ArticleCancers2023
Tumour Growth Models of Breast Cancer for Evaluating Early Detection-A Summary and a Simulation Study.
Article in Cancers, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Estimating lead time and overdiagnosis in cancer screening programmes: the MOCCI method.International journal of epidemiology · 2026Article
- A stochastic modelling framework for cancer patient trajectories: combining tumour growth, metastasis, and survival.Journal of mathematical biology · 2025Article
- The natural history of ductal carcinoma in situ: development, validation, and estimated outcomes of the SimDCIS model.Breast cancer research and treatment · 2025Article
- Evaluation of optimal strategies for breast cancer screening in Ghana: A simulation study based on a continuous tumor growth model.PloS one · 2025Article
- Optimizing breast cancer screening strategies for women with different BMI levels in Ghana: A simulation-based study on BMI-dependent tumor growth model.PLOS global public health · 2025Article
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Authors and funding
4 authors.
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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.
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