University of Groningen, University Medical Center Groningen, Department of Epidemiology, Groningen, the Netherlands.
Department of Epidemiology and Health Statistics, School of Public Health, Tianjin Medical University, Tianjin, China.
Breast. 2021 Feb;55:69-74. doi: 10.1016/j.breast.2020.12.003. Epub 2020 Dec 9.
Instead of a single value for mammographic sensitivity, a sensitivity function based on tumor size more realistically reflects mammography's detection capability. Because previous models may have overestimated size-specific sensitivity, we aimed to provide a novel approach to improve sensitivity estimation as a function of tumor size.
Using aggregated data on interval and screen-detected cancers, observed tumor sizes were back-calculated to the time of screening using an exponential tumor growth model and a follow-up time of 4 years. From the observed number of detected cancers and an estimation of the number of false-negative cancers, a model for the sensitivity as a function of tumor size was determined. A univariate sensitivity analysis was conducted by varying follow-up time and tumor volume doubling time (TVDT). A systematic review was conducted for external validation of the sensitivity model.
Aggregated data of 22,915 screen-detected and 10,670 interval breast cancers from the Dutch screening program were used. The model showed that sensitivity increased from 0 to 85% for tumor sizes from 2 to 20 mm. When TVDT was set at the upper and lower limits of the confidence interval, sensitivity for a 20-mm tumor was 74% and 93%, respectively. The estimated sensitivity gave comparable estimates to those from two of three studies identified by our systematic review.
Derived from aggregated breast screening outcomes data, our model's estimation of sensitivity as a function of tumor size may provide a better representation of data observed in screening programs than other models.
与单一的乳腺 X 线摄影敏感性值相比,基于肿瘤大小的敏感性函数更能真实反映乳腺 X 线摄影的检测能力。由于之前的模型可能高估了特定大小的敏感性,我们旨在提供一种新方法来改善作为肿瘤大小函数的敏感性估计。
使用关于间隔期和筛查检出癌的数据,使用指数肿瘤生长模型和 4 年的随访时间将观察到的肿瘤大小回溯到筛查时间。根据观察到的检出癌数量和估计的假阴性癌数量,确定了一种作为肿瘤大小函数的敏感性模型。通过改变随访时间和肿瘤倍增时间(TVDT)进行了单变量敏感性分析。进行了系统综述,以验证敏感性模型的外部有效性。
使用荷兰筛查计划的 22915 例筛查检出和 10670 例间隔期乳腺癌的汇总数据。该模型表明,敏感性从 2 至 20mm 的肿瘤大小的 0%增加到 85%。当 TVDT 设置在置信区间的上限和下限时,20mm 肿瘤的敏感性分别为 74%和 93%。估计的敏感性与我们系统综述中确定的三项研究中的两项的估计值相当。
从汇总的乳腺筛查结果数据中得出,我们的模型对肿瘤大小作为敏感性函数的估计可能比其他模型更能代表筛查计划中观察到的数据。