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比较 CISNET 乳腺癌发病和死亡预测与 40 至 49 岁年龄组乳腺 X 线筛查临床试验观察结果。

Comparing CISNET Breast Cancer Incidence and Mortality Predictions to Observed Clinical Trial Results of Mammography Screening from Ages 40 to 49.

机构信息

Department of Public Health, Erasmus Medical Center, Rotterdam, the Netherlands.

Department of Oncology, Georgetown-Lombardi Comprehensive Cancer Center, Georgetown University School of Medicine, Washington DC, USA.

出版信息

Med Decis Making. 2018 Apr;38(1_suppl):140S-150S. doi: 10.1177/0272989X17718168.

Abstract

BACKGROUND

The UK Age trial compared annual mammography screening of women ages 40 to 49 years with no screening and found a statistically significant breast cancer mortality reduction at the 10-year follow-up but not at the 17-year follow-up. The objective of this study was to compare the observed Age trial results with the Cancer Intervention and Surveillance Modeling Network (CISNET) breast cancer model predicted results.

METHODS

Five established CISNET breast cancer models used data on population demographics, screening attendance, and mammography performance from the Age trial together with extant natural history parameters to project breast cancer incidence and mortality in the control and intervention arm of the trial.

RESULTS

The models closely reproduced the effect of annual screening from ages 40 to 49 years on breast cancer incidence. Restricted to breast cancer deaths originating from cancers diagnosed during the intervention phase, the models estimated an average 15% (range across models, 13% to 17%) breast cancer mortality reduction at the 10-year follow-up compared with 25% (95% CI, 3% to 42%) observed in the trial. At the 17-year follow-up, the models predicted 13% (range, 10% to 17%) reduction in breast cancer mortality compared with the non-significant 12% (95% CI, -4% to 26%) in the trial.

CONCLUSIONS

The models underestimated the effect of screening on breast cancer mortality at the 10-year follow-up. Overall, the models captured the observed long-term effect of screening from age 40 to 49 years on breast cancer incidence and mortality in the UK Age trial, suggesting that the model structures, input parameters, and assumptions about breast cancer natural history are reasonable for estimating the impact of screening on mortality in this age group.

摘要

背景

英国年龄试验比较了 40 至 49 岁女性的年度乳房 X 光筛查与不筛查,并在 10 年随访时发现乳腺癌死亡率有统计学意义的降低,但在 17 年随访时没有。本研究的目的是比较观察到的年龄试验结果与癌症干预和监测建模网络(CISNET)乳腺癌模型预测的结果。

方法

五个成熟的 CISNET 乳腺癌模型使用了年龄试验中关于人口统计学、筛查参与率和乳房 X 光表现的数据,以及现有的自然史参数,来预测试验中对照组和干预组的乳腺癌发病率和死亡率。

结果

这些模型很好地再现了 40 至 49 岁期间年度筛查对乳腺癌发病率的影响。从干预阶段诊断出的癌症起源的乳腺癌死亡病例来看,模型估计在 10 年随访时,乳腺癌死亡率平均降低 15%(模型之间的范围为 13%至 17%),而试验中观察到的降低幅度为 25%(95%CI,3%至 42%)。在 17 年随访时,模型预测乳腺癌死亡率降低 13%(范围为 10%至 17%),而试验中观察到的非显著降低 12%(95%CI,-4%至 26%)。

结论

这些模型低估了筛查在 10 年随访时对乳腺癌死亡率的影响。总的来说,这些模型捕捉到了英国年龄试验中 40 岁至 49 岁期间筛查对乳腺癌发病率和死亡率的长期影响,这表明模型结构、输入参数和对乳腺癌自然史的假设对于估计该年龄段筛查对死亡率的影响是合理的。

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本文引用的文献

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3
The University of Wisconsin Breast Cancer Epidemiology Simulation Model: An Update.
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4
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Med Decis Making. 2018 Apr;38(1_suppl):9S-23S. doi: 10.1177/0272989X17700624.
5
Screening for Breast Cancer: U.S. Preventive Services Task Force Recommendation Statement.
Ann Intern Med. 2016 Feb 16;164(4):279-96. doi: 10.7326/M15-2886. Epub 2016 Jan 12.
6
Collaborative Modeling of the Benefits and Harms Associated With Different U.S. Breast Cancer Screening Strategies.
Ann Intern Med. 2016 Feb 16;164(4):215-25. doi: 10.7326/M15-1536. Epub 2016 Jan 12.
9
Data Resource Profile: The Human Mortality Database (HMD).
Int J Epidemiol. 2015 Oct;44(5):1549-56. doi: 10.1093/ije/dyv105. Epub 2015 Jun 23.
10
Prevalence of mammographically dense breasts in the United States.
J Natl Cancer Inst. 2014 Sep 12;106(10). doi: 10.1093/jnci/dju255. Print 2014 Oct.

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