Application of artificial intelligence and machine learning for prediction of oral cancer risk

2021 ◽  
Author(s):  
Anwar Alhazmi ◽  
Yaser Alhazmi ◽  
Ali Makrami ◽  
Amal Masmali ◽  
Nourah Salawi ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Oral Cancer ◽  
Cancer Risk ◽  
Oral Cancer Risk

scholarly journals Risk-Based Selection of Individuals for Oral Cancer Screening

2021 ◽  
Vol 39 (6) ◽  
pp. 663-674
Author(s):  
Li C. Cheung ◽  
Kunnambath Ramadas ◽  
Richard Muwonge ◽  
Hormuzd A. Katki ◽  
Gigi Thomas ◽  
...  
Keyword(s):  
Cancer Screening ◽  
Oral Cancer ◽  
Cancer Risk ◽  
Prediction Model ◽  
Risk Prediction ◽  
Cancer Mortality ◽  
Mortality Reduction ◽  
Oral Cancer Screening ◽  
Oral Cancer Risk ◽  
Alcohol Users

PURPOSE: We evaluated proof of principle for resource-efficient, risk-based screening through reanalysis of the Kerala Oral Cancer Screening Trial. METHODS: The cluster-randomized trial included three triennial rounds of visual inspection (seven clusters, n = 96,516) versus standard of care (six clusters, n = 95,354) and up to 9 years of follow-up. We developed a Cox regression–based risk prediction model for oral cancer incidence. Using this risk prediction model to adjust for the oral cancer risk imbalance between arms, through intention-to-treat (ITT) analyses that accounted for cluster randomization, we calculated the relative (hazard ratios [HRs]) and absolute (rate differences [RDs]) screening efficacy on oral cancer mortality and compared screening efficiency across risk thresholds. RESULTS: Oral cancer mortality was reduced by 27% in the screening versus control arms (HR = 0.73; 95% CI, 0.54 to 0.98), including a 29% reduction in ever-tobacco and/or ever-alcohol users (HR = 0.71; 95% CI, 0.51 to 0.99). This relative efficacy was similar across oral cancer risk quartiles ( P interaction = .59); consequently, the absolute efficacy increased with increasing model-predicted risk—overall trial: RD in the lowest risk quartile (Q1) = 0.5/100,000 versus 13.4/100,000 in the highest quartile (Q4), P trend = .059 and ever-tobacco and/or ever-alcohol users: Q1 RD = 1.0/100,000 versus Q4 = 22.5/100,000; P trend = .026. In a population akin to the Kerala trial, screening of 100% of individuals would provide 27.1% oral cancer mortality reduction at number needed to screen (NNS) = 2,043. Restriction of screening to ever-tobacco and/or ever-alcohol users with no additional risk stratification would substantially enhance efficiency (43.4% screened for 23.3% oral cancer mortality reduction at NNS = 1,029), whereas risk prediction model–based screening of 50% of ever-tobacco and/or ever-alcohol users at highest risk would further enhance efficiency with little loss in program sensitivity (21.7% screened for 19.7% oral cancer mortality reduction at NNS = 610). CONCLUSION: In the Kerala trial, the efficacy of oral cancer screening was greatest in individuals at highest oral cancer risk. These results provide proof of principle that risk-based oral cancer screening could substantially enhance the efficiency of screening programs.

Alcohol, tobacco and paan use and understanding of oral cancer risk among Asian males in Leicester

BDJ ◽  
2000 ◽  
Vol 188 (08) ◽  
pp. 444-451 ◽  
Author(s):  
A. Vora ◽  
C. Yeoman ◽  
J. Hayter
Keyword(s):  
Oral Cancer ◽  
Cancer Risk ◽  
Oral Cancer Risk

Mouthwash and oral cancer risk: An update

Oral Oncology ◽  
2009 ◽  
Vol 45 (3) ◽  
pp. 198-200 ◽  
Author(s):  
Carlo La Vecchia
Keyword(s):  
Oral Cancer ◽  
Cancer Risk ◽  
Oral Cancer Risk

scholarly journals Glutathione S-transferasesP1 AA (105Ile) allele increases oral cancer risk, interacts strongly with c-Jun Kinase and weakly detoxifies areca-nut metabolites

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Pallavi Yadav ◽  
Atanu Banerjee ◽  
Nabamita Boruah ◽  
Chongtham Sovachandra Singh ◽  
Puja Chatterjee ◽  
...  
Keyword(s):  
Oral Cancer ◽  
Cancer Risk ◽  
Areca Nut ◽  
Jun Kinase ◽  
Oral Cancer Risk
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