Mammographic sensitivity as a function of tumor size: A novel estimation based on population-based screening data

Jing Wang,Pam Gottschal,Lilu Ding,Daniëllew.A Van Veldhuizen,Wenli Lu,Nehmat Houssami,Marcel J.W Greuter,Geertruida H De Bock

doi:10.1016/j.breast.2020.12.003

Jing Wang, Pam Gottschal + Show 6 more

Open Access

https://doi.org/10.1016/j.breast.2020.12.003

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Abstract

BackgroundInstead 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. MethodsUsing 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. ResultsAggregated 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. ConclusionDerived 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.

Highlights

IntroductionBreast cancer is the most common cancer and one of the main causes of death in European women, approximately one in sevenrelevant for the evaluation of screening programs [7,8]
Breast cancer is the most common cancer and one of the main causes of death in European women, approximately one in sevenAbbreviations: false negatives (FN), false negative; TP, true positive; TVDT, tumor volume doubling time.relevant for the evaluation of screening programs [7,8]
We developed a novel model for the estimation of mammographic sensitivity as a continuous function of tumor size, given that mammography’s detection capability varies according to tumor size

Summary

Introduction

Breast cancer is the most common cancer and one of the main causes of death in European women, approximately one in sevenrelevant for the evaluation of screening programs [7,8]. Whereas most studies give one constant estimate for the sensitivity of mammography, Weedon-Fekjær et al developed a logistic model to estimate the sensitivity of mammography as a function of tumor size [10,11] In their studies, the sensitivity was estimated simultaneously with a continuous growth model utilizing breast cancer screening data, and back-calculation methods were used to estimate tumor size at screening from tumor size distributions of clinically detected tumors. Methods: 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. Conclusion: 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

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