Modeled Reductions in Late-stage Cancer with a Multi-Cancer Early Detection Test
Modeled Reductions in Late-stage Cancer with a Multi-Cancer Early Detection Test
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DOI：
10.1158/1055-9965.epi-20-1134
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发表时间：
2021-03-01
影响因子：
3.8
通讯作者：
Berg, Christine D.
中科院分区：
文献类型：
--
作者：
Hubbell, Earl;Clarke, Christina A.;Berg, Christine D.
背景：癌症是全球第二大死亡原因，许多病例是在预后较差的晚期发现的。实现多癌症早期检测(MCed)的新技术可能使“普遍癌症筛查”成为可能。我们扩展了单癌模型，以了解在日常护理中增加MCed检测对公共卫生的潜在影响。方法：我们从美国监测、流行病学和最终结果(SEER)计划中获得了2006至2015年间在50-79岁人群中诊断的所有浸润性癌症的特定阶段发病率和生存率的数据，并将其与已发表的MCed测试在状态转换模型(拦截模型)中的表现相结合，以预测诊断成功率、阶段转换和潜在的死亡率降低。我们建立了长期(事件)表现的模型，考虑了重复筛查对检测的限制。结果：MCed检测每年每10万人可以拦截485例癌症，在被拦截的晚期(III+IV)发病率降低78%。考虑到准备时间，这可以将截获的癌症患者的5年癌症死亡率降低39%，导致每10万人中有104人死亡，占所有与癌症相关的死亡人数的26%。结论：评估同时影响多种癌症类型的MCED测试的影响需要对所有癌症发病率进行建模。假设MCed检测指标在临床环境中成立，改善公共健康的总潜力是显著的。影响：在具有代表性的人群中对MCed检测的建模性能表明，如果添加到日常护理中，它可以显著降低总体癌症死亡率。
Background: Cancer is the second leading cause of death globally, with many cases detected at a late stage when prognosis is poor. New technologies enabling multi-cancer early detection (MCED) may make "universal cancer screening" possible. We extend single-cancer models to understand the potential public health effects of adding a MCED test to usual care.Methods: We obtained data on stage-specific incidence and survival of all invasive cancers diagnosed in persons aged 50-79 between 2006 and 2015 from the US Surveillance, Epidemiology, and End Results (SEER) program, and combined this with published performance of a MCED test in a state transition model (interception model) to predict diagnostic yield, stage shift, and potential mortality reductions. We model long-term (incident) performance, accounting for constraints on detection due to repeated screening.Results: The MCED test could intercept 485 cancers per year per 100,000 persons, reducing late-stage (III+IV) incidence by 78% in those intercepted. Accounting for lead time, this could reduce 5-year cancer mortality by 39% in those intercepted, resulting in an absolute reduction of 104 deaths per 100,000, or 26% of all cancer-related deaths. Findings are robust across tumor growth scenarios.Conclusions: Evaluating the impact of a MCED test that affects multiple cancer types simultaneously requires modeling across all cancer incidence. Assuming MCED test metrics hold in a clinical setting, the aggregate potential to improve public health is significant.Impact: Modeling performance of a MCED test in a representative population suggests that it could substantially reduce overall cancer mortality if added to usual care.