Cheung Li C, Ramadas Kunnambath, Muwonge Richard, Katki Hormuzd A, Thomas Gigi, Graubard Barry I, Basu Partha, Sankaranarayanan Rengaswamy, Somanathan Thara, Chaturvedi Anil K
Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, MD.
Department of Radiation Oncology, Regional Cancer Centre, Thiruvananthapuram, India.
J Clin Oncol. 2021 Feb 20;39(6):663-674. doi: 10.1200/JCO.20.02855. Epub 2021 Jan 15.
We evaluated proof of principle for resource-efficient, risk-based screening through reanalysis of the Kerala Oral Cancer Screening Trial.
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.
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 ( 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), trend = .059 and ever-tobacco and/or ever-alcohol users: Q1 RD = 1.0/100,000 versus Q4 = 22.5/100,000; 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).
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.
通过对喀拉拉邦口腔癌筛查试验的重新分析,我们评估了基于资源高效、风险的筛查的原理验证。
这项整群随机试验包括三轮每三年一次的视觉检查(7个整群,n = 96,516)与标准护理(6个整群,n = 95,354),并进行了长达9年的随访。我们开发了一个基于Cox回归的口腔癌发病率风险预测模型。通过考虑整群随机化的意向性分析(ITT),使用该风险预测模型来调整两组之间的口腔癌风险不平衡,我们计算了口腔癌死亡率的相对(风险比[HRs])和绝对(率差[RDs])筛查效果,并比较了不同风险阈值下的筛查效率。
与对照组相比,筛查组的口腔癌死亡率降低了27%(HR = 0.73;95%CI,0.54至0.98),包括曾经吸烟和/或饮酒者降低了29%(HR = 0.71;95%CI,0.51至0.99)。这种相对疗效在口腔癌风险四分位数中相似(交互作用 = 0.59);因此,绝对疗效随着模型预测风险的增加而增加——总体试验:最低风险四分位数(Q1)的RD = 0.5/100,000,而最高四分位数(Q4)为13.4/100,000,趋势 = 0.059;曾经吸烟和/或饮酒者:Q1的RD = 1.0/100,000,Q4 = 22.5/100,000;趋势 = 0.026。在类似于喀拉拉邦试验的人群中,对100%的个体进行筛查将使口腔癌死亡率降低27.1%,所需筛查人数(NNS) = 2,043。将筛查限制在曾经吸烟和/或饮酒者且不进行额外风险分层将显著提高效率(43.4%的人接受筛查,口腔癌死亡率降低23.3%,NNS = 1,029),而基于风险预测模型对50%风险最高的曾经吸烟和/或饮酒者进行筛查将进一步提高效率,同时项目敏感性损失很小(21.7%的人接受筛查,口腔癌死亡率降低19.7%,NNS = 610)。
在喀拉拉邦试验中,口腔癌筛查在口腔癌风险最高的个体中效果最佳。这些结果提供了原理验证,即基于风险的口腔癌筛查可以显著提高筛查项目的效率。