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

SIMPLE SUMMARY: Advanced statistical methods can be useful for understanding the roles of breast cancer risk factors in cancer progression and detection, and for assessing impacts of early detection of breast cancer in populations with implemented breast cancer screening programmes. In this article, we summarise approaches for estimating, from observational data, tumour progression models that are inspired by biological arguments. These models have the potential to be used in studies of personalised screening. We describe a simulation study that explores the impact of extending the age of screening invitation, which is currently being considered by Sweden’s National Board of Health and Welfare. 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.