Evidence map›Paper›PMID 29463227›Full record

ArticleBMC cancer2018

Evaluating different breast tumor progression models using screening data.

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

Abstract read
In one paragraph

Article in BMC cancer, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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2 · The registry

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Åsbjørn Schumacher WestvikDepartment of Pathology, Oslo University Hospital, Norway, P.O. Box 4956 Nydalen, 0424, Oslo, Norway.
Harald Weedon-FekjærOslo Center for biostatistics and epidemiology, Research Support Services, Oslo University Hospital, Norway, P.O. Box 4956 Nydalen, 0424, Oslo, Norway. harald.weedon-fekjar@medisin.uio.no.
Jan MæhlenDepartment of Pathology, Oslo University Hospital, Norway, P.O. Box 4956 Nydalen, 0424, Oslo, Norway.
Knut LiestølCenter of Cancer Biomedicine, Department of Informatics, University of Oslo, Norway, P.O. Box 1080 Blindern, 0316, Oslo, Norway.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMammography 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.

methodsBreast 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).

resultsThe 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.

conclusionsModels 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.

Indexed as

Models, StatisticalAgedBreast NeoplasmsComputer SimulationDisease ProgressionEarly Detection of CancerFemaleHumansIncidenceMammographyMass ScreeningMiddle AgedNorwayBreast cancerHormone replacement therapyMammography screeningOver-diagnosisSimulation modelTumor regression

Identifiers

PMID29463227
PMCPMC5819671

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.