Oral Cancer Screening and Education in Hong Kong

THE HONG KONG ORAL CANCER EDUCATION AND SCREENING (HOCES) PROGRAM: REFINING DISEASE PREVENTION, RISK STRATIFICATION AND EARLY DETECTION

Status

UNKNOWN

Phases

Unknown

Study type

Observational

Source

ClinicalTrials.gov

Registry ID

NCT04487938

Enrollment

3190

Registered

2020-07-27

Start date

2021-08-01

Completion date

2024-07-31

Last updated

2020-07-27

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Erosive Lichen Planus, Oral Cancer, Oral Erythroplakia, Oral Leukoplakia, Oral Submucous Fibrosis, Proliferative Verrucous Leukoplakia

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

Interventions

OTHERNo intervention utilised

No intervention utilised

Study design

Observational model

COHORT

Time perspective

PROSPECTIVE

Eligibility

Sex/Gender

ALL

Age

45 Years to No maximum

Healthy volunteers

Yes

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.

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.

Design outcomes

Primary

Measure	Time frame	Description
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.

Contacts

Primary ContactJohn Adeoye

jaadeoye@hku.hk56441784

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: Feb 4, 2026