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Evaluating different breast tumor progression models using screening data

Overview of attention for article published in BMC Cancer, February 2018
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Title
Evaluating different breast tumor progression models using screening data
Published in
BMC Cancer, February 2018
DOI 10.1186/s12885-018-4130-2
Pubmed ID
Authors

Åsbjørn Schumacher Westvik, Harald Weedon-Fekjær, Jan Mæhlen, Knut Liestøl

Abstract

Mammography screening is used to detect breast cancer at an early treatable stage, reducing breast cancer mortality. Traditionally, breast cancer has been seen as a disease with only progressive lesions, and here we examine the validity of this assumption by testing if incidence levels after introducing mammography screening can be reproduced assuming only progressive tumors. Breast cancer incidence data 1990-2009 obtained from the initially screened Norwegian counties (Akershus, Oslo, Rogaland and Hordaland) was included, covering the time-period before, during and after the introduction of mammography screening. From 1996 women aged 50-69 were invited for biennial public screening. Using estimates of tumor growth and screening sensitivity based on pre-screening and prevalence screening data (1990-1998), we simulated incidence levels during the following period (1999-2009). The simulated incidence levels during the period with repeated screenings were markedly below the observed levels. The results were robust to changes in model parameters. Adjusting for hormone replacement therapy use, we obtained levels closer to the observed levels. However, there was still a marked gap, and only by assuming some tumors that undergo regressive changes or enter a markedly less detectable state, was our model able to reproduce the observed incidence levels. Models with strictly progressive tumors are only able to partly explain the changes in incidence levels observed after screening introduction in the initially screened Norwegian counties. More complex explanations than a time shift in detection of future clinical cancers seem to be needed to reproduce the incidence trends, questioning the basis for many over-diagnosis calculations. As data are not randomized, similar studies in other populations are wanted to exclude effect of unknown confounders.

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Mendeley readers

The data shown below were compiled from readership statistics for 17 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 17 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 3 18%
Student > Ph. D. Student 2 12%
Lecturer 1 6%
Student > Doctoral Student 1 6%
Other 1 6%
Other 4 24%
Unknown 5 29%
Readers by discipline Count As %
Medicine and Dentistry 2 12%
Biochemistry, Genetics and Molecular Biology 2 12%
Nursing and Health Professions 1 6%
Agricultural and Biological Sciences 1 6%
Pharmacology, Toxicology and Pharmaceutical Science 1 6%
Other 4 24%
Unknown 6 35%