Bladder CancerBLAInosticNCT05193656
Bladder Cancer Detection Using Convolutional Neural Networks
An observational study of Bladder Cancer, run by Zealand University Hospital at 1 site in Denmark.
Trial summary
The investigators aim to experiment and implement various deep learning architectures to achieve human-level accuracy in Computer-aided diagnosis (CAD) systems. In particular, the investigators are interested in detecting bladder tumors from CT urography scans and cystoscopies of the bladder in this project. Read more about this study
- Who is it for?
- Any age
- Conditions the study is for
- Bladder Cancer
- What is being tested?
- Al_bladder
- How many people
- 5,000 participants
- Where
- 1 siteDenmark
- Study dates
- 1 Jun 2021to 1 Jun 2026
- 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: Patients with micro or macroscopic hematuria
Who may be able to join
- Patients with first time hematuria
- Patients with the control program for previous bladder cancer
Who cannot join
- Patients with control cystoscope for noncancer suspected disease
Trial locations and contacts
1 site in 1 country.
Denmark1 site, 1 recruiting
- Recruiting
Zealand University Hospital
Roskilde, 4000
Nessn H. Azawi, M.D., 004526393034, nesa@regionsjaelland.dk
Nessn Azawi, Ph.D
About this study
The investigators aim to experiment and implement various deep learning architectures to achieve human-level accuracy in Computer-aided diagnosis (CAD) systems. In particular, the investigators are interested in detecting bladder tumors from CT urography scans and cystoscopies of the bladder in this project.
Read the detailed description
The investigators aim to experiment and implement various deep learning architectures to achieve human-level accuracy in Computer-aided diagnosis (CAD) systems. In particular, the investigators are interested in detecting bladder tumors from CT urography scans and cystoscopies of the bladder in this project. The investigators want to classify bladder tumors as cancer, non cancer, high grade and low grade, invasive and non-invasive, with high sensitivity and low false positive rate using various convolutional neural networks (CNN). This task can be considered as the first step in building CAD systems for bladder cancer diagnosis. Moreover, by automating this task, the investigator scan significantly reduce the time for the radiologists to create large-scale labeled datasets of CT-urography scans and reduce the false-negative and positive that can happen due to human evaluation cystoscopies.
- Conditions
- Bladder Cancer
- Being tested
- Al_bladder
- Keywords
- Machine learning, Artificial intelligence
- 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.
Al_bladderDiagnostic test
Detection of bladder tumor with help of Artificial intelligence
| Study group | Role | What they receive |
|---|---|---|
| Detecting bladder tumor | Patients with hematuria, or previous bladder tumor Receives: Diagnostic Test: Al_bladder |
What is being measured
The primary outcome is the main question the study is designed to answer.
Comparing standard technique to Machine Learning
The accuracy of Machine learning to detect bladder cancer compared to standard cystoscopy
Measured: 5 years
Secondary outcomes (1)
Detecting accuracy of subtypes of bladder cancer
The abelity of Machine Learning to identify high grad bladder cancer from low grad bladder cancer
Measured: 5 years
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted18 Jan 2022
- Study start1 Jun 2021
- Primary completion1 Jun 2026 (estimated)
- Study completion1 Jun 2026 (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
- Cohort
- Time perspective
- Prospective
- Enrolment
- 5,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Zealand University Hospital
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
- Investigators
- Nessn Azawi, phd, Principal investigator, Zealand University Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05193656
- Sponsor's protocol ID
- SJ-905
- Acronym
- BLAInostic
- Status last verified
- Jan 2024
- First submitted
- 17 Nov 2021
- Last updated
- 30 Jan 2024
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 30 Jan 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Bladder Cancer Detection Using Convolutional Neural Networks
- ClinicalTrials.gov ID
- NCT05193656
- Acronym / sponsor ID
- BLAInostic / SJ-905
- Conditions
- Bladder Cancer
- Keywords
- Machine learning, Artificial intelligence
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 5,000 participants (estimated)
- Lead sponsor
- Zealand University HospitalUniversity, hospital or other
- Study start
- 1 Jun 2021 (actual)
- Primary completion
- 1 Jun 2026 (estimated)
- Study completion
- 1 Jun 2026 (estimated)
- First posted
- 18 Jan 2022 (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.
The investigators aim to experiment and implement various deep learning architectures to achieve human-level accuracy in Computer-aided diagnosis (CAD) systems. In particular, the investigators are interested in detecting bladder tumors from CT urography scans and cystoscopies of the bladder in this project.
Detailed description
The investigators aim to experiment and implement various deep learning architectures to achieve human-level accuracy in Computer-aided diagnosis (CAD) systems. In particular, the investigators are interested in detecting bladder tumors from CT urography scans and cystoscopies of the bladder in this project. The investigators want to classify bladder tumors as cancer, non cancer, high grade and low grade, invasive and non-invasive, with high sensitivity and low false positive rate using various convolutional neural networks (CNN). This task can be considered as the first step in building CAD systems for bladder cancer diagnosis. Moreover, by automating this task, the investigator scan significantly reduce the time for the radiologists to create large-scale labeled datasets of CT-urography scans and reduce the false-negative and positive that can happen due to human evaluation cystoscopies.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Detecting bladder tumor | – | Diagnostic Test: Al_bladderPatients with hematuria, or previous bladder tumor |
Who may qualify
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
- Study population
- Patients with micro or macroscopic hematuria
Key inclusion criteria (2 listed)
- Patients with first time hematuria
- Patients with the control program for previous bladder cancer
Key exclusion criteria (1 listed)
- Patients with control cystoscope for noncancer suspected disease
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 |
|---|---|---|
| Al_bladderDetection of bladder tumor with help of Artificial intelligence | Diagnostic test | Detecting bladder tumor |
Primary outcome
Comparing standard technique to Machine Learning
Time frame: 5 years
The accuracy of Machine learning to detect bladder cancer compared to standard cystoscopy
1 secondary outcome
Detecting accuracy of subtypes of bladder cancer
Time frame: 5 years
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 30 Jan 2024.
- Individual participant dataSharing planned: No
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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.
Nessn Azawi, phd principal investigator
Zealand University Hospital
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 Zealand University Hospital at 1 site in Denmark.
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 2 inclusion and 1 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 30 Jan 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 Denmark. 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?
Contact Nessn Azawi, phd (26393034, nesa@regionsjaelland.dk), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Comparing standard technique to Machine Learning (time frame: 5 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 30 Jan 2024.