Literature DB >> 27240167

Overdiagnosis and overtreatment associated with breast cancer mammography screening: A simulation study with calibration to population-based data.

Arnaud Seigneurin1, José Labarère2, Olivier François3, Catherine Exbrayat4, Maylis Dupouy3, Marc Filippi3, Marc Colonna5.   

Abstract

OBJECTIVES: The magnitude of overdiagnosis of breast cancer associated with mammography screening remains controversial because of methodological issues. The objective of this study was to quantify overdiagnosis and overtreatment associated with a population-based screening programme, taking into account lead time and uncertainty concerning baseline incidence of breast cancers.
MATERIAL AND METHODS: A simulation model was developed to replicate incidence and detection rates of breast cancer observed in the Isère Département, France. The parameters of the model were estimated using an approximate Bayesian computation method.
RESULTS: For women aged 50-74 years during the 2007-2010 period, overdiagnosis of non-progressive breast cancers accounted for 17.0% (95% credibility interval (CI): 2.5%-35.5%) of all in situ cancers diagnosed, 5.5% (95% CI: 0.8%-9.8%) of all invasive cancers diagnosed, and 20.3% (95% CI: 3.0%-38.9%) of in situ and 13.0% (95% CI: 2.2%-23.3%) of invasive screen detected breast cancers. The estimates of overdiagnosis due to competitive causes of death were 1.0% (95% CI: 0.2%-%1.7) and 1.1% (95% CI: 0.6%-1.7%) for all in situ and invasive cancers diagnosed, respectively, and 1.3% (95% CI: 0.2%-2.0%) and 2.6% (95% CI: 1.4%-4.0%) of all in situ and invasive screen detected breast cancers, respectively. Among 1000 screen-detected cancers in 2010, 155 (95% CI: 27-284), 134 (95% CI: 10-242) and 140 (95% CI: 25-254) women underwent breast conserving surgery, lymph node dissection and radiation therapy for overdiagnosed cancers, respectively.
CONCLUSION: Our estimates of overdiagnosis should be balanced against the reduction of breast cancer mortality to assess the value of breast cancer screening programme.
Copyright © 2016 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Breast cancer; Cancer screening; Epidemiology; Modelling

Mesh:

Year:  2016        PMID: 27240167     DOI: 10.1016/j.breast.2016.04.013

Source DB:  PubMed          Journal:  Breast        ISSN: 0960-9776            Impact factor:   4.380


  6 in total

1.  Identification of the Fraction of Indolent Tumors and Associated Overdiagnosis in Breast Cancer Screening Trials.

Authors:  Marc D Ryser; Roman Gulati; Marisa C Eisenberg; Yu Shen; E Shelley Hwang; Ruth B Etzioni
Journal:  Am J Epidemiol       Date:  2019-01-01       Impact factor: 4.897

2.  Reliability and Concurrent Validity of the GAIN Short Screener Among Youth Utilizing Integrated Health Services.

Authors:  Raymond Khanano; Skye Barbic; Joanna Henderson; Steve Mathias; Christopher G Richardson
Journal:  J Can Acad Child Adolesc Psychiatry       Date:  2021-05-01

Review 3.  Overdiagnosis across medical disciplines: a scoping review.

Authors:  Kevin Jenniskens; Joris A H de Groot; Johannes B Reitsma; Karel G M Moons; Lotty Hooft; Christiana A Naaktgeboren
Journal:  BMJ Open       Date:  2017-12-27       Impact factor: 2.692

4.  Breast cancer screening: Impact on care pathways.

Authors:  Delphine Lefeuvre; Nathalie Catajar; Christine Le Bihan Benjamin; Norbert Ifrah; Frédéric De Bels; Jérôme Viguier; Philippe Jean Bousquet
Journal:  Cancer Med       Date:  2019-06-06       Impact factor: 4.452

5.  Use of Mastectomy for Overdiagnosed Breast Cancer in the United States: Analysis of the SEER 9 Cancer Registries.

Authors:  C Harding; F Pompei; D Burmistrov; R Wilson
Journal:  J Cancer Epidemiol       Date:  2019-01-22

6.  Modeling the natural history of ductal carcinoma in situ based on population data.

Authors:  Sarocha Chootipongchaivat; Nicolien T van Ravesteyn; Xiaoxue Li; Hui Huang; Harald Weedon-Fekjær; Marc D Ryser; Donald L Weaver; Elizabeth S Burnside; Brandy M Heckman-Stoddard; Harry J de Koning; Sandra J Lee
Journal:  Breast Cancer Res       Date:  2020-05-27       Impact factor: 6.466

  6 in total

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