Kidney CancerRCCCNNNCT03857373
Renal Cancer Detection Using Convolutional Neural Networks
An observational study of Kidney Cancer, run by Nessn Azawi at 1 site in Denmark.
Trial summary
We aim to experiment and implement various deep learning architectures in order to achieve human-level accuracy in Computer-aided diagnosis (CAD) systems. In particular, we are interested in detecting renal tumors from CT urography scans in this project. Read more about this study
- Who is it for?
- Any age
- Conditions the study is for
- Kidney Cancer
- How many people
- 5,000 participants
- Where
- 1 siteDenmark
- Study dates
- 1 Feb 2019to 1 Jan 2027
- How long will I take part?
- 5 Years
- 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 RCC
Who may be able to join
- All patient with RCC, who underwent surgery
Who cannot join
- Patients with RCC, who did not underwent surgery
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
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
We aim to experiment and implement various deep learning architectures in order to achieve human-level accuracy in Computer-aided diagnosis (CAD) systems. In particular, we are interested in detecting renal tumors from CT urography scans in this project. We would like to classify renal tumor to cancer, non cancer, renal cyst I, renal cyst II, renal cyst III and renal cyst VI, with high sensitivity and low false positive rate using various types of convolutional neural networks (CNN). This task can be considered as the first step in building CAD systems for renal cancer diagnosis. Moreover, by automating this task, we can significantly reduce the time for the radiologists to create large-scale labeled datasets of CT-urography scans.
Read the detailed description
We aim to experiment and implement various deep learning architectures in order to achieve human-level accuracy in Computer-aided diagnosis (CAD) systems. In particular, we are interested in detecting renal tumors from CT urography scans in this project. We would like to classify renal tumor to cancer, non cancer, renal cyst I, renal cyst II, renal cyst III and renal cyst VI, with high sensitivity and low false positive rate using various types of convolutional neural networks (CNN). This task can be considered as the first step in building CAD systems for renal cancer diagnosis. Moreover, by automating this task, we can significantly reduce the time for the radiologists to create large-scale labeled datasets of CT-urography scans.
- Conditions
- Kidney Cancer
- Keywords
- Renal Cancer, Machine Learning
- Medical subject headings
- Kidney Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| Renal Cancer | Patients identified with RCC |
What is being measured
The primary outcome is the main question the study is designed to answer.
Predicting recurrences
Predicting recurrences of RCC
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 posted28 Feb 2019
- Study start1 Feb 2019
- Primary completion1 Jan 2025 (estimated)
- Study completion1 Jan 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Kidney 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)
- Target duration
- 5 Years
Sponsor and investigators
- Lead sponsor
- Nessn Azawi
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor-investigator, Nessn Azawi, Zealand University Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT03857373
- Sponsor's protocol ID
- Zealand_UCRU
- Acronym
- RCCCNN
- Status last verified
- Jan 2024
- First submitted
- 26 Feb 2019
- Last updated
- 30 Jan 2024
- Results posted
- 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
- Renal Cancer Detection Using Convolutional Neural Networks
- ClinicalTrials.gov ID
- NCT03857373
- Acronym / sponsor ID
- RCCCNN / Zealand_UCRU
- Conditions
- Kidney Cancer
- Keywords
- Renal Cancer, Machine Learning
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 5,000 participants (estimated)
- Lead sponsor
- Nessn AzawiUniversity, hospital or other
- Study start
- 1 Feb 2019 (actual)
- Primary completion
- 1 Jan 2025 (estimated)
- Study completion
- 1 Jan 2027 (estimated)
- First posted
- 28 Feb 2019 (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.
We aim to experiment and implement various deep learning architectures in order to achieve human-level accuracy in Computer-aided diagnosis (CAD) systems. In particular, we are interested in detecting renal tumors from CT urography scans in this project. We would like to classify renal tumor to cancer, non cancer, renal cyst I, renal cyst II, renal cyst III and renal cyst VI, with high sensitivity and low false positive rate using various types of convolutional neural networks (CNN). This task can be considered as the first step in building CAD systems for renal cancer diagnosis. Moreover, by automating this task, we can significantly reduce the time for the radiologists to create large-scale labeled datasets of CT-urography scans.
Detailed description
We aim to experiment and implement various deep learning architectures in order to achieve human-level accuracy in Computer-aided diagnosis (CAD) systems. In particular, we are interested in detecting renal tumors from CT urography scans in this project. We would like to classify renal tumor to cancer, non cancer, renal cyst I, renal cyst II, renal cyst III and renal cyst VI, with high sensitivity and low false positive rate using various types of convolutional neural networks (CNN). This task can be considered as the first step in building CAD systems for renal cancer diagnosis. Moreover, by automating this task, we can significantly reduce the time for the radiologists to create large-scale labeled datasets of CT-urography scans.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Target follow-up
- 5 Years
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Renal Cancer | – | –Patients identified with RCC |
Who may qualify
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
- Study population
- Patients with RCC
Key inclusion criteria (1 listed)
- All patient with RCC, who underwent surgery
Key exclusion criteria (1 listed)
- Patients with RCC, who did not underwent surgery
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Predicting recurrences
Time frame: 5 years
Predicting recurrences of RCC
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 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.
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 Kidney Cancer, run by Nessn Azawi 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 1 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: looking for participants now, 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?
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 Nessn Azawi, Phd (004526393034, nesa@regionsjaelland.dk), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Predicting recurrences (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; AzTrials last refreshed its copy on 2 Nov 2026.