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基于风险的口腔癌筛查个体选择

Risk-Based Selection of Individuals for Oral Cancer Screening.

作者信息

Cheung Li C, Ramadas Kunnambath, Muwonge Richard, Katki Hormuzd A, Thomas Gigi, Graubard Barry I, Basu Partha, Sankaranarayanan Rengaswamy, Somanathan Thara, Chaturvedi Anil K

机构信息

Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, MD.

Department of Radiation Oncology, Regional Cancer Centre, Thiruvananthapuram, India.

出版信息

J Clin Oncol. 2021 Feb 20;39(6):663-674. doi: 10.1200/JCO.20.02855. Epub 2021 Jan 15.

Abstract

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 ( 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).

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.

摘要

目的

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。

方法

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。

结果

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)。

结论

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。

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