Tumour Growth Models of Breast Cancer for Evaluating Early Detection—A Summary and a Simulation Study

Abstract

With the advent of nationwide mammography screening programmes, a number of natural history models of breast cancers have been developed and used to assess the effects of screening. The first half of this article provides an overview of a class of these models and describes how they can be used to study latent processes of tumour progression from observational data. The second half of the article describes a simulation study which applies a continuous growth model to illustrate how effects of extending the maximum age of the current Swedish screening programme from 74 to 80 can be evaluated. Compared to no screening, the current and extended programmes reduced breast cancer mortality by 18.5% and 21.7%, respectively. The proportion of screen-detected invasive cancers which were overdiagnosed was estimated to be 1.9% in the current programme and 2.9% in the extended programme. With the help of these breast cancer natural history models, we can better understand the latent processes, and better study the effects of breast cancer screening.

Novel predictions of invasive breast cancer risk in mammography screening cohorts

“…Therefore, we should be cautious with interpreting the results, since some of the predicted screen‐detected cases are overdiagnosed 47 . One approach to study this is to use these models in a broader simulation, as we have previously outlined 48 …”

“…47 One approach to study this is to use these models in a broader simulation, as we have previously outlined. 48 The modeling approach used in this new risk prediction relies heavily on its parametric assumptions. They are necessary to piece together the latent natural history based only on data from diagnosis.…”

The natural history of ductal carcinoma in situ (DCIS) in simulation models: A systematic review

“…Overdiagnosis, the proportion of breast cancers that would not have been diagnosed during a woman's lifetime in absence of screening, is considered to be one of the largest harms of screening [ 7 ]. Estimates of overdiagnosis vary largely (0%–91%) and depend on many factors, such as whether the estimate includes only IBC, only DCIS, or both [ 5 , 7 , [9] , [10] , [11] ]. Although there is consensus that DCIS is the largest contributor to overdiagnosis, the extent to which this occurs is unclear due to the unknown natural history of DCIS [ 3 , 6 , 10 , 12 , 13 ].…”

“…Modelling studies provide a complementary method to make accurate estimates of tumour progression and overdiagnosis, with their ability to use long follow-up and account for both the benefits and harms of screening. Especially natural history models can provide insight into underlying processes and can be useful in assessment of the benefits and harms of screening scenarios [ 11 ]. However, modelling studies have shown a large range in assumptions that affect tumour progression and overdiagnosis estimates [ 2 , 3 , 11 , 13 ].…”

“…Especially natural history models can provide insight into underlying processes and can be useful in assessment of the benefits and harms of screening scenarios [ 11 ]. However, modelling studies have shown a large range in assumptions that affect tumour progression and overdiagnosis estimates [ 2 , 3 , 11 , 13 ].…”