Deep Learning in Retinoblastoma Detection and Monitoring.
Deep Learning Computer-aided Detection System for Retinoblastoma Detection and Monitoring.
1 other identifier
observational
200
1 country
1
Brief 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. 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.
Trial Health
Trial Health Score
Automated assessment based on enrollment pace, timeline, and geographic reach
participants targeted
Target at P75+ for all trials
Started Mar 2020
Typical duration for all trials
1 active site
Health score is calculated from publicly available data and should be used for screening purposes only.
Trial Relationships
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Study Timeline
Key milestones and dates
Study Start
First participant enrolled
March 1, 2020
CompletedFirst Submitted
Initial submission to the registry
March 24, 2022
CompletedFirst Posted
Study publicly available on registry
April 1, 2022
CompletedPrimary Completion
Last participant's last visit for primary outcome
May 1, 2022
CompletedStudy Completion
Last participant's last visit for all outcomes
October 1, 2022
CompletedApril 1, 2022
March 1, 2022
2.2 years
March 24, 2022
March 24, 2022
Conditions
Outcome Measures
Primary Outcomes (1)
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
1 week
Study Arms (1)
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.
Interventions
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.
Eligibility Criteria
Retinoblastoma patients undergo standard medical management.
You may qualify if:
- Retinoblastoma patients undergo standard medical management.
You may not qualify if:
- The operators identified images non-assessable for a correct diagnosis, due to reasons such as blur and defocus, and excluded them from further analysis.
Contact the study team to confirm eligibility.
Sponsors & Collaborators
Study Sites (1)
Wen-Bin Wei
Beijing, Beijing Municipality, 100730, China
Related Publications (1)
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.
PMID: 37344582DERIVED
MeSH Terms
Conditions
Condition Hierarchy (Ancestors)
Central Study Contacts
Study Design
- Study Type
- observational
- Observational Model
- COHORT
- Time Perspective
- PROSPECTIVE
- Sponsor Type
- OTHER
- Responsible Party
- PRINCIPAL INVESTIGATOR
- PI Title
- Prof.
Study Record Dates
First Submitted
March 24, 2022
First Posted
April 1, 2022
Study Start
March 1, 2020
Primary Completion
May 1, 2022
Study Completion
October 1, 2022
Last Updated
April 1, 2022
Record last verified: 2022-03