Deep Learning in Retinoblastoma Detection and Monitoring.

Sponsor
Beijing Tongren Hospital (Other)
Overall Status
Recruiting
CT.gov ID
NCT05308043
Collaborator
(none)
200
1
31
6.4

Study Details

Study Description

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.

Condition or Disease Intervention/Treatment Phase
  • Diagnostic Test: Deep learning algorism

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

Study Type:
Observational
Anticipated Enrollment :
200 participants
Observational Model:
Cohort
Time Perspective:
Prospective
Official Title:
Deep Learning Computer-aided Detection System for Retinoblastoma Detection and Monitoring.
Actual Study Start Date :
Mar 1, 2020
Anticipated Primary Completion Date :
May 1, 2022
Anticipated Study Completion Date :
Oct 1, 2022

Arms and Interventions

Arm Intervention/Treatment
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.

Diagnostic Test: Deep learning algorism
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.

Outcome Measures

Primary Outcome Measures

  1. Diagnosis accurcy of deep learning algorism [1 week]

    The diagnosic accurcy of this deep learning algorism is the proportion of true positive and true negative in all evaluated cases

Eligibility Criteria

Criteria

Ages Eligible for Study:
0 Years to 5 Years
Sexes Eligible for Study:
All
Accepts Healthy Volunteers:
No
Inclusion Criteria:
  • Retinoblastoma patients undergo standard medical management.
Exclusion Criteria:
  • The operators identified images non-assessable for a correct diagnosis, due to reasons such as blur and defocus, and excluded them from further analysis.

Contacts and Locations

Locations

Site City State Country Postal Code
1 Wen-Bin Wei Beijing Beijing China 100730

Sponsors and Collaborators

  • Beijing Tongren Hospital

Investigators

None specified.

Study Documents (Full-Text)

None provided.

More Information

Publications

None provided.
Responsible Party:
Wenbin Wei, Prof., Beijing Tongren Hospital
ClinicalTrials.gov Identifier:
NCT05308043
Other Study ID Numbers:
  • AI in retinoblastoma
First Posted:
Apr 1, 2022
Last Update Posted:
Apr 1, 2022
Last Verified:
Mar 1, 2022
Studies a U.S. FDA-regulated Drug Product:
No
Studies a U.S. FDA-regulated Device Product:
No
Additional relevant MeSH terms:

Study Results

No Results Posted as of Apr 1, 2022