RetinoblastomaNCT05308043
Deep Learning in Retinoblastoma Detection and Monitoring.
An observational study of Retinoblastoma, run by Beijing Tongren Hospital at 1 site in China.
Trial summary
Retinoblastoma is the most common eye cancer of childhood. Eye-preserving therapies require routine monitoring of retinoblastoma regression and recurrence to guide corresponding treatment. Read more about this study
- Who is it for?
- Ages 0 to 5 years
- Conditions the study is for
- Retinoblastoma
- What is being tested?
- Deep learning algorism
- How many people
- 200 participants
- Where
- 1 siteChina
- Study dates
- 1 Mar 2020to 1 Oct 2022
- 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 0 to 5 years
- All sexes
- No healthy volunteers
- Child
Study population: Retinoblastoma patients undergo standard medical management.
Who may be able to join
- Retinoblastoma patients undergo standard medical management.
Who cannot join
- The operators identified images non-assessable for a correct diagnosis, due to reasons such as blur and defocus, and excluded them from further analysis.
Trial locations and contacts
1 site in 1 country.
Ruiheng Zhang, MD contact
China1 site, 1 recruiting
- Recruiting
Wen-Bin Wei
Beijing, Beijing Municipality, 100730
Wen-Bin Wei, MD, weiwenbintr@163.com
Wen-Bin Wei, MD
About this study
Official title: Deep Learning Computer-aided Detection System for Retinoblastoma Detection and Monitoring.
Retinoblastoma is the most common eye cancer of childhood. Eye-preserving therapies require routine monitoring of retinoblastoma regression and recurrence to guide corresponding treatment. In the current study, we develop a deep learning algorism that can simultaneously identify retinoblastoma tumours on Retcam images and distinguish between active and inactive retinoblastoma tumours. This algorism will be validated through a prospectively collected dataset.
Read the detailed description
Retinoblastoma, the most common eye cancer of childhood, affects 1 in 15 000 to 1 in 18 000 live births. China has the second-largest number of patients with retinoblastoma in the world. Eye-preserving therapies have been used widely in China for approximately 15 years. Eye-preserving therapies require routine monitoring of retinoblastoma regression and recurrence to guide corresponding treatment. However, the major amount of qualified ophthalmologists are concentrated in several medical centres. Deep learning based on Retcam examination that can identify retinoblastoma will reduce screening accuracy of the local hospitals and reduce monitoring wordload. In the current study, a deep learning algorism was developed that can simultaneously identify retinoblastoma tumours on Retcam images and distinguish between active and inactive retinoblastoma tumours. This algorism will be validated through a prospectively collected dataset.
- Conditions
- Retinoblastoma
- Being tested
- Deep learning algorism
- Medical subject headings
- Retinoblastoma
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Deep learning algorismDiagnostic test
A deep learning algorism that was developed previous would be applied to identify retinoblastoma tumours on Retcam images and distinguish between active and inactive retinoblastoma tumours. The decision of two different senior ophthalmologists would be the gold standard.
| Study group | Role | What they receive |
|---|---|---|
| Retinoblastoma patients | Retinoblastoma patients who undergo standard medical care in Beijing Tongren Hospital. The anonymous image of these patients will be prospectively collected and labelled by senior ophthalmologists. Receives: Diagnostic Test: Deep learning algorism |
What is being measured
The primary outcome is the main question the study is designed to answer.
Diagnosis accurcy of deep learning algorism
The diagnosic accurcy of this deep learning algorism is the proportion of true positive and true negative in all evaluated cases
Measured: 1 week
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted1 Apr 2022
- Study start1 Mar 2020
- Primary completion1 May 2022 (estimated)
- Study completion1 Oct 2022 (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 RetinoblastomaStudies of the same condition, open and under way.
- More trials run by Beijing Tongren HospitalThe 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
- Cohort
- Time perspective
- Prospective
- Enrolment
- 200 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Beijing Tongren Hospital
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Wenbin Wei, Beijing Tongren Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05308043
- Sponsor's protocol ID
- AI in retinoblastoma
- Status last verified
- Mar 2022
- First submitted
- 24 Mar 2022
- Last updated
- 1 Apr 2022
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Publications
Zhang R, Dong L, Li R, Zhang K, Li Y, Zhao H, Shi J, Ge X, Xu X, Jiang L, Shi X, Zhang C, Zhou W, Xu L, Wu H, Li H, Yu C, Li J, Ma J, Wei W. Automatic retinoblastoma screening and surveillance using deep learning. Br J Cancer. 2023 Aug;129(3):466-474. doi: 10.1038/s41416-023-02320-z. Epub 2023 Jun 21.
