Literature DB >> 9709283

Markov models of breast tumor progression: some age-specific results.

S W Duffy1, N E Day, L Tabár, H H Chen, T C Smith.   

Abstract

Researchers have noted that mammographic screening has a reduced effect on breast cancer mortality in women in their forties compared to older women. Explanations for this include poorer sensitivity in younger women due to denser breast tissue, as well as more rapid tumor progression, giving a shorter mean sojourn time (the average duration of the preclinical screen-detectable period). To test these hypotheses, we developed a series of Markov-chain models to estimate tumor progression rates and sensitivity. Parameters were estimated using tumor data from the Swedish two-county trial of mammographic screening for breast cancer. The mean sojourn time was shorter in women aged 40-49 compared to women aged 50-59 and 60-69 (2.44, 3.70, and 4.17 years, respectively). Sensitivity was lower in the 40-49 age group compared to the two older groups (83%, 100%, and 100%, respectively). Thus, both rapid progression and poorer sensitivity are associated with the 40-49 age group. We also modeled tumor size, node status, and malignancy grade together with subsequent breast cancer mortality and found that, to achieve a reduction in mortality commensurate with that in women over 50, the interscreening interval for women in their forties should be less than two years. We conclude that Markov models and the use of tumor size, node status, and malignancy grade as surrogates for mortality can be useful in design and analysis of future studies of breast cancer screening.

Entities:  

Mesh:

Year:  1997        PMID: 9709283     DOI: 10.1093/jncimono/1997.22.93

Source DB:  PubMed          Journal:  J Natl Cancer Inst Monogr        ISSN: 1052-6773


  14 in total

1.  Computational modeling and multilevel cancer control interventions.

Authors:  Joseph P Morrissey; Kristen Hassmiller Lich; Rebecca Anhang Price; Jeanne Mandelblatt
Journal:  J Natl Cancer Inst Monogr       Date:  2012-05

2.  Annual mammography at age 45-49 years and biennial mammography at age 50-69 years: comparing performance measures in an organised screening setting.

Authors:  Lauro Bucchi; Alessandra Ravaioli; Flavia Baldacchini; Orietta Giuliani; Silvia Mancini; Rosa Vattiato; Fabio Falcini; Paolo Giorgi Rossi; Cinzia Campari; Debora Canuti; Enza Di Felice; Priscilla Sassoli de Bianchi; Stefano Ferretti; Nicoletta Bertozzi
Journal:  Eur Radiol       Date:  2019-03-18       Impact factor: 5.315

3.  Validation of a modelling approach for estimating the likely effectiveness of cancer screening using cancer data on prevalence screening and incidence.

Authors:  Nora Pashayan; Paul Pharoah; László Tabár; David E Neal; Richard M Martin; Jenny Donovan; Freddie Hamdy; Stephen W Duffy
Journal:  Cancer Epidemiol       Date:  2010-08-16       Impact factor: 2.984

4.  Effects of annual vs triennial mammography interval on breast cancer incidence and mortality in ages 40-49 in Finland.

Authors:  I Parvinen; S Chiu; L Pylkkänen; P Klemi; P Immonen-Räihä; L Kauhava; N Malila; M Hakama
Journal:  Br J Cancer       Date:  2011-09-20       Impact factor: 7.640

5.  Estimating breast cancer mortality reduction and overdiagnosis due to screening for different strategies in the United Kingdom.

Authors:  N B Gunsoy; M Garcia-Closas; S M Moss
Journal:  Br J Cancer       Date:  2014-04-24       Impact factor: 7.640

6.  Breast Cancer Risk From Modifiable and Non-Modifiable Risk Factors among Women in Southeast Asia: A Meta-Analysis

Authors:  Ricvan Dana Nindrea; Teguh Aryandono; Lutfan Lazuardi
Journal:  Asian Pac J Cancer Prev       Date:  2017-12-28

7.  Parameter estimates for invasive breast cancer progression in the Canadian National Breast Screening Study.

Authors:  S Taghipour; D Banjevic; A B Miller; N Montgomery; A K S Jardine; B J Harvey
Journal:  Br J Cancer       Date:  2013-01-15       Impact factor: 7.640

Review 8.  Screening for breast cancer with mammography.

Authors:  Peter C Gøtzsche; Karsten Juhl Jørgensen
Journal:  Cochrane Database Syst Rev       Date:  2013-06-04

9.  Quantifying the natural history of breast cancer.

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
Journal:  Br J Cancer       Date:  2013-10-01       Impact factor: 7.640

10.  Modelling the overdiagnosis of breast cancer due to mammography screening in women aged 40 to 49 in the United Kingdom.

Authors:  Necdet B Gunsoy; Montserrat Garcia-Closas; Sue M Moss
Journal:  Breast Cancer Res       Date:  2012-11-29       Impact factor: 6.466

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