Preoperative multi-parameter magnetic resonance imaging is essential to ascertain whether patients with bladder cancer exhibit muscular invasion, facilitating the selection of appropriate treatment options.
Key inclusion criteria (1 listed)
- Urothelial carcinoma of the bladder confirmed by final histopathology ②Received a standard contrast-enhanced 3.0T mpMRI before surgery ③All tumors within patients included were resected and received pathologic examination separately
Key exclusion criteria (2 listed)
- ①Absence of surgical interventions
- ②With inadequate image quality or with inadequate pathology for analysis
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 |
|---|---|---|
| magnetic resonance imagingPatients of bladder cancer underwent multiparameter magnetic resonance imaging before surgery | Other | muscle-invasive bladder cancer, non-muscle-invasive bladder cancer |
Primary outcome
Muscle-invasive bladder cancer
Time frame: one month
The artificial intelligence diagnosis results, based on preoperative MRI, indicated muscle-invasive bladder cancer. Subsequently, this preoperative diagnosis was compared with the postoperative pathological diagnosis to evaluate the diagnostic performance of the artificial intelligence.
Non-muscle-invasive bladder cancer
Time frame: one month
The artificial intelligence diagnosis results, based on preoperative MRI, indicated non-muscle-invasive bladder cancer. Subsequently, this preoperative diagnosis was compared with the postoperative pathological diagnosis to evaluate the diagnostic performance of the artificial intelligence.
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 12 Apr 2024.
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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. This study is not recruiting at the moment.
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 Muscle-invasive Bladder Cancer and Artificial Intelligence, run by The First Affiliated Hospital with Nanjing 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 1 inclusion and 2 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: status not confirmed, as of the sponsor's last update on 12 Apr 2024. 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?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Each site lists its own contact: choose the nearest one under Trial locations. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Muscle-invasive bladder cancer (time frame: one month). 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?
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 12 Apr 2024.