Quantifying the natural history of breast cancer View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2013-10

AUTHORS

K H X Tan, L Simonella, H L Wee, A Roellin, Y-W Lim, W-Y Lim, K S Chia, M Hartman, A R Cook

ABSTRACT

BACKGROUND: Natural history models of breast cancer progression provide an opportunity to evaluate and identify optimal screening scenarios. This paper describes a detailed Markov model characterising breast cancer tumour progression. METHODS: Breast cancer is modelled by a 13-state continuous-time Markov model. The model differentiates between indolent and aggressive ductal carcinomas in situ tumours, and aggressive tumours of different sizes. We compared such aggressive cancers, that is, which are non-indolent, to those which are non-growing and regressing. Model input parameters and structure were informed by the 1978-1984 Ostergotland county breast screening randomised controlled trial. Overlaid on the natural history model is the effect of screening on diagnosis. Parameters were estimated using Bayesian methods. Markov chain Monte Carlo integration was used to sample the resulting posterior distribution. RESULTS: The breast cancer incidence rate in the Ostergotland population was 21 (95% CI: 17-25) per 10 000 woman-years. Accounting for length-biased sampling, an estimated 91% (95% CI: 85-97%) of breast cancers were aggressive. Larger tumours, 21-50 mm, had an average sojourn of 6 years (95% CI: 3-16 years), whereas aggressive ductal carcinomas in situ took around half a month (95% CI: 0-1 month) to progress to the invasive ≤10 mm state. CONCLUSION: These tumour progression rate estimates may facilitate future work analysing cost-effectiveness and quality-adjusted life years for various screening strategies. More... »

PAGES

2035

Identifiers

URI

http://scigraph.springernature.com/pub.10.1038/bjc.2013.471

DOI

http://dx.doi.org/10.1038/bjc.2013.471

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1023494099

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/24084766


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curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1038/bjc.2013.471'

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