Oral Cancer Screening and Education in Hong Kong

• Conditions: Oral Leukoplakia; Oral Submucous Fibrosis; Oral Cancer; Oral Erythroplakia; Proliferative Verrucous Leukoplakia; Erosive Lichen Planus
• Interventions: Other: No intervention utilised

• Registration Number: NCT04487938
• Lead Sponsor: The University of Hong Kong

Brief Summary

This study will be conducted to obtain data on oral cancer risk factors to generate machine learning models with good predictive accuracy for stratifying individuals with high-oral cancer risk and delineating high-risk and low-risk oral lesions. Likewise, this study will seek to provide oral cancer-related health education and training on oral-self-examination for beneficiaries

Detailed Description

Not available

Basic Information
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Recruitment & Eligibility
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Study & Design

Study Timeline

• Status Verified: 2020-07
• Study First Submitted: 2020-07-22
• Study First Submitted QC: 2020-07-22
• Last Update Submitted: 2020-07-22
• Study First Posted: 2020-07-27
• Last Update Posted: 2020-07-27
• Study Start: 2021-08-01
• Primary Completion: 2023-07-31
• Study Completion: 2024-07-31

Recruitment & Eligibility

• Status: UNKNOWN
• Sex: All
• Target Recruitment: 3190

Inclusion Criteria

• Healthy individuals satisfying age and residential area criteria with no previous history of oral cancer. Individuals with a history of other cancers will be included in the study provided they have been in remission for more than three years.

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Exclusion Criteria

• Participants with reduced mouth opening (irrespective of the cause) to permit proper administration of VOE or photosensitive epilepsy will be excluded. Likewise, those who decline the provision of written consent or participation in any part of the study.

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Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Arm && Interventions

Group	Intervention	Description
Tobacco use and/or alcohol consumption	No intervention utilised	-
No tobacco use and/or alcohol consumption	No intervention utilised	-

Primary Outcome Measures

Name	Time	Method
Accuracy of machine learning algorithms for predicting high-risk persons	24 months	Predictive accuracy of the ML classifiers for forecasting individuals with or likely to develop high-risk lesions within 24 months of first screening encounter based on demographic and lifestyle information.
Accuracy of machine learning algorithms for discriminating high-risk and low-risk lesions	24 months	Predictive accuracy of ML classifiers for classifying high-risk and low-risk lesions based on demographic and lifestyle risk factors, oral high-risk HPV status, and salivary DNA hypermethylation levels.