Deep Learning in Retinoblastoma Detection and Monitoring.

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.

Study Overview

• Status: Recruiting
• Conditions: Retinoblastoma
• Intervention / Treatment: 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 Type: Observational
• Enrollment (Anticipated): 200

Contacts and Locations

• Study Contact: Name: Wenbin Wei, MD; Phone Number: 010-58269523; Email: weiwenbintr@163.com
• Study Contact Backup: Name: Ruiheng Zhang, MD; Email: zhangruihengsy@outlook.com

Study Locations

• China
  • Beijing
    • Beijing, Beijing, China, 100730
      • Recruiting
      • Wen-Bin Wei

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 1 second to 5 years (CHILD)
• Accepts Healthy Volunteers: No
• Genders Eligible for Study: All

• Sampling Method: Non-Probability Sample

Study Population

Retinoblastoma patients undergo standard medical management.

Description

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.

Study Plan

How is the study designed?

Number of groups / cohorts

1

Cohorts and Interventions

Group / Cohort	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.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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

Collaborators and Investigators

• Sponsor: Beijing Tongren Hospital

Study record dates

Study Major Dates

• Study Start (ACTUAL): March 1, 2020
• Primary Completion (ANTICIPATED): May 1, 2022
• Study Completion (ANTICIPATED): October 1, 2022

Study Registration Dates

• First Submitted: March 24, 2022
• First Submitted That Met QC Criteria: March 24, 2022
• First Posted (ACTUAL): April 1, 2022

Study Record Updates

• Last Update Posted (ACTUAL): April 1, 2022
• Last Update Submitted That Met QC Criteria: March 24, 2022
• Last Verified: March 1, 2022

More Information

Terms related to this study

• Additional Relevant MeSH Terms: Neoplasms by Histologic Type; Neoplasms; Neoplasms by Site; Neoplasms, Glandular and Epithelial; Eye Diseases; Retinal Diseases; Neoplasms, Neuroepithelial; Neuroectodermal Tumors; Neoplasms, Germ Cell and Embryonal; Neoplasms, Nerve Tissue; Eye Diseases, Hereditary; Eye Neoplasms; Retinal Neoplasms; Retinoblastoma
• Other Study ID Numbers: AI in retinoblastoma

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No