Tumour Growth Models of Breast Cancer for Evaluating Early Detection—A Summary and a Simulation Study
(2023) In Cancers 15(3).- 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... (More)
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.
(Less)
- author
- Strandberg, Rickard ; Abrahamsson, Linda LU ; Isheden, Gabriel and Humphreys, Keith LU
- organization
- publishing date
- 2023-02
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- breast cancer, early detection, mammography, observational study, overdiagnosis, screening, tumour growth
- in
- Cancers
- volume
- 15
- issue
- 3
- article number
- 912
- publisher
- MDPI AG
- external identifiers
-
- scopus:85147826274
- pmid:36765870
- ISSN
- 2072-6694
- DOI
- 10.3390/cancers15030912
- language
- English
- LU publication?
- yes
- id
- 37143eb3-164f-4b76-9f2e-238e6467c98f
- date added to LUP
- 2023-02-21 12:35:48
- date last changed
- 2024-06-13 17:17:12
@article{37143eb3-164f-4b76-9f2e-238e6467c98f,
abstract = {{<p>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.</p>}},
author = {{Strandberg, Rickard and Abrahamsson, Linda and Isheden, Gabriel and Humphreys, Keith}},
issn = {{2072-6694}},
keywords = {{breast cancer; early detection; mammography; observational study; overdiagnosis; screening; tumour growth}},
language = {{eng}},
number = {{3}},
publisher = {{MDPI AG}},
series = {{Cancers}},
title = {{Tumour Growth Models of Breast Cancer for Evaluating Early Detection—A Summary and a Simulation Study}},
url = {{http://dx.doi.org/10.3390/cancers15030912}},
doi = {{10.3390/cancers15030912}},
volume = {{15}},
year = {{2023}},
}