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

breast cancer early detection mammography observational study overdiagnosis screening tumour growth

Journal

Cancers
ISSN: 2072-6694
Titre abrégé: Cancers (Basel)
Pays: Switzerland
ID NLM: 101526829

Informations de publication

Date de publication:
31 Jan 2023
Historique:
received: 15 12 2022
revised: 26 01 2023
accepted: 29 01 2023
entrez: 11 2 2023
pubmed: 12 2 2023
medline: 12 2 2023
Statut: epublish

Résumé

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.

Identifiants

pubmed: 36765870
pii: cancers15030912
doi: 10.3390/cancers15030912
pmc: PMC9913080
pii:
doi:

Types de publication

Journal Article

Langues

eng

Subventions

Organisme : Swedish Research Council
ID : 2020-01302
Organisme : Swedish Cancer Society
ID : CAN 2020-0714

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Auteurs

Rickard Strandberg (R)

Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, 171 77 Stockholm, Sweden.

Linda Abrahamsson (L)

Center for Primary Health Care Research, Lund University, 205 02 Malmö, Sweden.

Gabriel Isheden (G)

Intelligent Decisions Analytics AB, 171 65 Solna, Sweden.

Keith Humphreys (K)

Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, 171 77 Stockholm, Sweden.

Classifications MeSH