Bulliard Jean-Luc, Beau Anna-Belle, Njor Sisse, Wu Wendy Yi-Ying, Procopio Pietro, Nickson Carolyn, Lynge Elsebeth
Centre for Primary Care and Public Health (unisanté), University of Lausanne, Lausanne, Switzerland.
Pharmacologie Médicale, Faculté de Médecine, Université Paul-Sabatier III, CHU Toulouse, UMR INSERM, Toulouse, France.
Int J Cancer. 2021 Apr 19. doi: 10.1002/ijc.33602.
Overdiagnosis is a harmful consequence of screening which is particularly challenging to estimate. An unbiased setting to measure overdiagnosis in breast cancer screening requires comparative data from a screened and an unscreened cohort for at least 30 years. Such randomised data will not become available, leaving us with observational data over shorter time periods and outcomes of modelling. This collaborative effort of the International Cancer Screening Network quantified the variation in estimated breast cancer overdiagnosis in organised programmes with evaluation of both observed and simulated data, and presented examples of how modelling can provide additional insights. Reliable observational data, analysed with study design accounting for methodological pitfalls, and modelling studies with different approaches, indicate that overdiagnosis accounts for less than 10% of invasive breast cancer cases in a screening target population of women aged 50 to 69. Estimates above this level are likely to derive from inaccuracies in study design. The widely discrepant estimates of overdiagnosis reported from observational data could substantially be reduced by use of a cohort study design with at least 10 years of follow-up after screening stops. In contexts where concomitant opportunistic screening or gradual implementation of screening occurs, and data on valid comparison groups are not readily available, modelling of screening intervention becomes an advantageous option to obtain reliable estimates of breast cancer overdiagnosis.
过度诊断是筛查的一个有害后果,尤其难以评估。衡量乳腺癌筛查中过度诊断的无偏倚环境需要来自筛查队列和未筛查队列至少30年的比较数据。这种随机数据无法获得,我们只能依靠较短时间段的观察数据和建模结果。国际癌症筛查网络的这项合作努力通过对观察数据和模拟数据的评估,量化了有组织项目中估计的乳腺癌过度诊断的差异,并给出了建模如何能提供更多见解的例子。可靠的观察数据,通过考虑方法学陷阱的研究设计进行分析,以及采用不同方法的建模研究表明,在50至69岁女性的筛查目标人群中,过度诊断占浸润性乳腺癌病例的比例不到10%。高于这一水平的估计可能源于研究设计的不准确。通过使用筛查停止后至少随访10年的队列研究设计,观察数据报告的过度诊断的广泛差异估计可以大幅减少。在同时存在机会性筛查或筛查逐步实施且有效对照组数据不易获得的情况下,筛查干预建模成为获得乳腺癌过度诊断可靠估计的有利选择。