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 ◽  
...  

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.

Cancers ◽  
2021 ◽  
Vol 13 (14) ◽  
pp. 3496
Author(s):  
Yohwan Yeo ◽  
Dong Wook Shin ◽  
Kyungdo Han ◽  
Sang Hyun Park ◽  
Keun-Hye Jeon ◽  
...  

Early detection of lung cancer by screening has contributed to reduce lung cancer mortality. Identifying high risk subjects for lung cancer is necessary to maximize the benefits and minimize the harms followed by lung cancer screening. In the present study, individual lung cancer risk in Korea was presented using a risk prediction model. Participants who completed health examinations in 2009 based on the Korean National Health Insurance (KNHI) database (DB) were eligible for the present study. Risk scores were assigned based on the adjusted hazard ratio (HR), and the standardized points for each risk factor were calculated to be proportional to the b coefficients. Model discrimination was assessed using the concordance statistic (c-statistic), and calibration ability assessed by plotting the mean predicted probability against the mean observed probability of lung cancer. Among candidate predictors, age, sex, smoking intensity, body mass index (BMI), presence of chronic obstructive pulmonary disease (COPD), pulmonary tuberculosis (TB), and type 2 diabetes mellitus (DM) were finally included. Our risk prediction model showed good discrimination (c-statistic, 0.810; 95% CI: 0.801–0.819). The relationship between model-predicted and actual lung cancer development correlated well in the calibration plot. When using easily accessible and modifiable risk factors, this model can help individuals make decisions regarding lung cancer screening or lifestyle modification, including smoking cessation.


CHEST Journal ◽  
2019 ◽  
Vol 156 (1) ◽  
pp. 112-119 ◽  
Author(s):  
Heber MacMahon ◽  
Feng Li ◽  
Yulei Jiang ◽  
Samuel G. Armato

Author(s):  
Alex Orchard ◽  
Sumsum P. Sunny ◽  
Amritha Suresh ◽  
Praveen Birur ◽  
Moni Kuriakose ◽  
...  

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