PubMed 37344582
Study at a glance
From the registry record, last updated by the sponsor on 1 Apr 2022. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Deep Learning Computer-aided Detection System for Retinoblastoma Detection and Monitoring.
- ClinicalTrials.gov ID
- NCT05308043
- Acronym / sponsor ID
- AI in retinoblastoma
- Conditions
- Retinoblastoma
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 200 participants (estimated)
- Lead sponsor
- Beijing Tongren HospitalUniversity, hospital or other
- Study start
- 1 Mar 2020 (actual)
- Primary completion
- 1 May 2022 (estimated)
- Study completion
- 1 Oct 2022 (estimated)
- First posted
- 1 Apr 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.
Retinoblastoma is the most common eye cancer of childhood. Eye-preserving therapies require routine monitoring of retinoblastoma regression and recurrence to guide corresponding treatment. In the current study, we develop a deep learning algorism that can simultaneously identify retinoblastoma tumours on Retcam images and distinguish between active and inactive retinoblastoma tumours. This algorism will be validated through a prospectively collected dataset.
Detailed description
Retinoblastoma, the most common eye cancer of childhood, affects 1 in 15 000 to 1 in 18 000 live births. China has the second-largest number of patients with retinoblastoma in the world. Eye-preserving therapies have been used widely in China for approximately 15 years. Eye-preserving therapies require routine monitoring of retinoblastoma regression and recurrence to guide corresponding treatment. However, the major amount of qualified ophthalmologists are concentrated in several medical centres. Deep learning based on Retcam examination that can identify retinoblastoma will reduce screening accuracy of the local hospitals and reduce monitoring wordload. In the current study, a deep learning algorism was developed that can simultaneously identify retinoblastoma tumours on Retcam images and distinguish between active and inactive retinoblastoma tumours. This algorism will be validated through a prospectively collected dataset.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Retinoblastoma patients | – | Diagnostic Test: Deep learning algorismRetinoblastoma patients who undergo standard medical care in Beijing Tongren Hospital. The anonymous image of these patients will be prospectively collected and labelled by senior ophthalmologists. |
Who may qualify
- Ages
- 0 to 5 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child
- Study population
- Retinoblastoma patients undergo standard medical management.
Key inclusion criteria (1 listed)
- Retinoblastoma patients undergo standard medical management.
Key exclusion criteria (1 listed)
- The operators identified images non-assessable for a correct diagnosis, due to reasons such as blur and defocus, and excluded them from further 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 |
|---|---|---|
| Deep learning algorismA deep learning algorism that was developed previous would be applied to identify retinoblastoma tumours on Retcam images and distinguish between active and inactive retinoblastoma tumours. The decision of two different senior ophthalmologists would be the gold standard. | Diagnostic test | Retinoblastoma patients |
Primary outcome
Diagnosis accurcy of deep learning algorism
Time frame: 1 week
The diagnosic accurcy of this deep learning algorism is the proportion of true positive and true negative in all evaluated cases
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 1 Apr 2022.
- Derived from this study: PubMed 37344582Zhang R, Dong L, Li R, Zhang K, Li Y, Zhao H, Shi J, Ge X, Xu X, Jiang L, Shi X, Zhang C, Zhou W, Xu L, Wu H, Li H, Yu C, Li J, Ma J, Wei W. Automatic retinoblastoma screening and surveillance using deep learning. Br J Cancer. 2023 Aug;129(3):466-474. doi: 10.1038/s41416-023-02320-z. Epub 2023 Jun 21.
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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.
Ruiheng Zhang, MD contact
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 Retinoblastoma, run by Beijing Tongren Hospital 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 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 1 Apr 2022. 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?
Contact Wenbin Wei, MD (010-58269523, weiwenbintr@163.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Diagnosis accurcy of deep learning algorism (time frame: 1 week). 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 NCT05308043, 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 1 Apr 2022.