Clinical trial · Observational
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
NCT04487938CI-TRIAL-00046174unknownClinicalTrials.gov clinicaltrialsProvenance
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 8, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260908-000001
Summary
Brief summary (as posted)
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
Conditions
Conditions (6)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Erosive Lichen Planus | — | UNRESOLVED | — |
| Oral Cancer | Malignant Lip and Oral Cavity Neoplasm | CURATED_BROADER | 0.80 |
| Oral Erythroplakia | — | UNRESOLVED | — |
| Oral Leukoplakia | — | UNRESOLVED | — |
| Oral Submucous Fibrosis | — | UNRESOLVED | — |
| Proliferative Verrucous Leukoplakia | — | UNRESOLVED | — |
Interventions
Interventions (1)
| Intervention | Type | Mapped drug | Match |
|---|---|---|---|
| No intervention utilised | Other | — | UNRESOLVED |
Design
Arms and outcomes
Arms (2)
- label
- Tobacco use and/or alcohol consumption
- interventionNames
- Other: No intervention utilised
- label
- No tobacco use and/or alcohol consumption
- interventionNames
- Other: No intervention utilised
Primary outcomes (2)
- measure
- Accuracy of machine learning algorithms for predicting high-risk persons
- timeFrame
- 24 months
- description
- 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.
- measure
- Accuracy of machine learning algorithms for discriminating high-risk and low-risk lesions
Eligibility
Eligibility (as posted)
- Sex
- All
- Minimum age
- 45 Years
Show eligibility criteria text
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.
References
Publications (4)
- BACKGROUNDAdeoye J, Choi SW, Thomson P. Bayesian disease mapping and the 'High-Risk' oral cancer population in Hong Kong. J Oral Pathol Med. 2020 Oct;49(9):907-913. doi: 10.1111/jop.13045. Epub 2020 Jun 10. PMID 32450000
- BACKGROUNDAdeoye J, Brennan PA, Thomson P. "Search less, verify more"-Reviewing salivary biomarkers in oral cancer detection. J Oral Pathol Med. 2020 Sep;49(8):711-719. doi: 10.1111/jop.13003. Epub 2020 Mar 5. PMID 32027406
- BACKGROUNDAdeoye J, Chu CS, Choi SW, Thomson P. Oral Cancer Awareness and Individuals' Inclination to Its Screening and Risk Prediction in Hong Kong. J Cancer Educ. 2022 Apr;37(2):439-448. doi: 10.1007/s13187-020-01834-x. PMID 32705524
- BACKGROUNDAdeoye J, Thomson P. Strategies to improve diagnosis and risk assessment for oral cancer patients. Faculty Dental Journal. 2020;11(3):122-7.